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Li, Mingjie, Zichi Wang, Haoxian Song, and Yong Liu. "Disguise of Steganography Behaviour: Steganography Using Image Processing with Generative Adversarial Network." Security and Communication Networks 2021 (December 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/2356284.
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The deep learning based image steganalysis is becoming a serious threat to modification-based image steganography in recent years. Generation-based steganography directly produces stego images with secret data and can resist the advanced steganalysis algorithms. This paper proposes a novel generation-based steganography method by disguising the stego images into the kinds of images processed by normal operations (e.g., histogram equalization and sharpening). Firstly, an image processing model is trained using DCGAN and WGAN-GP, which is used to generate the images processed by normal operations. Then, the noise mapped by secret data is inputted into the trained model, and the obtained stego image is indistinguishable from the processed image. In this way, the steganographic process can be covered by the process of image processing, leaving little embedding trace in the process of steganography. As a result, the security of steganography is guaranteed. Experimental results show that the proposed scheme has better security performance than the existing steganographic methods when checked by state-of-the-art steganalytic tools, and the superiority and applicability of the proposed work are shown.
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Vejare, Ritvij, Abhishek Vaish, Kapish Singh, and Mrunali Desai. "Removal of Image Steganography using Generative Adversarial Network." Indian Journal of Artificial Intelligence and Neural Networking 2, no. 4 (June 30, 2022): 6–10. http://dx.doi.org/10.54105/ijainn.d1054.062422.
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Secret messages can be concealed in ordinary media like audio, video and images. This is called as Steganography. Steganography is used by cyber attackers to send malicious content that could harm victims. Digital steganography, or steganography in photographs, is exceedingly difficult to detect. The detection of steganography in images, has been investigated in thoroughly by a variety of parties. The use of steganographic techniques to send more malware to a compromised host in order to undertake different post-exploitation operations that affect the exploited system. Many steganalysis algorithms, on the other hand, are limited to working with a subset of all potential photos in the wild or have a high false positive rate. As a result, barring any suspected image becomes an arbitrary policy. Filtering questionable photos before they are received by the host machine is a more practical policy. In this paper, a Generative Adversarial Network based model is proposed that may be optimized to delete steganographic content while maintaining the original image's perceptual quality. For removing steganography from photos while keeping the maximum visual image quality, a model is built utilizing a combination of Generative Adversarial Network (GAN) and Image Processing. In the future, utilizing a generator to synthesize a picture will become more popular, and detection of steganography in images will become very difficult. In comparison to other models that have been addressed further, the proposed model is able to give a mean square error of 5.4204 between the generated image and the cover image, as well as better outcomes based on several metrics. As a result, a GAN-based steganography eradication method will aid in this endeavor.
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Ding, Kangyi, Teng Hu, Weina Niu, Xiaolei Liu, Junpeng He, Mingyong Yin, and Xiaosong Zhang. "A Novel Steganography Method for Character-Level Text Image Based on Adversarial Attacks." Sensors 22, no. 17 (August 29, 2022): 6497. http://dx.doi.org/10.3390/s22176497.
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The Internet has become the main channel of information communication, which contains a large amount of secret information. Although network communication provides a convenient channel for human communication, there is also a risk of information leakage. Traditional image steganography algorithms use manually crafted steganographic algorithms or custom models for steganography, while our approach uses ordinary OCR models for information embedding and extraction. Even if our OCR models for steganography are intercepted, it is difficult to find their relevance to steganography. We propose a novel steganography method for character-level text images based on adversarial attacks. We exploit the complexity and uniqueness of neural network boundaries and use neural networks as a tool for information embedding and extraction. We use an adversarial attack to embed the steganographic information into the character region of the image. To avoid detection by other OCR models, we optimize the generation of the adversarial samples and use a verification model to filter the generated steganographic images, which, in turn, ensures that the embedded information can only be recognized by our local model. The decoupling experiments show that the strategies we adopt to weaken the transferability can reduce the possibility of other OCR models recognizing the embedded information while ensuring the success rate of information embedding. Meanwhile, the perturbations we add to embed the information are acceptable. Finally, we explored the impact of different parameters on the algorithm with the potential of our steganography algorithm through parameter selection experiments. We also verify the effectiveness of our validation model to select the best steganographic images. The experiments show that our algorithm can achieve a 100% information embedding rate and more than 95% steganography success rate under the set condition of 3 samples per group. In addition, our embedded information can be hardly detected by other OCR models.
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Manral, Nisha. "Secure Data Transfer Using Image Steganography." International Journal for Research in Applied Science and Engineering Technology 9, no. VIII (August 10, 2021): 175–80. http://dx.doi.org/10.22214/ijraset.2021.37322.
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Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exists a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Different applications have different requirements of the steganography technique used. For example, some applications may require absolute invisibility of the secret information, while others require a larger secret message to be hidden. This paper intends to give an overview of image steganography, its uses and techniques. It also attempts to identify the requirements of a good steganographic algorithm and briefly reflects on which steganographic techniques are more suitable for which applications.
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D, Shahi. "Reversible Steganography for RGB Images Using Image Interpolation." Journal of Advanced Research in Dynamical and Control Systems 12, no. 3 (March 20, 2020): 41–49. http://dx.doi.org/10.5373/jardcs/v12i3/20201165.
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Xu, Meng, Xiangyang Luo, Jinwei Wang, and Hao Wang. "Color image steganalysis based on quaternion discrete cosine transform." Electronic Research Archive 31, no. 7 (2023): 4102–18. http://dx.doi.org/10.3934/era.2023209.
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<abstract> <p>With the rapid development and application of Internet technology in recent years, the issue of information security has received more and more attention. Digital steganography is used as a means of secure communication to hide information by modifying the carrier. However, steganography can also be used for illegal acts, so it is of great significance to study steganalysis techniques. The steganalysis technology can be used to solve the illegal steganography problem of computer vision and engineering applications technology. Most of the images in the Internet are color images, and steganalysis for color images is a very critical problem in the field of steganalysis at this stage. Currently proposed algorithms for steganalysis of color images mainly rely on the manual design of steganographic features, and the steganographic features do not fully consider the internal connection between the three channels of color images. In recent years, advanced steganography techniques for color images have been proposed, which brings more serious challenges to color image steganalysis. Quaternions are a good tool to represent color images, and the transformation of quaternions can fully exploit the correlation among color image channels. In this paper, we propose a color image steganalysis algorithm based on quaternion discrete cosine transform, firstly, the image is represented by quaternion, then the quaternion discrete cosine transform is applied to it, and the coefficients obtained from the transformation are extracted to design features of the coeval matrix. The experimental results show that the proposed algorithm works better than the typical color image steganalysis algorithm.</p> </abstract>
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Zhang, Chunying, Xinkai Gao, Xiaoxiao Liu, Wei Hou, Guanghui Yang, Tao Xue, Liya Wang, and Lu Liu. "IDGAN: Information-Driven Generative Adversarial Network of Coverless Image Steganography." Electronics 12, no. 13 (June 29, 2023): 2881. http://dx.doi.org/10.3390/electronics12132881.
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Traditional image steganography techniques complete the steganography process by embedding secret information into cover images, but steganalysis tools can easily detect detectable pixel changes that lead to the leakage of confidential information. The use of a generative adversarial network (GAN) makes it possible to embed information using a combination of information and noise in generating images to achieve steganography. However, this approach is usually accompanied by issues such as poor image quality and low steganography capacity. To address these challenges, we propose a steganography model based on a novel information-driven generative adversarial network (IDGAN), which fuses a GAN, attention mechanisms, and image interpolation techniques. We introduced an attention mechanism on top of the original GAN model to improve image accuracy. In the generation model, we replaced some transposed convolution operations with image interpolation for better quality of dense images. In contrast to traditional steganographic methods, the IDGAN generates images containing confidential information without using cover images and utilizes GANs for information embedding, thus having better anti-detection capability. Moreover, the IDGAN uses an attention mechanism to improve the image details and clarity and optimizes the steganography effect through an image interpolation algorithm. Experimental results demonstrate that the IDGAN achieves an accuracy of 99.4%, 95.4%, 93.2%, and 100% on the MNIST, Intel Image Classification, Flowers, and Face datasets, respectively, with an embedding rate of 0.17 bpp. The model effectively protects confidential information while maintaining high image quality.
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Maurya, Indu, and S. K Gupta. "Understandable Steganography." International Journal of Engineering & Technology 7, no. 3 (June 23, 2018): 1024. http://dx.doi.org/10.14419/ijet.v7i3.8940.
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For hiding information in a digitalized object Steganography is an important technique. It is a special kind of scientific technique which involves the secret information communication inside suitable cover objects of multimedia like image files, audio or videos. The embedded data and its existence are hidden with the help of Steganography. It is a method of hiding data which has enormously improved the security level of confidential data with the help of special hiding mechanism and is considered as remarkable achievement in the computational power. The main aims of Steganography are; capacity of concealed data along with its robustness, lack of detection etc. These are some of the additional features which make it distinguishing from other older techniques like watermarking as well as cryptography. In this research paper, we have surveyed Steganography of digital images and cover the basic and key concepts. In spatial representation the development of image Steganographic methodology in the format of JPEG, along with that we will also debate on the modern developments as far as Steganography is concerned.For increasing Steganographic security, specifically used approaches are shortlisted and the developments made after investigations are also presented in this paper.
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Damanik, Hillman Akhyar, Merry Anggraeni, and Tomi Defisa. "ANALYSIS STEGO-IMAGE EXTRACTION USING ROT13 AND LEAST SIGNIFICANT BIT (LSB) ALGORITHM METHOD ON TEXT SECURITY." Jurnal Ilmiah FIFO 9, no. 2 (November 1, 2017): 147. http://dx.doi.org/10.22441/fifo.2017.v9i2.008.
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Cryptography is both a science and an art to keep the message confidential. While steganography is the science and art of hiding secret messages in other messages so that the existence of such secret messages is unknowable and generally serves to disguise the existence of confidential data making it difficult to detect and protect the copyright of a product. Steganography requires two properties, namely container media and secret messages. The application of steganographic and cryptographic combination is done by Least Significant Bit (LSB) and ROT13 algorithm. Steganography with the LSB method is one of the methods used to hide messages on digital media by inserting it to the lowest bits or the most right bits of the pixel data that compile the file. In this research, the authors propose the technique of securing Steganography secret messages with layered security, by adding Cryptography to secret messages that will be inserted into digital images and then messages inserted into digital images through Steganography using LSB method.
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Madhi, Hadi Hussein, Mustafa Sahib Shareef, Seham Ahmed Hashem, and Abdallah Waleed Ali. "Pixel steganography method for grayscale image steganography on colour images." Periodicals of Engineering and Natural Sciences (PEN) 9, no. 3 (August 11, 2021): 615. http://dx.doi.org/10.21533/pen.v9i3.2203.
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Khairunnisak, Khairunnisak, Gilang Miftakhul Fahmi, and Didit Suhartono. "Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network." SINTECH (Science and Information Technology) Journal 6, no. 1 (April 30, 2023): 47–57. http://dx.doi.org/10.31598/sintechjournal.v6i1.1258.
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Abstract
In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium. One of the media for hiding messages is pictures. However, steganography techniques can still be detected by steganalysis techniques. Steganalysis is a technique for analyzing hidden messages in steganography. Therefore this study applies image processing techniques with the Generative Adversarial Network algorithm model, which aims to manipulate images so that steganalysis techniques cannot detect hidden messages. Proof of the results of applying the Generative Adversarial Network algorithm using a web-based application containing message hiding and extraction functions. The results obtained are that the Generative Adversarial Network algorithm can be applied to create mock objects, and images can revive based on training data which is a model for how the algorithm works. In addition, the results of testing the Generative Adversarial Network algorithm were successfully applied to image steganography which functions to prevent steganalysis techniques from trying to detect messages in images. Future research is expected to be able to select steganographic images other than the results from the training data model according to the original size chosen randomly according to the selection of the user.
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Sailaja, B., K. Meghana, Damayanthi S. Harika, S. Sandhya, and Sushma V. Satya. "Secure multimedia data transfer using crypto and stegano algorithms." i-manager’s Journal on Software Engineering 16, no. 4 (2022): 9. http://dx.doi.org/10.26634/jse.16.4.18820.
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Recent developments in Steganalysis have made it difficult to secure personal content, messages, or digital images using steganography. Using Steganalysis can easily reveal the presence of hidden information in storage files. This paper presents a new Steganographic approach to communication between two individuals. The approach presented in this paper uses both Steganographic and cryptographic methods. Cryptography uses Rivest–Shamir–Adleman (RSA) and Steganography use image Steganography to hide data. It also uses the mutual authentication process to satisfy all services in the field of cryptography, i.e., access control, confidentiality, integrity, and authentication. In this way, it can store data more securely. Since it uses the RSA algorithm to protect the data again, it performs Steganography to hide the data in the image. Thus, any other person on the network cannot access the data present on the network. Only the sender and receiver can extract the message from the data.
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Khaldi, Amine. "Steganographic Techniques Classification According to Image Format." International Annals of Science 8, no. 1 (November 4, 2019): 143–49. http://dx.doi.org/10.21467/ias.8.1.143-149.
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In this work, we present a classification of steganographic methods applicable to digital images. We also propose a classification of steganographic methods according to the type of image used. We noticed there are no methods that can be applied to all image formats. Each type of image has its characteristics and each steganographic method operates on a precise colorimetric representation. This classification provides an overview of the techniques used for the steganography of digital images
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Ouyang, Chun-Juan, Ming Leng, Jie-Wu Xia, and Huan Liu. "Vague Sets Security Measure for Steganographic System Based on High-Order Markov Model." Security and Communication Networks 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/1790268.
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Security measure is of great importance in both steganography and steganalysis. Considering that statistical feature perturbations caused by steganography in an image are always nondeterministic and that an image is considered nonstationary, in this paper, the steganography is regarded as a fuzzy process. Here a steganographic security measure is proposed. This security measure evaluates the similarity between two vague sets of cover images and stego images in terms of n-order Markov chain to capture the interpixel correlation. The new security measure has proven to have the properties of boundedness, commutativity, and unity. Furthermore, the security measures of zero order, first order, second order, third order, and so forth are obtained by adjusting the order value of n-order Markov chain. Experimental results indicate that the larger n is, the better the measuring ability of the proposed security measure will be. The proposed security measure is more sensitive than other security measures defined under a deterministic distribution model, when the embedding is low. It is expected to provide a helpful guidance for designing secure steganographic algorithms or reliable steganalytic methods.
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Khandait, P. D., S. P. Khandait, K. K. Ingole, and Manjusha Talmale. "Component analysis of matrix pattern on RGB images for image stegano-key in MATLAB." Open Journal of Science and Technology 4, no. 2 (September 6, 2021): 70–76. http://dx.doi.org/10.31580/ojst.v4i2.1704.
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Steganography is quite possibly the best procedures to shroud the presence of concealed data inside a cowl thing. Pictures are the notable cowl things for Steganography and in this work, picture Steganography is embraced. Implanting secret information based interior pictures calls for concentrated calculation, and thus equipment based Steganography usage increases fundamental interest of Steganography based calculation. There are a few techniques to shroud inside recorded cover-photographs. The spatial area strategies control the duvet-photograph pixel-bit esteems to insert the mystery data. The mystery pieces are composed quickly to the duvet picture pixel bytes. Thusly, the spatial territory procedures are basic and simple to execute. The Least Significant Bit (LSB) based picture steganography is one of the significant systems in spatial area photograph Steganography. In this work, a shiny new strategy for LSB Steganography has been suggested that is an extemporized model of 1 bit LSB technique.
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SALLEE, PHIL. "MODEL-BASED METHODS FOR STEGANOGRAPHY AND STEGANALYSIS." International Journal of Image and Graphics 05, no. 01 (January 2005): 167–89. http://dx.doi.org/10.1142/s0219467805001719.
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This paper presents methods for performing steganography and steganalysis using a statistical model of the cover medium. The methodology is general, and can be applied to virtually any type of media. It provides answers for some fundamental questions that have not been fully addressed by previous steganographic methods, such as how large a message can be hidden without risking detection by certain statistical methods, and how to achieve this maximum capacity. Current steganographic methods have been shown to be insecure against simple statistical attacks. Using the model-based methodology, an example steganography method is proposed for JPEG images that achieves a higher embedding efficiency and message capacity than previous methods while remaining secure against first order statistical attacks. A method is also described for defending against "blockiness" steganalysis attacks. Finally, a model-based steganalysis method is presented for estimating the length of messages hidden with Jsteg in JPEG images.
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Farhad Khorshid, Shler. "Coverless Image Steganography: Review." Academic Journal of Nawroz University 11, no. 3 (August 26, 2022): 314–26. http://dx.doi.org/10.25007/ajnu.v11n3a1460.
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Many of the existing image steganographic techniques embed secret information into cover images by slightly altering their contents. These modifications have several effects, on the other hand, Stego-images distorted by these problems become vulnerable to steganalysis tools. Coverless information hiding represents a solution to this problem. Unlike traditional techniques, which alter the carrier, a coverless information hiding procedure does not change it. This paper reviews some of the recent works that have been conducted on the topic of coverless image steganography and provides an important insight into how these techniques are performed.
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Progonov, Dmytro, and Mariia Yarysh. "Analyzing the accuracy of detecting steganograms formed by adaptive steganographic methods when using artificial neural networks." Eastern-European Journal of Enterprise Technologies 1, no. 9(115) (February 28, 2022): 45–55. http://dx.doi.org/10.15587/1729-4061.2022.251350.
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This paper reports a comparative analysis of accuracy in the detection of steganograms formed according to adaptive steganographic methods, using steganography detectors based on common and specialized types of artificial neural networks. The results of the review of modern convolutional neural networks applied for the tasks of digital image stegoanalysis have established that the accuracy of operating the steganography detectors based on these networks is significantly compromised when processing image packets characterized by a significant variability of statistical parameters. The performance accuracy of steganography detectors based on the modern statistical model of container images maxSRMd2 has been investigated, as well as on the latest convolutional and «hybrid» artificial neural networks, in particular, GB-Ras and ASSAF networks, when detecting steganograms formed according to the adaptive steganographic methods HUGO and MiPOD. It was established that the use of the statistical model maxSRMd2 makes it possible to significantly (up to 30 %) improve the accuracy of steganogram detection in the case of analyzing those images that are characterized by a high level of natural noise. It was found that the use of the ASSAF network makes it possible to significantly (up to 35 %) reduce an error of steganogram detection compared to current steganography detectors based on the GB-Ras network and the maxSRMd2 statistical model. It was determined that the high accuracy of the ASSAF network-based steganography detector is maintained even in the most difficult case of image processing with high noise and poor filling of the container image with stegodata (less than 10 %). The results reported here are of theoretical interest for designing high-precision steganography detectors capable of working under conditions of high variability in image parameters.
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Iglesias, Patricia, Miguel-Angel Sicilia, and Elena García-Barriocanal. "Detecting Browser Drive-By Exploits in Images Using Deep Learning." Electronics 12, no. 3 (January 17, 2023): 473. http://dx.doi.org/10.3390/electronics12030473.
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Steganography is the set of techniques aiming to hide information in messages as images. Recently, stenographic techniques have been combined with polyglot attacks to deliver exploits in Web browsers. Machine learning approaches have been proposed in previous works as a solution for detecting stenography in images, but the specifics of hiding exploit code have not been systematically addressed to date. This paper proposes the use of deep learning methods for such detection, accounting for the specifics of the situation in which the images and the malicious content are delivered using Spatial and Frequency Domain Steganography algorithms. The methods were evaluated by using benchmark image databases with collections of JavaScript exploits, for different density levels and steganographic techniques in images. A convolutional neural network was built to classify the infected images with a validation accuracy around 98.61% and a validation AUC score of 99.75%.
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Prasad, Swaroop Shankar, Ofer Hadar, and Ilia Polian. "Detection of Malicious Spatial-Domain Steganography over Noisy Channels Using Convolutional Neural Networks." Electronic Imaging 2020, no. 4 (January 26, 2020): 76–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.4.mwsf-076.
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Image steganography can have legitimate uses, for example, augmenting an image with a watermark for copyright reasons, but can also be utilized for malicious purposes. We investigate the detection of malicious steganography using neural networkbased classification when images are transmitted through a noisy channel. Noise makes detection harder because the classifier must not only detect perturbations in the image but also decide whether they are due to the malicious steganographic modifications or due to natural noise. Our results show that reliable detection is possible even for state-of-the-art steganographic algorithms that insert stego bits not affecting an image’s visual quality. The detection accuracy is high (above 85%) if the payload, or the amount of the steganographic content in an image, exceeds a certain threshold. At the same time, noise critically affects the steganographic information being transmitted, both through desynchronization (destruction of information which bits of the image contain steganographic information) and by flipping these bits themselves. This will force the adversary to use a redundant encoding with a substantial number of error-correction bits for reliable transmission, making detection feasible even for small payloads.
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Zhu, Xishun, Zhengliang Lai, Nanrun Zhou, and Jianhua Wu. "Steganography with High Reconstruction Robustness: Hiding of Encrypted Secret Images." Mathematics 10, no. 16 (August 15, 2022): 2934. http://dx.doi.org/10.3390/math10162934.
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As one of the important methods to protect information security, steganography can ensure the security of data in the process of information transmission, which has attracted much attention in the information security community. However, many current steganography algorithms are not sufficiently resistant to recent steganalysis algorithms, such as deep learning-based steganalysis algorithms. In this manuscript, a new steganography algorithm, based on residual networks and pixel shuffle, is proposed, which combines image encryption and image hiding, named Resen-Hi-Net, an algorithm that first encrypts a secret image and then hides it in a carrier image to produce a meaningful container image. The proposed Resen-Hi-Net has the advantages of both image encryption and image hiding. The experimental results showed that the proposed Resen-Hi-Net could realize both image encryption and image hiding; the visual container image quality was as high as 40.19 dB on average in PSNR to reduce the possibility of being attacked, and the reconstructed secret image quality was also good enough (34.39 dB on average in PSNR). In addition, the proposed Resen-Hi-Net has a strong ability to resist destructive attacks and various steganographic analyses.
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Viddin, Irsandy Maulana Satya, Antonius Cahya Prihandoko, and Diksy Media Firmansyah. "An authentication alternative using histogram shifting steganography method." Jurnal Teknologi dan Sistem Komputer 9, no. 2 (February 26, 2021): 106–12. http://dx.doi.org/10.14710/jtsiskom.2021.13931.
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This study aims to develop an authentication alternative by applying the Histogram shifting steganography method. The media used for authentication is image media. Histogram shifting utilizes the histogram of an image to insert a secret message. The developed authentication has implemented the Histogram shifting to insert user credentials into the carrier image. Users can use the steganographic image to log into their accounts. The method extracts the credentials from the image during the login. PSNR test of the steganographic images produces an average value of 52.52 dB. The extraction capability test shows that the method can extract all test images correctly. In addition, this authentication method is also more resistant to attacks common to password authentication.
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Agrawal, Rohit, Kapil Ahuja, Marc C. Steinbach, and Thomas Wick. "SABMIS: sparse approximation based blind multi-image steganography scheme." PeerJ Computer Science 8 (November 28, 2022): e1080. http://dx.doi.org/10.7717/peerj-cs.1080.
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We hide grayscale secret images into a grayscale cover image, which is considered to be a challenging steganography problem. Our goal is to develop a steganography scheme with enhanced embedding capacity while preserving the visual quality of the stego-image as well as the extracted secret image, and ensuring that the stego-image is resistant to steganographic attacks. The novel embedding rule of our scheme helps to hide secret image sparse coefficients into the oversampled cover image sparse coefficients in a staggered manner. The stego-image is constructed by using the Alternating Direction Method of Multipliers (ADMM) to solve the Least Absolute Shrinkage and Selection Operator (LASSO) formulation of the underlying minimization problem. Finally, the secret images are extracted from the constructed stego-image using the reverse of our embedding rule. Using these components together, to achieve the above mentioned competing goals, forms our most novel contribution. We term our scheme SABMIS (Sparse Approximation Blind Multi-Image Steganography). We perform extensive experiments on several standard images. By choosing the size of the length and the width of the secret images to be half of the length and the width of cover image, respectively, we obtain embedding capacities of 2 bpp (bits per pixel), 4 bpp, 6 bpp, and 8 bpp while embedding one, two, three, and four secret images, respectively. Our focus is on hiding multiple secret images. For the case of hiding two and three secret images, our embedding capacities are higher than all the embedding capacities obtained in the literature until now (3 times and 6 times than the existing best, respectively). For the case of hiding four secret images, although our capacity is slightly lower than one work (about 2/3rd), we do better on the other two goals (quality of stego-image & extracted secret image as well as resistance to steganographic attacks). For our experiments, there is very little deterioration in the quality of the stego-images as compared to their corresponding cover images. Like all other competing works, this is supported visually as well as over 30 dB of Peak Signal-to-Noise Ratio (PSNR) values. The good quality of the stego-images is further validated by multiple numerical measures. None of the existing works perform this exhaustive validation. When using SABMIS, the quality of the extracted secret images is almost same as that of the corresponding original secret images. This aspect is also not demonstrated in all competing literature. SABMIS further improves the security of the inherently steganographic attack resistant transform based schemes. Thus, it is one of the most secure schemes among the existing ones. Additionally, we demonstrate that SABMIS executes in few minutes, and show its application on the real-life problems of securely transmitting medical images over the internet.
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Rehman, Amjad, Tanzila Saba, Toqeer Mahmood, Zahid Mehmood, Mohsin Shah, and Adeel Anjum. "Data hiding technique in steganography for information security using number theory." Journal of Information Science 45, no. 6 (December 6, 2018): 767–78. http://dx.doi.org/10.1177/0165551518816303.
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In the current era, due to the widespread availability of the Internet, it is extremely easy for people to communicate and share multimedia contents with each other. However, at the same time, secure transfer of personal and copyrighted material has become a critical issue. Consequently, secure means of data transfer are the most urgent need of the time. Steganography is the science and art of protecting the secret data from an unauthorised access. The steganographic approaches conceal secret data into a cover file of type audio, video, text and/or image. The actual challenge in steganography is to achieve high robustness and capacity without bargaining on the imperceptibility of the cover file. In this article, an efficient steganography method is proposed for the transfer of secret data in digital images using number theory. For this purpose, the proposed method represents the cover image using the Fibonacci sequence. The representation of an image in the Fibonacci sequence allows increasing the bit planes from 8-bit to 12-bit planes. The experimental results of the proposed method in comparison with other existing steganographic methods exhibit that our method not only achieves high embedding of secret data but also gives high quality of stego images in terms of peak signal-to-noise ratio (PSNR). Furthermore, the robustness of the technique is also evaluated in the presence of salt and pepper noise attack on the cover images.
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Zhong, Nan, Zhenxing Qian, Zichi Wang, and Xinpeng Zhang. "Steganography in stylized images." Journal of Electronic Imaging 28, no. 03 (May 14, 2019): 1. http://dx.doi.org/10.1117/1.jei.28.3.033005.
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Fadlil, Affan, Budi Prasetiyo, and Alamsyah Alamsyah. "Increasing Message Capacity in Images Using Advanced Least Significant Bit and Image Scaling." Scientific Journal of Informatics 8, no. 2 (November 30, 2021): 268–75. http://dx.doi.org/10.15294/sji.v8i2.28138.
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Purpose: Steganography is the science of writing hidden or hiding messages so that apart from the sender and the recipient, no one can know or realize that a message is hidden. This paper aims to analyze the method of advanced LSB to increase message capacity. Methods/Study design/approach: The steganography technique advanced LSB algorithm develops pre-existing steganographic algorithms such as LSB by utilizing a range of media pixel values cover (images that are used as media to hide messages) with different insertion rules from LSB. Image scaling in digital image processing is known as resampling. Resampling is a mathematical technique used to produce a new image from the previous image with different pixel size, often called interpolation. Increasing the pixel size of the previous image is called upsampling and in this study we will only use twice the image magnification. Result/Findings: The results of each test method using advanced LSB without image scaling and advanced LSB using image scaling were compared to obtain detailed comparison results of each method. Novelty/Originality/Value: Advanced LSB and image scaling in this study can increase the message capacity three times compared to only using the advanced LSB method without image scaling. It depends on the image pixels used.
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Bin Sulong, Ghazali, and Maria A.Wimmer. "Image hiding by using spatial domain steganography." Wasit Journal of Computer and Mathematics Science 2, no. 1 (March 31, 2023): 39–45. http://dx.doi.org/10.31185/wjcm.110.
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This article provides an overview of steganography and its use for hiding images in other images. Steganography is a technique that allows users to hide information in plain sight, making it difficult for unauthorized parties to detect or access the information. Spatial domain steganography is a popular technique for hiding images within other images, where the least significant bits of the cover image are modified to embed the secret image. The article discusses the advantages of steganography and its use in various applications such as digital watermarking and secure communication. The article also provides an overview of the various techniques used for spatial domain steganography, and how these techniques can be implemented using programming languages such as Python. Finally, the article concludes by emphasizing the importance of using steganography responsibly and ethically.
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Zhu, Jie, Xianfeng Zhao, and Qingxiao Guan. "Detecting and Distinguishing Adaptive and Non-Adaptive Steganography by Image Segmentation." International Journal of Digital Crime and Forensics 11, no. 1 (January 2019): 62–77. http://dx.doi.org/10.4018/ijdcf.2019010105.
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This article describes how blind steganalysis aiming at uncovering the existence of hidden data in digital images remains an open problem. Conventional spatial image steganographic algorithms hide data into pixels spreading evenly in the entire cover image, while the content-adaptive algorithms prefer the textural areas and edge regions. In this article, the impact of image content on blind steganalysis is discussed and a practical and extensible approach to distinguish the different types of steganography and construct blind steganalytic detector is proposed. Through the technique of image segmentation, the images are segmented into sub-images with different levels of texture. The classifier only cares for the sub-images which can help modeling the statistical detectability and is trained on sub-images instead of the entire image. Experimental results show the authors' scheme can recognize the type of steganographic methods reliably. The further steps to improve capacity of blind steganalysis based on image segmentation are also mentioned and achieve better performance than ordinary blind steganalysis.
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Chang, Ching-Chun. "Neural Reversible Steganography with Long Short-Term Memory." Security and Communication Networks 2021 (April 4, 2021): 1–14. http://dx.doi.org/10.1155/2021/5580272.
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Deep learning has brought about a phenomenal paradigm shift in digital steganography. However, there is as yet no consensus on the use of deep neural networks in reversible steganography, a class of steganographic methods that permits the distortion caused by message embedding to be removed. The underdevelopment of the field of reversible steganography with deep learning can be attributed to the perception that perfect reversal of steganographic distortion seems scarcely achievable, due to the lack of transparency and interpretability of neural networks. Rather than employing neural networks in the coding module of a reversible steganographic scheme, we instead apply them to an analytics module that exploits data redundancy to maximise steganographic capacity. State-of-the-art reversible steganographic schemes for digital images are based primarily on a histogram-shifting method in which the analytics module is often modelled as a pixel intensity predictor. In this paper, we propose to refine the prior estimation from a conventional linear predictor through a neural network model. The refinement can be to some extent viewed as a low-level vision task (e.g., noise reduction and super-resolution imaging). In this way, we explore a leading-edge neuroscience-inspired low-level vision model based on long short-term memory with a brief discussion of its biological plausibility. Experimental results demonstrated a significant boost contributed by the neural network model in terms of prediction accuracy and steganographic rate-distortion performance.
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Yalla, Surya Prakash, Archana Uriti, and Abhisek Sethy. "GUI Implementation of Modified and Secure Image Steganography Using Least Significant Bit Substitution." International Journal of Safety and Security Engineering 12, no. 5 (November 30, 2022): 639–43. http://dx.doi.org/10.18280/ijsse.120513.
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Due to swift improvement of information innovation in recent times, providing security to data has become major concern and threat to Information Privacy has become inevitable. Data Hiding technology is an efficient way to solve the problems of data leakage and loss of information. Data hiding called steganography is a security method to provide security to secret data which is transferred from sender to receiver from harmful attacks. Steganography is an interaction of concealing a mysterious message inside a cover object which is not secret. There are many cover media like images, audio, video, text files etc. There are many ways to approach steganography like spatial domain, transformation domain, masking and filtering. This technique is helpful because the human eye is quite insensitive to the minute changes that help the embedded data stay safe and secure. The main motive of steganography is to get high stego image quality, low computational complexity, more embedding capability, visually unnoticeable, invisibility, and improved security. A capable steganographic technique must be resistant to any steganalysis approach the secret data is prone to. In this proposed system, implement the GUI implementation image steganography in spatial domain using Least Significant Bit (LSB) where the modified high capacity cover image undergoes the Discrete Wavelet Transformation (DWT) and propose an Advanced Encryption Standard (AES) secret key stego system such that the data is secure. The distortion between the two images in identified with the help of MSE and Histogram analysis.
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Radhe, Shyam Panda, Gupta Deepika, Jaiswal Madhuri, Kasar Vaishnavi, and A. L. Prasanna. "Image steganography approach using spatial and transform domain technique." i-manager's Journal on Computer Science 10, no. 1 (2022): 21. http://dx.doi.org/10.26634/jcom.10.1.18504.
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Cryptography is often used to keep information confidential by making it illegible. However, incomprehensible information may raise suspicions of the adversary and may lead him to sabotage this style of communication. Therefore, steganography has a place in the arena of information security. Steganography refers to the technique of hiding information in digital media to hide the existence of information. Data caching is the art of hiding data for colorful purposes similar as; to maintain private data, secure non-public data and so on. Securely exchange the data over the internet network is veritably important issue. So, to transfer the data securely to the destination, there are numerous approaches like cryptography and steganography. In this design we propose a LSB & DCT grounded steganographic system for hiding the data by applying Least Significant Bit (LSB) algorithm for bedding the data into the images which is enforced through the HTML, CSS, JavaScript.
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Sundos A. Hameed Al-azawi and Abbas A. AbulHameed. "Information Hiding in Color Image Using Steganographic technique." Journal of the College of Basic Education 17, no. 70 (December 22, 2022): 113–22. http://dx.doi.org/10.35950/cbej.vi.8480.
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Steganography is one of the important research subjects in the field of information security. It enables secret communication by embedding messages in the texts, images, audio, video files or other digital carriers. Among all the image information hiding methods, LSB embedding is widely used for its high hiding capacity and it is with great significance to detect the images with hidden messages produced by LSB embedding effectively, accurately and reliably. Therefore, many experts made efforts on the LSB steganography and steganalysis research over the years. This research presents a steganographic technique based on using LSB of one of the pixel color components in the image and changes them according to the message’s bits to hide. The rest of bits in the pixel color component selected are also changed in order get the nearest color to the original one in the scale of colors. This new method has been tested with others that work in the spatial domain through applying some common metrics which give us good result as a compared with the other steganographic tools.
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Kadam, Gayatri Ulhas, Purva Ignathi Jadhav, Trupti Shahaji Chandanshive, Kajal Vilas Shinde, and Mrs Ashwini Bamanikar. "Multi Images Steganography using Neural Network." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 406–10. http://dx.doi.org/10.22214/ijraset.2022.41242.
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Abstract: Currently, the most successful approach to steganography in empirical objects, such as digital media, is to embed the payload while minimizing a suitably defined distortion function. The design of the distortion is essentially the only task left to the stegnographer since efficient practical codes exist that embed near the payload-distortion bound. The ractitioner goal is to design the distortion to obtain a scheme with a high empirical statistical detectability. In this paper, we propose a universal distortion design called universal wavelet relative distortion (UNIWARD) that can be applied for embedding in an arbitrary domain. The embedding distortion is computed as a sum of relative changes of coefficients in a directional filter bank decomposition of the cover image. The directionality forces the embedding changes to such parts of the cover object that are difficult to model in multiple directions, such as textures or noisy regions, while avoiding smooth regions or clean edges. We demonstrate experimentally using rich models as well as targeted attacks that steganographic methods built using UNIWARD match or outperform the current state of the art in the spatial domain, JPEG domain, and sideinformed JPEG domain.
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Melman, A. S., P. O. Petrov, A. A. Shelupanov, A. V. Aristov, and Y. P. Pokholkov. "Embedding information into JPEG images with distortion masking in frequency domain." Proceedings of Tomsk State University of Control Systems and Radioelectronics 23, no. 4 (December 25, 2020): 45–50. http://dx.doi.org/10.21293/1818-0442-2020-23-4-45-50.
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Steganography allows to ensure the confidentiality of information by organizing covert data transmission channels. However, the effectiveness of steganographic information protection directly depends on the invisibility of a secret message, both for the human eye and for steganalysis methods. The paper proposes an approach that allows solving the problem of vulnerability of the popular QIM embedding method to statistical steganalysis. For this, it is proposed to use a variable quantization step, which is adaptively selected for each block of the JPEG cover image. The experimental results demonstrate an increase in the security level of steganographic embedding due to the application of the proposed approach.
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Koshkina, N. "About JPEG Images Parameters Impact to Steganalys Accuracy." Cybernetics and Computer Technologies, no. 1 (March 30, 2021): 74–85. http://dx.doi.org/10.34229/2707-451x.21.1.8.
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Introduction. Existing examples of illegal use of computer steganography prove the need for the development of stegananalytical methods and systems as one of the most important areas of cybersecurity. The advantage of machine learning-based stegananalytical methods is their versatility: they do not rely on knowledge of the injection algorithm and can be used to detect a wide range of steganographic methods. However, before being used for detecting steganocontainers, the methods mentioned require training on containers that are determined for sure whether they contain hidden messages or not. On this stage, it is very important to understand how the parameters of containers under investigation, in particular, such a common variant as JPEG images, affect the accuracy of steganalysis. After all, the inconsistency of the source of containers is an open problem of steganalysis leading to significant decrease of accuracy of detecting hidden messages after the classifier is moved from the laboratory to the real world. The purpose of the work is investigation of influence of the content, size and quality factor of JPEG images to the accuracy of their steganalysis performed by statistical methods based on machine learning. Results. During the research the following patterns were revealed: 1) the accuracy is better when images with a close percentage of coefficients suitable for DCT concealment are used for training and control, 2) images are classified more accurately when they have a relatively small number of suitable DCT coefficients, 3) with using mixed training samples (by content or parameters) the accuracy of steganalysis deteriorates, 4) decreasing quality factor of JPEG-images leads to increasing the accuracy of their steganalysis, 5) increasing size of images increases the accuracy of their steganalysis, 6) images where desynchronization of blocks took place during preprocessing are classified more accurately, 7) the sequence of the image preprocessing operations affects the accuracy of its steganoanalysis. Conclusions. For steganography tasks – the choice of JPEG containers, taking into account revealed patterns, makes steganographic hides more resistant to passive attacks. Considering them for tasks of steganalysis allows one to interpret the obtained results more accurately. Keywords: information security, steganography, stegananalysis, intelligent computer systems, machine learning, detection accuracy.
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Modupe, Alade Oluwaseun, Amusan Elizabeth Adedoyin, and Adedeji Oluyinka Titilayo. "A Comparative Analysis of LSB, MSB and PVD Based Image Steganography." International Journal of Research and Review 8, no. 9 (September 23, 2021): 373–77. http://dx.doi.org/10.52403/ijrr.20210948.
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Steganography is the art and science of hiding information by embedding data into cover media. Numerous techniques are designed to provide the security for the communication of data over the Internet. A good steganographic algorithm is recognized by the performance of the techniques measured with the support of the performance metrics among which are PSNR, MSE, SSIM, robustness and capacity to hide the information in the cover image. In this paper a comparative analysis of Least Significant Bit (LSB), Most Significant Bit (MSB) and Pixel Value Differencing (PVD) image steganography in grayscale and colored images was performed. Three different cover images was used to hide secret message. A comparative performance analysis of LSB, MSB and PVD methods used in image steganography was performed using peak signal to noise ratio (PSNR), Mean square error (MSE) and Structural Similarity index (SSIM) as performance metrics. LSB technique gives higher PSNR and SSIM values than MSB and PVD method with lower MSE than the other two techniques. Future research can be geared towards investigating the embedding capacity, security, and computational complexity of each technique. Keywords: Least Significant Bit (LSB), Most Significant Bit (MSB), Pixel value differencing (PVD), PSNR, SSIM and MSE,
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Jaradat, Aya, Eyad Taqieddin, and Moad Mowafi. "A High-Capacity Image Steganography Method Using Chaotic Particle Swarm Optimization." Security and Communication Networks 2021 (June 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/6679284.
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Image steganography has been widely adopted to protect confidential data. Researchers have been seeking to improve the steganographic techniques in order to increase the embedding capacity while preserving the stego-image quality. In this paper, we propose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations in the cover image to hide the secret data while maintaining the quality of the resultant stego-image. To enhance the embedding capacity, the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits. Experimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM image quality metrics.
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Duan, Xintao, Nao Liu, Mengxiao Gou, Wenxin Wang, and Chuan Qin. "SteganoCNN: Image Steganography with Generalization Ability Based on Convolutional Neural Network." Entropy 22, no. 10 (October 8, 2020): 1140. http://dx.doi.org/10.3390/e22101140.
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Image-to-image steganography is hiding one image in another image. However, hiding two secret images into one carrier image is a challenge today. The application of image steganography based on deep learning in real-life is relatively rare. In this paper, a new Steganography Convolution Neural Network (SteganoCNN) model is proposed, which solves the problem of two images embedded in a carrier image and can effectively reconstruct two secret images. SteganoCNN has two modules, an encoding network, and a decoding network, whereas the decoding network includes two extraction networks. First, the entire network is trained end-to-end, the encoding network automatically embeds the secret image into the carrier image, and the decoding network is used to reconstruct two different secret images. The experimental results show that the proposed steganography scheme has a maximum image payload capacity of 47.92 bits per pixel, and at the same time, it can effectively avoid the detection of steganalysis tools while keeping the stego-image undistorted. Meanwhile, StegaoCNN has good generalization capabilities and can realize the steganography of different data types, such as remote sensing images and aerial images.
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Bi, Xinliang, Xiaoyuan Yang, Chao Wang, and Jia Liu. "High-Capacity Image Steganography Algorithm Based on Image Style Transfer." Security and Communication Networks 2021 (September 28, 2021): 1–14. http://dx.doi.org/10.1155/2021/4179340.
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Steganography is a technique for publicly transmitting secret information through a cover. Most of the existing steganography algorithms are based on modifying the cover image, generating a stego image that is very similar to the cover image but has different pixel values, or establishing a mapping relationship between the stego image and the secret message. Attackers will discover the existence of secret communications from these modifications or differences. In order to solve this problem, we propose a steganography algorithm ISTNet based on image style transfer, which can convert a cover image into another stego image with a completely different style. We have improved the decoder so that the secret image features can be fused with style features in a variety of sizes to improve the accuracy of secret image extraction. The algorithm has the functions of image steganography and image style transfer at the same time, and the images it generates are both stego images and stylized images. Attackers will pay more attention to the style transfer side of the algorithm, but it is difficult to find the steganography side. Experiments show that our algorithm effectively increases the steganography capacity from 0.06 bpp to 8 bpp, and the generated stylized images are not significantly different from the stylized images on the Internet.
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Abbas, Prof Dr Tawfiq Abdulkhaleq. "Steganography Using Fractal Images Technique." IOSR Journal of Engineering 4, no. 2 (February 2014): 52–61. http://dx.doi.org/10.9790/3021-04225261.
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Farn, En-Jung. "Jigsaw puzzle images for steganography." Optical Engineering 48, no. 7 (July 1, 2009): 077006. http://dx.doi.org/10.1117/1.3159872.
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Po-Chyi Su and C. C. J. Kuo. "Steganography in JPEG2000 compressed images." IEEE Transactions on Consumer Electronics 49, no. 4 (November 2003): 824–32. http://dx.doi.org/10.1109/tce.2003.1261161.
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Luo, X., F. Liu, C. Yang, S. Lian, and D. Wang. "On F5 Steganography in Images." Computer Journal 55, no. 4 (September 12, 2011): 447–56. http://dx.doi.org/10.1093/comjnl/bxr092.
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Mohammed Zaki, Hassan. "Color Pattern Steganography in Images." Technium: Romanian Journal of Applied Sciences and Technology 9 (April 21, 2023): 60–65. http://dx.doi.org/10.47577/technium.v9i.8337.
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Text steganography into images is one of the important strategies that are used to hide the transmitted secret messages via internet connections. Most of the proposed methods required mapping key to recover the hidden secret messages and implement more complex algorithms in hiding and recovering processes. The paper proposed an improved method to hide text in image cove based on the color pattern of the cover image. It based on the sequence of the new occurrence of the color. The results showed that the proposed method has high performance in hiding with extremely low PSNR(<0.002). The low differences between the original image and the stegno image provide more secured hiding that satisfied the State-of-Art. The proposed method can be used in transmitting classified and confidential messages via public internet connections.
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Pan, Ping, Zeming Wu, Chen Yang, and Bing Zhao. "Double-Matrix Decomposition Image Steganography Scheme Based on Wavelet Transform with Multi-Region Coverage." Entropy 24, no. 2 (February 7, 2022): 246. http://dx.doi.org/10.3390/e24020246.
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On the basis of ensuring the quality and concealment of steganographic images, this paper proposes a double-matrix decomposition image steganography scheme with multi-region coverage, to solve the problem of poor extraction ability of steganographic images under attack or interference. First of all, the cover image is transformed by multi-wavelet transform, and the hidden region covering multiple wavelet sub-bands is selected in the wavelet domain of the cover image to embed the secret information. After determining the hidden region, the hidden region is processed by Arnold transform, Hessenberg decomposition, and singular-value decomposition. Finally, the secret information is embedded into the cover image by embedding intensity factor. In order to ensure robustness, the hidden region selected in the wavelet domain is used as the input of Hessenberg matrix decomposition, and the robustness of the algorithm is further enhanced by Hessenberg matrix decomposition and singular-value decomposition. Experimental results show that the proposed method has excellent performance in concealment and quality of extracted secret images, and secret information is extracted from steganographic images attacked by various image processing attacks, which proves that the proposed method has good anti-attack ability under different attacks.
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Wu, Han-Yan, Ling-Hwei Chen, and Yu-Tai Ching. "Block-Based Steganography Method Using Optimal Selection to Reach High Efficiency and Capacity for Palette Images." Applied Sciences 10, no. 21 (November 4, 2020): 7820. http://dx.doi.org/10.3390/app10217820.
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The primary goal of steganographic methods is to develop statically undetectable methods with high steganographic capacity. The embedding efficiency is one kind of measure for undetectability. Block-based steganography methods have been proposed for achieving higher embedding efficiency under limited embedding capacity. However, in these methods, some blocks with larger embedding distortions are skipped, and a location map is usually incorporated into these methods to record the embedding status of each block. This reduces the embedding capacity for secret messages. In this study, we proposed a block-based steganography method without a location map for palette images. In this method, multiple secret bits can be embedded in a block by modifying at most one pixel with minimal embedding distortion; this enables each block to be used for data embedding; thus, our method provides higher embedding capacity. Furthermore, under the same capacity, the estimated and experimental embedding efficiencies of the proposed method are compared with those of Imaizumi et al. and Aryal et al.’s methods; the comparisons indicate that the proposed method has higher embedding efficiency than Imaizumi et al. and Aryal et al.’s methods.
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Miftahul Amri, Muhammad, Mahamadaree Waeno, and Muhammad Zain Musa. "LSB Steganography to Embed Creator's Watermark in Batik Digital Arts." Engineering Science Letter 2, no. 01 (March 11, 2023): 27–32. http://dx.doi.org/10.56741/esl.v2i01.301.
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This paper presents an implementation of Least Significant Bit (LSB) steganography to embed a hidden watermark in batik digital images. The goal of the technique is to provide a means of protecting the intellectual property of creators of batik digital arts while still allowing the images to be freely distributed and shared. The study demonstrates the effectiveness of the technique by showing that the embedded watermark is not visible to the human eye and does not significantly alter the quality of the image. The proposed technique uses the LSB method to embed a watermark in the binary data of the batik digital image. This method involves replacing the least significant bit of the image's pixel values with the bits of the watermark, thus altering the binary data of the image without causing noticeable changes in its visual appearance. The embedded watermark can only be detected by a decoding process, making it difficult to be removed or tampered with. The results of the study show that the implemented LSB steganography technique is successful in achieving its objective. The technique is able to effectively embed a hidden watermark in batik digital images without significantly altering the image quality or causing visual artifacts. This work highlights the potential of LSB steganography as a valuable tool for protecting the intellectual property of digital art creators, particularly in the field of batik images. Overall, this study contributes to the growing body of research on steganography and digital media protection. The successful implementation of the LSB steganography technique provides a promising approach for safeguarding the intellectual property of creators in the digital arts industry. Future research may explore other steganography techniques to address potential vulnerabilities and limitations of the LSB method.
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Ch., Rupa. "Squint Pixel Steganography." International Journal of Digital Crime and Forensics 8, no. 4 (October 2016): 37–47. http://dx.doi.org/10.4018/ijdcf.2016100104.
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Technology is playing a major role in the rapid growth of Techno media in relation to information security. Tampers are a major handicap while transferring medical images. In order to circumvent these issues used Steganography to hide the information inside a cover medium with different carrier formats. In this paper, the author proposes a novel squint pixel based medical image steganography technique to avoid distortion by an attacker. In this method, Original medical image itself acts as a carrier image. A Medical image segmented into two sets of pixels, Region of interest (ROI) and squint pixels of region of non-interest (RONI). The authentic data and information of ROI of a medical image embedded in penultimate and least significant bits (PLSB) of squint pixels of RONI. Results of experiments on various medical images show that the proposed method produces high quality stego medical images with high accuracy and recovery of ROI data without loss.
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Pawlak, Piotr, Jakub Podgórniak, and Grzegorz Kozieł. "An analysis of the possibility of realization steganography in C#." Journal of Computer Sciences Institute 21 (December 30, 2021): 383–90. http://dx.doi.org/10.35784/jcsi.2761.
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The computing power of modern computers is sufficient to break many cryptographic keys, therefore it is necessary to create an additional security layer which hides the very fact of transmitting a secret message. For this purpose, steganographic methods can be used. The article is devoted to the analysis of the possibility of implementing digital images steganography with the use of the C # programming language. Firstly, existing libraries and mathematical transformations which can help with performing steganography were found. Also, own code solutions were implemented. In order to objectively evaluate the methods of data hiding, the parameters describing the degree of distortion of transforms and hidden images were calculated. Subsequently, optimal solutions for specific problems were identified and demonstrational data hiding was performed. Based on the obtained results, it can be concluded that it is possible to successfully implement steganography in the C # language. There are many ready-made libraries and tools, the effectiveness of which has been verified in the conducted analysis. Due to the contradictory of stenographic requirements, it is not possible to meet all of them optimally, i.e. undetectability, resistance to destruction and information capacity. For this reason, it is not possible to clearly indicate the best solutions. In order to achieve satisfactory results, one should look for compromises between the set requirements.
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El Abbadi, Nidhal. "New Algorithm for Text in Text Steganography." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 26, 2021): 99–112. http://dx.doi.org/10.55562/jrucs.v23i2.483.
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Steganography is a technique to hide secret information in some other data (we call it a cover) without leaving any apparent evidence of data alteration. All of the traditional steganographic techniques have limited information- hiding capacity. They can hide only 10% (or less) of the data amounts of the cover. While much of the recent research in steganography has been on hiding data in images, many of the solutions that for images are more complicated when applied to natural language text as a cover medium. Many approaches to steganalysis attempt to detect statistical anomalies in cover data which predict the presence of hidden information. Natural language cover texts must not only pass the statistical muster of automatic analysis, but also the minds of human readers. This paper present a new algorithm to hide a large amount of text in cover text without effecting the cover, by using many types of pointers ( which are characters can interpreter as invisible character, or as apart of cover. Pointers used as single pointer or set of pointer to represent new single pointer. In this algorithm we can hide more than 40% of the data amounts of the cover.