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Academic literature on the topic 'Images - Steganography'
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Journal articles on the topic "Images - Steganography"
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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 (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, et al. "A Novel Steganography Method for Character-Level Text Image Based on Adversarial Attacks." Sensors 22, no. 17 (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 (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 (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, et al. "IDGAN: Information-Driven Generative Adversarial Network of Coverless Image Steganography." Electronics 12, no. 13 (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 (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 (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 (2021): 615. http://dx.doi.org/10.21533/pen.v9i3.2203.
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