Relevant bibliographies by topics / Images - Steganography

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Academic literature on the topic 'Images - Steganography'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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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 (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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Dissertations / Theses on the topic "Images - Steganography"

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Zakaria, Ahmad. "Batch steganography and pooled steganalysis in JPEG images." Thesis, Montpellier, 2020. http://www.theses.fr/2020MONTS079.

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RÉSUMÉ :La stéganographie par lot consiste à dissimuler un message en le répartissant dans un ensemble d’images, tandis que la stéganalyse groupée consiste à analyser un ensemble d’images pour conclure à la présence ou non d’un message caché. Il existe de nombreuses stratégies d’étalement d’un message et on peut raisonnablement penser que le stéganalyste ne connaît pas celle qui est utilisée, mais il peut supposer que le stéganographe utilise le même algorithme d’insertion pour toutes les images. Dans ce cas, on peut montrer que la solution la plus appropriée pour la stéganalyse groupée est d’utiliser un unique détecteur quantitatif (c'est-à-dire qui prédit la taille du message caché), d’évaluer pour chaque image la taille du message caché (qui peut être nulle s'il n'y en a pas) et de faire la moyenne des tailles (qui sont finalement considérées comme des scores) obtenues sur l'ensemble des images.Quelle serait la solution optimale si maintenant, le stéganalyste pouvait discriminer la stratégie d’étalement parmi un ensemble de stratégies connues. Le stéganalyste pourrait-il utiliser un algorithme de stéganalyse groupé meilleur que la moyenne des scores ? Le stéganalyste pourrait-il obtenir des résultats proches du scénario dit "clairvoyant" où l’on suppose qu’il connaît exactement la stratégie d’étalement ?Dans cette thèse, nous essayons de répondre à ces questions en proposant une architecture de stéganalyse groupée fondé sur un détecteur quantitatif d’images et une fonction de groupement optimisée des scores. La première contribution est une étude des algorithmes de stéganalyse quantitatifs afin de décider lequel est le mieux adapté à la stéganalyse groupée. Pour cela, nous proposons d’étendre cette comparaison aux algorithmes de stéganalyse binaires et nous proposons une méthodologie pour passer des résultats de la stéganalyse binaire en stéganalyse quantitative et réciproquement.Le cœur de la thèse se situe dans la deuxième contribution. Nous étudions le scénario où le stéganalyste ne connaît pas la stratégie d’étalement. Nous proposons alors une fonction de groupement optimisée des résultats fondés sur un ensemble de stratégies d’étalement ce qui permet d’améliorer la précision de la stéganalyse groupée par rapport à une simple moyenne. Cette fonction de groupement est calculée en utilisant des techniques d’apprentissage supervisé. Les résultats expérimentaux obtenus avec six stratégies d’étalement différentes et un détecteur quantitatif de l’état de l’art confirment notre hypothèse. Notre fonction de groupement obtient des résultats proches d’un stéganalyste clairvoyant qui est censé connaître la stratégie d’étalement.Mots clés : Sécurité multimédia, Stéganographie par lot, Stéganalyse groupée, Apprentissage machine
ABSTRACT:Batch steganography consists of hiding a message by spreading it out in a set of images, while pooled steganalysis consists of analyzing a set of images to conclude whether or not a hidden message is present. There are many strategies for spreading a message and it is reasonable to assume that the steganalyst does not know which one is being used, but it can be assumed that the steganographer uses the same embedding algorithm for all images. In this case, it can be shown that the most appropriate solution for pooled steganalysis is to use a single quantitative detector (i.e. one that predicts the size of the hidden message), to evaluate for each image the size, the hidden message (which can be zero if there is none), and to average the sizes (which are finally considered as scores) obtained over all the images.What would be the optimal solution if now the steganalyst could discriminate the spreading strategy among a set of known strategies. Could the steganalyst use a pooled steganalysis algorithm that is better than averaging the scores? Could the steganalyst obtain results close to the so-called "clairvoyant" scenario where it is assumed that the steganalyst knows exactly the spreading strategy?In this thesis, we try to answer these questions by proposing a pooled steganalysis architecture based on a quantitative image detector and an optimized score pooling function. The first contribution is a study of quantitative steganalysis algorithms in order to decide which one is best suited for pooled steganalysis. For this purpose, we propose to extend this comparison to binary steganalysis algorithms and we propose a methodology to switch from binary steganalysis results to quantitative steganalysis and vice versa.The core of the thesis lies in the second contribution. We study the scenario where the steganalyst does not know the spreading strategy. We then propose an optimized pooling function of the results based on a set of spreading strategies which improves the accuracy of the pooled steganalysis compared to a simple average. This pooling function is computed using supervised learning techniques. Experimental results obtained with six different spreading strategies and a state-of-the-art quantitative detector confirm our hypothesis. Our pooling function gives results close to a clairvoyant steganalyst who is supposed to know the spreading strategy.Keywords: Multimedia Security, Batch Steganography, Pooled Steganalysis, Machine Learning
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Le, Pham [Verfasser]. "Detection of Steganography in Images with Statistical Models / Pham Le." München : Verlag Dr. Hut, 2014. http://d-nb.info/1052375162/34.

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Oliveira, Fábio Borges de. "Analysis of the cryptography security and steganography in images sequences." Laboratório Nacional de Computação Científica, 2007. http://www.lncc.br/tdmc/tde_busca/arquivo.php?codArquivo=134.

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Information security is being considered of great importance to the private and governamental institutions. For this reason, we opted to conduct a study of security in this dissertation. We started with an introduction to the information theory, and then we proposed a new kind of Perfect Secrecy cryptographic and finally made a study of steganography in an image sequence, in which we suggest a more aggressive steganography in coefficients of the discrete cosine transform.
A segurança da informação vem sendo considerada de grande importância para as instituições privadas e governamentais. Por este motivo, optamos em realizar um estudo sobre segurança nesta dissertação. Iniciamos com uma introdução à teoria da informação, partimos para métodos de criptografia onde propomos um novo tipo de Segredo Perfeito e finalmente fazemos um estudo de esteganografia em uma sequência de imagens, onde propomos uma esteganografia mais agressiva nos coeficientes da transformada discreta de cosseno.
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ESCOBAR, JAISSE GRELA. "A TOOL FOR TRACKING VIDEOS AND IMAGES USING STEGANOGRAPHY TECHNIQUES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2015. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25814@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
Na indústria de TV frequentemente ocorrem vazamentos de materiais de filmagem quando estes se distribuem entre os colaboradores de produção, prejudicando grandemente as empresas. Neste trabalho propomos uma ferramenta que, utilizando técnicas de esteganografia adaptativa, permite detectar a fonte do vazamento com um elevado grau de confiança. Um requisito importante é que a informação mascarada no vídeo (ou na imagem) resista a operações de processamento tais como redimensionamento e mudança de resolução. A ideia é usar o algoritmo Speeded Up Robust Features (SURF), estratégia consagrada, na detecção e descrição de características em imagens para detectar regiões robustas da imagem e inserir nelas uma pequena identificação mascarada. A ferramenta utiliza a transformada Haar – Discrete Wavelet Transform em duas dimensões, para depois fazer modificações na imagem. Esta dissertação propõe direções iniciais promissoras para a identificação segura de certificados de origem de imagens e vídeos.
In the TV industry, leaks of film materials occur frequently when they are distributed among the members of the production team, causing great harm to the companies. In this paper, we propose a tool that allows detecting the source of the leak with a high degree of confidence, using techniques of adaptive steganography. An important requirement is that the information embedded in the video (or image) should resist to processing operations such as resizing and resolution changes. The idea is to use the Speeded Up Robust Features (SURF) algorithm, a well-known strategy for detection and description of images features, to detect the robust regions of the image and insert a small masked identification in them. The tool uses the Haar - Discrete Wavelet Transform in two dimensions and then modifies the image. This dissertation proposes promising initial directions for secure identification of the certificate of origin of digital images and videos.
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Grajeda, Marín Ismael Rufino. "Increase in the information payload for steganography in digital images." Tesis de Licenciatura, Universidad Autónoma del Estado de México, 2019. http://hdl.handle.net/20.500.11799/105009.

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El presente artículo se deriva de una investigación previa realizada en la facultad de ingeniería [8], en la cual se propuso y desarrolló un algoritmo de esteganografía en imágenes digitales que permitiera mejorar en varios aspectos los métodos previamente existentes. Se desarrolló una adaptación al método denominado "diferencia de píxeles por tres vías". Con el objetivo de apoyar en la experimentación se realizó un estudio implementación del mismo, derivado de los resultados experimentales se desprendieron 3 nuevos algoritmos.
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Cerkez, Paul. "Automated Detection of Semagram-Laden Images." NSUWorks, 2012. http://nsuworks.nova.edu/gscis_etd/115.

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Digital steganography is gaining wide acceptance in the world of electronic copyright stamping. Digital media that are easy to steal, such as graphics, photos and audio files, are being tagged with both visible and invisible copyright stamp known as a digital watermark. However, these same methodologies are also used to hide communications between actors in criminal or covert activities. An inherent difficulty in developing steganography attacks is overcoming the variety of methods for hiding a message and the multitude of choices of available media. The steganalyst cannot create an attack until the hidden content method appears. When a message is visually transmitted in a non-textual format (i.e., in an image) it is referred to as a semagram. Semagrams are a subset of steganography and are relatively easy to create. However, detecting a hidden message in an image-based semagram is more difficult than detecting digital modifications to an image's structure. The trend in steganography is a decrease in detectable digital traces, and a move toward semagrams. This research outlines the creation of a novel, computer-based application, designed to detect the likely presence of a Morse Code based semagram message in an image. This application capitalizes on the adaptability and learning capabilities of various artificial neural network (NN) architectures, most notably hierarchical architectures. Four NN architectures [feed-forward Back-Propagation NN (BPNN), Self organizing Map (SOM), Neural Abstraction Pyramid (NAP), and a Hybrid Custom Network (HCN)] were tested for applicability to this domain with the best performing one being the HCN. Each NN was given a baseline set of training images (quantity based on NN architecture) then test images were presented, (each test set having 3,337 images). There were 36 levels of testing. Each subsequent test set representing an increase in complexity over the previous one. In the end, the HCN proved to be the NN of choice from among the four tested. The final HCN implementation was the only network able to successfully perform against all 36 levels. Additionally, the HCN, while only being trained on the base Morse Code images, successfully detected images in the 9 test sets of Morse Code isomorphs.
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Chandrababu, Aron. "Using an artificial neural network to detect the presence of image steganography." Akron, OH : University of Akron, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=akron1237343521.

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Thesis (M.S.)--University of Akron, Dept. of Computer Science, 2009.
"May, 2009." Title from electronic thesis title page (viewed 11/18/2009) Advisor, Kathy J. Liszka; Faculty Readers, Timothy W. O'Neil, Tim Margush; Department Chair, Wolfgang Pelz; Dean of the College, Chand Midha; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.
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Thorpe, Christopher. "Compression aided feature based steganalysis of perturbed quantization steganography in JPEG images." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file, 114 p, 2008. http://proquest.umi.com/pqdweb?did=1459914021&sid=6&Fmt=2&clientId=8331&RQT=309&VName=PQD.

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Rwabutaza, Allan Anthony. "A Cryptanalysis Methodology for the Reverse Engineering of Encrypted Information in Images." Wright State University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=wright1261417786.

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Abdulla, Alan Anwer. "Exploiting similarities between secret and cover images for improved embedding efficiency and security in digital steganography." Thesis, University of Buckingham, 2015. http://bear.buckingham.ac.uk/149/.

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The rapid advancements in digital communication technology and huge increase in computer power have generated an exponential growth in the use of the Internet for various commercial, governmental and social interactions that involve transmission of a variety of complex data and multimedia objects. Securing the content of sensitive as well as personal transactions over open networks while ensuring the privacy of information has become essential but increasingly challenging. Therefore, information and multimedia security research area attracts more and more interest, and its scope of applications expands significantly. Communication security mechanisms have been investigated and developed to protect information privacy with Encryption and Steganography providing the two most obvious solutions. Encrypting a secret message transforms it to a noise-like data which is observable but meaningless, while Steganography conceals the very existence of secret information by hiding in mundane communication that does not attract unwelcome snooping. Digital steganography is concerned with using images, videos and audio signals as cover objects for hiding secret bit-streams. Suitability of media files for such purposes is due to the high degree of redundancy as well as being the most widely exchanged digital data. Over the last two decades, there has been a plethora of research that aim to develop new hiding schemes to overcome the variety of challenges relating to imperceptibility of the hidden secrets, payload capacity, efficiency of embedding and robustness against steganalysis attacks. Most existing techniques treat secrets as random bit-streams even when dealing with non-random signals such as images that may add to the toughness of the challenges. This thesis is devoted to investigate and develop steganography schemes for embedding secret images in image files. While many existing schemes have been developed to perform well with respect to one or more of the above objectives, we aim to achieve optimal performance in terms of all these objectives. We shall only be concerned with embedding secret images in the spatial domain of cover images. The main difficulty in addressing the different challenges stems from the fact that the act of embedding results in changing cover image pixel values that cannot be avoided, although these changes may not be easy to detect by the human eye. These pixel changes is a consequence of dissimilarity between the cover LSB plane and the secretimage bit-stream, and result in changes to the statistical parameters of stego-image bit-planes as well as to local image features. Steganalysis tools exploit these effects to model targeted as well as blind attacks. These challenges are usually dealt with by randomising the changes to the LSB, using different/multiple bit-planes to embed one or more secret bits using elaborate schemes, or embedding in certain regions that are noise-tolerant. Our innovative approach to deal with these challenges is first to develop some image procedures and models that result in increasing similarity between the cover image LSB plane and the secret image bit-stream. This will be achieved in two novel steps involving manipulation of both the secret image and the cover image, prior to embedding, that result a higher 0:1 ratio in both the secret bit-stream and the cover pixels‘ LSB plane. For the secret images, we exploit the fact that image pixel values are in general neither uniformly distributed, as is the case of random secrets, nor spatially stationary. We shall develop three secret image pre-processing algorithms to transform the secret image bit-stream for increased 0:1 ratio. Two of these are similar, but one in the spatial domain and the other in the Wavelet domain. In both cases, the most frequent pixels are mapped onto bytes with more 0s. The third method, process blocks by subtracting their means from their pixel values and hence reducing the require number of bits to represent these blocks. In other words, this third algorithm also reduces the length of the secret image bit-stream without loss of information. We shall demonstrate that these algorithms yield a significant increase in the secret image bit-stream 0:1 ratio, the one that based on the Wavelet domain is the best-performing with 80% ratio. For the cover images, we exploit the fact that pixel value decomposition schemes, based on Fibonacci or other defining sequences that differ from the usual binary scheme, expand the number of bit-planes and thereby may help increase the 0:1 ratio in cover image LSB plane. We investigate some such existing techniques and demonstrate that these schemes indeed lead to increased 0:1 ratio in the corresponding cover image LSB plane. We also develop a new extension of the binary decomposition scheme that is the best-performing one with 77% ratio. We exploit the above two steps strategy to propose a bit-plane(s) mapping embedding technique, instead of bit-plane(s) replacement to make each cover pixel usable for secret embedding. This is motivated by the observation that non-binary pixel decomposition schemes also result in decreasing the number of possible patterns for the three first bit-planes to 4 or 5 instead of 8. We shall demonstrate that the combination of the mapping-based embedding scheme and the two steps strategy produces stego-images that have minimal distortion, i.e. reducing the number of the cover pixels changes after message embedding and increasing embedding efficiency. We shall also demonstrate that these schemes result in reasonable stego-image quality and are robust against all the targeted steganalysis tools but not against the blind SRM tool. We shall finally identify possible future work to achieve robustness against SRM at some payload rates and further improve stego-image quality.
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Books on the topic "Images - Steganography"

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Yahya, Abid. Steganography Techniques for Digital Images. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-78597-4.

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Currie, Daniel L. Implementation and efficiency of steganographic techniques in bitmapped images and embedded data survivability against lossy compression schemes. Monterey, Calif: Naval Postgraduate School, 1996.

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Yahya, Abid. Steganography Techniques for Digital Images. Springer, 2018.

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Yahya, Abid. Steganography Techniques for Digital Images. Springer International Publishing AG, 2019.

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(Editor), Stefan Katzenbeisser, and Fabien, A.P. Petitcolas (Editor), eds. Information Hiding Techniques for Steganography and Digital Watermarking. Artech House Publishers, 2000.

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Steganography and Watermarking. Nova Science Pub Inc, 2013.

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Image Steganography for Hidden Communication. Storming Media, 2000.

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Digital Image and Video Watermarking and Steganography. IntechOpen, 2019.

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McAmis, Monica. Steganographic techniques in cryptography. 2000.

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Digital Image and Video Watermarking and Steganography [Working Title]. IntechOpen, 2018. http://dx.doi.org/10.5772/intechopen.75155.

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Book chapters on the topic "Images - Steganography"

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Yahya, Abid. "Steganography Techniques." In Steganography Techniques for Digital Images, 9–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78597-4_2.

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Yahya, Abid. "Introduction to Steganography." In Steganography Techniques for Digital Images, 1–7. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78597-4_1.

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Yahya, Abid. "Characteristic Region-Based Image Steganography." In Steganography Techniques for Digital Images, 43–83. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78597-4_3.

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Yahya, Abid. "An Enhanced Robust and Protected Image Steganographic System." In Steganography Techniques for Digital Images, 85–111. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78597-4_4.

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Yahya, Abid. "Conclusion Toward Hidden Communication." In Steganography Techniques for Digital Images, 113–17. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78597-4_5.

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Modi, Mangat Rai, Saiful Islam, and Phalguni Gupta. "Edge Based Steganography on Colored Images." In Intelligent Computing Theories, 593–600. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39479-9_69.

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Jagadeesh, Noopa, Aishwarya Nandakumar, P. Harmya, and S. S. Anju. "Secret Image Sharing Using Steganography with Different Cover Images." In Advances in Computing and Communications, 490–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22714-1_50.

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Agaian, Sos S., and Ravindranath C. Cherukuri. "Run Length Based Steganography for Binary Images." In Lecture Notes in Computer Science, 481–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11590316_75.

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Gupta, Anjali, Lalit K. Awasthi, and Samayveer Singh. "Steganography Methods for GIF Images: A Review." In Proceedings of Fourth International Conference on Computing, Communications, and Cyber-Security, 657–70. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1479-1_49.

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Hemalatha, S., U. Dinesh Acharya, A. Renuka, and Priya R. Kamath. "A Secure Image Steganography Technique to Hide Multiple Secret Images." In Lecture Notes in Electrical Engineering, 613–20. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-6154-8_60.

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Conference papers on the topic "Images - Steganography"

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Mojsilović, Marija, Selver Pepić, and Goran Miodragović. "Implementation of embedded messages using steganography in the PHP software package." In 9th International Scientific Conference Technics and Informatics in Education. University of Kragujevac, Faculty of Technical Sciences Čačak, 2022. http://dx.doi.org/10.46793/tie22.171m.

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The term steganography is usually associated with hiding and concealing information and messages. People, and even IT professionals, very rarely come into contact with steganography and steganalysis. Only messages are protected by cryptographic protection, while steganography can be said to protect both messages and parties participating in the communication. Steganography also means hiding messages inside computer files and data streams. This paper provides an overview of the implementation of embedded messages using steganography in the PHP software package. Emphasis is placed on hiding information, i.e. messages in JPEG images. As well as decoding or reading a hidden message. The field of steganography is naturally linked to the field of steganalysis, the primary goal of which is the detection of a hidden message, and then its extraction from the object of the message carrier. The most commonly used method for hiding messages is LSB, it is a method that changes the least significant bits to match the secret message. Then, by passing the steganographic key, the message is encrypted using the RC4 algorithm.
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Saha, Abhisek, Sholanki Halder, and Shama Kollya. "Image steganography using 24-bit bitmap images." In 2011 14th International Conference on Computer and Information Technology (ICCIT). IEEE, 2011. http://dx.doi.org/10.1109/iccitechn.2011.6164873.

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Sange, Sanjay R., Suhas M. Patil, Sudeep Thepade, and Nilesh N. Gawade. "Steganography: Hiding Half Tone Images." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697558.

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Cheddad, Abbas, Joan Condell, Kevin Curran, and Paul Mc Kevitt. "Enhancing Steganography in Digital Images." In 2008 Canadian Conference on Computer and Robot Vision (CRV). IEEE, 2008. http://dx.doi.org/10.1109/crv.2008.54.

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Spaulding, Jeremiah, Hideki Noda, Mahdad N. Shirazi, Michiharu Niimi, and Eiji Kawaguchi. "Steganography using wavelet compressed images." In Photonics West 2001 - Electronic Imaging, edited by Ping W. Wong and Edward J. Delp III. SPIE, 2001. http://dx.doi.org/10.1117/12.435415.

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Mei-Ching Chen, Sos S. Agaian, and C. L. Philip Chen. "Generalized collage steganography on images." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811419.

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Marwaha, Piyush, and Paresh Marwaha. "Visual cryptographic steganography in images." In 2010 International Conference on Computing, Communication and Networking Technologies (ICCCNT'10). IEEE, 2010. http://dx.doi.org/10.1109/icccnt.2010.5591730.

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Gopalan, Kaliappan. "An image steganography implementation for JPEG-compressed images." In 2007 International Symposium on Communications and Information Technologies. IEEE, 2007. http://dx.doi.org/10.1109/iscit.2007.4392114.

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Agaian, Sos S., and Johanna M. Susmilch. "Fractal steganography using artificially generated images." In 2006 IEEE Region 5 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tpsd.2006.5507412.

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Yang, Ching-Nung, Yao-Yu Yang, Tse-Shih Chen, and Guo-Cin Ye. "New Steganography Scheme in Halftone Images." In 2008 Fourth International Conference on Intelligent Information Hiding and Multimedia Signal Processing (IIH-MSP). IEEE, 2008. http://dx.doi.org/10.1109/iih-msp.2008.265.

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Reports on the topic "Images - Steganography"

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Duffany, Jeffrey L., and Marcus D. Velez. Steganography and Steganalysis in Digital Images. Fort Belvoir, VA: Defense Technical Information Center, January 2012. http://dx.doi.org/10.21236/ada582940.

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Marvel, Lisa M. Image Steganography for Hidden Communication. Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada377277.

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Marvel, Lisa M., Charles G. Boncelet, Retter Jr., and Charles T. Methodology of Spread-Spectrum Image Steganography. Fort Belvoir, VA: Defense Technical Information Center, June 1998. http://dx.doi.org/10.21236/ada349102.

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Brundick, Frederick S., and Lisa M. Marvel. Implementation of Spread Spectrum Image Steganography. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada392155.

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Brundick, Frederick S., George W. Hartwig, Marvel Jr., and Lisa M. Reducing Spread Spectrum Image Steganography (SSIS) Extraction Errors With Feedback-Driven Adjustment. Fort Belvoir, VA: Defense Technical Information Center, December 2002. http://dx.doi.org/10.21236/ada409026.

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