Multi-carrier information hiding algorithm based on three-dimensional model’s concave-convex structure characteristics by Shuai REN, Lei SHI, Binbin WANG, Huirong CHENG, Qianqian ZHANG, Honglin LIU

“…Aiming at the problem that the embedding capacity, invaibility and robustness of a single-carrier information hiding algorithm cannot be further improved due to the limitation of the number of carriers, the voxelization of the carrier and the embedding of secret information were combined with the concave-convex structure characteristics of the three-dimensional model, and a method was proposed.A multi-carrier information hiding algorithm based on the three-dimensional model’s concave-convex structure features.Firstly, the three-dimensional model was voxelized, and the three-dimensional model’s concave-convex structure features were extracted from the data set obtained after voxelization to classify the carrier library, and the concave-convex degree was obtained by conversion after the interval was encoded.Secondly, the secret information was segmented according to the number of carrier classifications and scrambled and optimized, so that the embedding of the carrier and the secret information was effectively connected through its classification and number of segments, and double embedding of secret information by encoding data of concavity intervals and voxelized coordinate points, respectively, to further improve the performance of the algorithm.Finally, the genetic algorithm was applied to optimize the secret information to complete the information hiding.The experiment shows that compared with the high-capacity three-dimensional model steganography algorithm based on a single carrier, the invisibility, robustness and capacity of the algorithm were significantly improved.…”
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Revealing Traces of Image Resampling and Resampling Antiforensics by Anjie Peng, Yadong Wu, Xiangui Kang

“…The forensics of resampling plays an important role in image tampering detection, steganography, and steganalysis. In this paper, we proposed an effective and secure detector, which can simultaneously detect resampling and its forged resampling which is attacked by antiforensic schemes. …”
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Medical Image Encryption and Decryption Based on DNA: A Survey by Saja Theab Ahmed, Dalal Abdulmohsin Hammood, Raad Farhood Chisab, Nurulisma Binti Hj. Ismail

“…If DNA is employed appropriately, it can be used to achieve a number of security technologies, including encryption, steganography, signature, and authentication through the use of DNA molecules as information carriers. …”
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Adversarial subdomain adaptation network for mismatched steganalysis by Lei ZHANG, Hongxia WANG

“…Once data in the training and test sets come from different cover sources, that is, under the condition of cover source mismatch, it usually makes the detection accuracy rate of an outstanding steganalysis model to be reduced.In practical applications, the analyzers need to process images collected from the Internet.However, compared with the training set data, these suspicious images are likely to have completely different capture and processing histories, which may lead to the degradation of steganalysis model.It is also why steganalysis tools are difficult to deploy successfully in the real-world applications.To improve the practical application value of steganalysis methods based on deep learning, test sample information is utilized and domain adaptation method is used to solve the problem of cover source mismatch.Regarding the training set data as the source domain and test set data as the target domain, the detection performance of steganalysis models in the target domain is enhanced by minimizing the discrepancy between the feature distribution of source domain and target domain.ASAN (adversarial subdomain adaptation network) was proposed from the perspective of feature generation on the one hand.The source domain features and target domain features generated by the steganalysis model were required to be as similar as possible, so that the discriminator cannot distinguish which domain the features came from.On the other hand, to reduce the difference of feature distribution between domains, the subdomain adaptation method was adopted to reduce the unexpected change of the distribution of related subdomains.The distance between the cover and stego samples was enlarged effectively to improve the classification accuracy.After testing three steganography algorithms on multiple datasets, it is confirmed that the proposed method can effectively improve the detection accuracy rate of the model in the case of dataset mismatch and algorithm mismatch and it can also reduce the negative impact of the mismatch problem of the model.…”
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Pengamanan Citra Digital Menggunakan Kriptografi DnaDan Modified LSB by Sabrina Adela Br Sibarani, Andreas Munthe, Ronsen Purba, Ali Akbar Lubis

“…After encryption, the ciphertext is hidden within a cover image using Modified Least Significant Bit (MLSB) steganography, which optimizes bit insertion in the RGB channels by selecting random pixels using a modulo generator. …”
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