OPTIMAL PIXEL ADJUSTMENT BASED REVERSIBLE STEGANOGRAPHY

A novel prediction-based reversible steganographic scheme based on image in-painting is used to embed the secret information. First, reference pixels are chosen adaptively according to the distribution characteristics of the image content. Then, the image in-painting technique based on partial differential equations (PDE) was introduced to generate a prediction image that has similar structural and geometric information as the cover image. Finally, by using the two selected groups of peak points and zero points, the histogram of the prediction error is shifted to embed the secret bits reversibly[1]. Since the same reference pixels can be exploited in the extraction procedure, the embedded secret bits can be extracted from the stego image correctly, and the restoration of the cover image is lossless. Through, the use of the adaptive strategy for choosing reference pixels and the in-painting predictor, the more embeddable pixels are acquired.However, PDE based in-painting algorithm is computationally complex and requires more execution time. Also, the quality of the stego image is not considered in the in-painting algorithm. To improve the visual quality of the stego image Optimal Pixel Adjustment algorithm (OPA) can be used. The OPA is applied after embedding the message. The frequency domain is employed to increase the robustness of the steganography method. OPA algorithm is to minimize the error difference between the original coefficient value and the altered value by checking the right next bit to the modified LSBs so that the resulted change will be minimal. This research work uses OPA to obtain an optimal mapping function to reduce the difference error between the cover and the stego-image which increases the hiding capacity with low distortions and Peak Signal to Noise Ratio (PSNR)

Deep Learning for Reversible Steganography: Principles and Insights

Deep-learning\textendash{centric} reversible steganography has emerged as a promising research paradigm. A direct way of applying deep learning to reversible steganography is to construct a pair of encoder and decoder, whose parameters are trained jointly, thereby learning the steganographic system as a whole. This end-to-end framework, however, falls short of the reversibility requirement because it is difficult for this kind of monolithic system, as a black box, to create or duplicate intricate reversible mechanisms. In response to this issue, a recent approach is to carve up the steganographic system and work on modules independently. In particular, neural networks are deployed in an analytics module to learn the data distribution, while an established mechanism is called upon to handle the remaining tasks. In this paper, we investigate the modular framework and deploy deep neural networks in a reversible steganographic scheme referred to as prediction-error modulation, in which an analytics module serves the purpose of pixel intensity prediction. The primary focus of this study is on deep-learning\textendash{based} context-aware pixel intensity prediction. We address the unsolved issues reported in related literature, including the impact of pixel initialisation on prediction accuracy and the influence of uncertainty propagation in dual-layer embedding. Furthermore, we establish a connection between context-aware pixel intensity prediction and low-level computer vision and analyse the performance of several advanced neural networks