Randomized Symmetric Crypto Spatial Fusion Steganographic System

The image fusion steganographic system embeds encrypted messages in decomposed multimedia carriers using a pseudorandom generator but it fails to evaluate the contents of the cover image. This results in the secret data being embedded in smooth regions, which leads to visible distortion that affects the imperceptibility and confidentiality. To solve this issue, as well as to improve the quality and robustness of the system, the Randomized Symmetric Crypto Spatial Fusion Steganography System is proposed in this study. It comprises three-subsystem bitwise encryption, spatial fusion, and bitwise embedding. First, bitwise encryption encrypts the message using bitwise operation to improve the confidentiality. Then, spatial fusion decomposes and evaluates the region of embedding on the basis of sharp intensity and capacity. This restricts the visibility of distortion and provides a high embedding capacity. Finally, the bitwise embedding system embeds the encrypted message through differencing the pixels in the region by 1, checking even or odd options and not equal to zero constraints. This reduces the modification rate to avoid distortion. The proposed heuristic algorithm is implemented in the blue channel, to which the human visual system is less sensitive. It was tested using standard IST natural images with steganalysis algorithms and resulted in better quality, imperceptibility, embedding capacity and invulnerability to various attacks compared to other steganographic systems

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

Preventing Unauthorized AI Over-Analysis by Medical Image Adversarial Watermarking

The advancement of deep learning has facilitated the integration of Artificial Intelligence (AI) into clinical practices, particularly in computer-aided diagnosis. Given the pivotal role of medical images in various diagnostic procedures, it becomes imperative to ensure the responsible and secure utilization of AI techniques. However, the unauthorized utilization of AI for image analysis raises significant concerns regarding patient privacy and potential infringement on the proprietary rights of data custodians. Consequently, the development of pragmatic and cost-effective strategies that safeguard patient privacy and uphold medical image copyrights emerges as a critical necessity. In direct response to this pressing demand, we present a pioneering solution named Medical Image Adversarial watermarking (MIAD-MARK). Our approach introduces watermarks that strategically mislead unauthorized AI diagnostic models, inducing erroneous predictions without compromising the integrity of the visual content. Importantly, our method integrates an authorization protocol tailored for legitimate users, enabling the removal of the MIAD-MARK through encryption-generated keys. Through extensive experiments, we validate the efficacy of MIAD-MARK across three prominent medical image datasets. The empirical outcomes demonstrate the substantial impact of our approach, notably reducing the accuracy of standard AI diagnostic models to a mere 8.57% under white box conditions and 45.83% in the more challenging black box scenario. Additionally, our solution effectively mitigates unauthorized exploitation of medical images even in the presence of sophisticated watermark removal networks. Notably, those AI diagnosis networks exhibit a meager average accuracy of 38.59% when applied to images protected by MIAD-MARK, underscoring the robustness of our safeguarding mechanism

Reversible Data Hiding in Encrypted Images Using MSBs Integration and Histogram Modification

This paper presents a reversible data hiding in encrypted image that employs based notions of the RDH in plain-image schemes including histogram modification and prediction-error computation. In the proposed method, original image may be encrypted by desire encryption algorithm. Most significant bit (MSB) of encrypted pixels are integrated to vacate room for embedding data bits. Integrated ones will be more resistant against failure of reconstruction if they are modified for embedding data bits. At the recipient, we employ chess-board predictor for lossless reconstruction of the original image by the aim of prediction-error analysis. Comparing to existent RDHEI algorithms, not only we propose a separable method to extract data bits, but also content-owner may attain a perfect reconstruction of the original image without having data hider key. Experimental results confirm that the proposed algorithm outperforms state of the art ones