A novel chaos‐based image encryption scheme has been proposed recently. In this scheme, redundancies of the Fridrich’s structure were reduced significantly via a new circular inter‐intra‐pixels bit‐level permutation strategy. However, we proved that the original encryption scheme is vulnerable to the known/chosen‐plaintext attacks. Both the permutation and diffusion phases have been improved to enhance the security of the original scheme. By shifting each row of the plain image randomly, known‐plaintext attacks could be resisted. Furthermore, by appending double crossover diffusion to the end of the original scheme, chosen‐plaintext attacks lost their efficacies. Simulation results demonstrated that the improved encryption scheme outperforms the original one.

Steganography has been widely used in the field of image privacy protection. However, with the advancement of steganalysis techniques, deep learning-based models are now capable of accurately detecting modifications in stego-images, posing a significant threat to traditional steganography. To address this, we propose AGASI, a GAN-based approach for strengthening adversarial image steganography. This method employs an encoder as the generator in conjunction with a discriminator to form a generative adversarial network (GAN), thereby enhancing the robustness of stego-images against steganalysis tools. Additionally, the GAN framework reduces the gap between the original secret image and the extracted image, while the decoder effectively extracts the secret image from the stego-image, achieving the goal of image privacy protection. Experimental results demonstrate that the AGASI method not only ensures high-quality secret images but also effectively reduces the accuracy of neural network classifiers, inducing misclassifications and significantly increasing the embedding capacity of the steganography system. For instance, under PGD attack, the adversarial stego-images generated by the GAN, at higher disturbance levels, successfully maintain the quality of the secret image while achieving an 84.73% misclassification rate in neural network detection. Compared to images with the same visual quality, our method increased the misclassification rate by 23.31%.

A novel method of meaningful image encryption is proposed in this paper. A secret image is encrypted into another meaningful image using the algorithm of reversible data hiding (RDH). High covertness can be ensured during the communication, and the possibility of being attacked of the secret image would be reduced to a very low level. The key innovation of the proposed method is that RDH is applied to compressive sensing (CS) domain, which brings a variety of benefits in terms of image sampling, communication and security. The secret image after preliminary encryption is embedded into the sparse representation coefficients of the host image with the help of the dictionary. The embedding rate could reach 2 bpp, which is significantly higher than those of other state-of-art schemes. In addition, the computational complexity of receiver is reduced. Simulations verify our proposal.

Nowadays, digital images are confronted with notable privacy and security issues, and many research works have been accomplished to countermeasure these risks. In this article, a novel scheme for data hiding in encrypted domain is proposed using fuzzy C-means clustering and compressive sensing technologies to protect privacy of the host image. The original image is preprocessed first to generate multiple highly correlated classes with fuzzy C-means clustering algorithm. Then, all classes are further divided into two parts according to proper threshold. One is encrypted by stream cipher, and the other is encrypted and compressed simultaneously with compressive sensing technology for easy data embedding by information hider. The receiver can extract additional data and recover the original image with data-hiding key and encryption key. Experiments and analysis demonstrate that the proposed scheme can achieve higher embedding rate about additional data and better visual quality of recovered image than other state-of-the-art schemes.

Recent studies on watermarking techniques based on image carriers have demonstrated new approaches that combine adversarial perturbations against steganalysis with embedding distortions. However, while these methods successfully counter convolutional neural network-based steganalysis, they do not adequately protect the data of the carrier itself. Recognizing the high sensitivity of Deep Neural Networks (DNNs) to small perturbations, we propose HAG-NET, a method based on image carriers, which is jointly trained by the encoder, decoder, and attacker. In this paper, the encoder generates Adversarial Steganographic Examples (ASEs) that are adversarial to the target classification network, thereby providing protection for the carrier data. Additionally, the decoder can recover secret data from ASEs. The experimental results demonstrate that ASEs produced by HAG-NET achieve an average success rate of over 99% on both the MNIST and CIFAR-10 datasets. ASEs generated with the attacker exhibit greater robustness in terms of attack ability, with an average increase of about 3.32%. Furthermore, our method, when compared with other generative stego examples under similar perturbation strength, contains significantly more information according to image information entropy measurements.