Reversible data hiding in JPEG images is critical for secure multimedia applications, while, existing histogram modification schemes are confined to fixed 1D or 2D dimensions. These methods fail to adapt to the characteristics of cover images and the demands of embedded data, resulting in suboptimal trade-offs among embedding capacity, image quality, and file size. To address these issues, this study proposes a Multi-Dimensional Histogram Modification Framework that advances histograms from "fixed dimension" to "variable dimension" and extends them to high-dimensional structures. Our key contributions are as follows: first, we propose an adaptive dimension selection strategy that dynamically determines the optimal histogram dimension by evaluating the capacity and distortion for each cover image and embedding task; second, we develop a high-dimensional histogram construction method that combines AC coefficients with absolute values of 1 and 2 at the same frequency to enhance the utilization of coefficient correlation; third, we design an optimized mapping algorithm that searches for the optimal mapping matrix to minimize both distortion and file expansion. Experimental results show that the proposed method outperforms existing 1D and 2D histogram modification schemes. On average, it increases the embedding capacity by 5000 bits, improves the image quality by 0.2 dB, and reduces the file size by 4000 bits.

Reversible Adversarial Example (RAE) could be used to protect the privacy and copyright of images on social networks (SONs) by exploring the adversarial examples to disrupt the access of malicious AI models while ensuring recoverability with authorized users. Existing RAE methods add adversarial perturbations in spatial images which do not apply to JPEG images, the most widely adopted image format for image storage and transmission. To tackle this issue, we propose the first Reversible Adversarial Example (JPEG-RAE) generation framework for JPEG images, which consists of two primary components, i.e., JPEG-AE and G-RDH. JPEG-AE crafts the adversarial perturbations in the JPEG domain of images by leveraging chain rule of gradient propagation, so that they could effectively mislead the AI models in spatial domain when they are JPEG decompressed. And G-RDH adopts a gradient-directed bi-directional histogram shifting scheme for efficient reversible hiding of adversarial perturbations and location data in JPEG domain, where the histogram shifting is in sync with the sign of back-propagated gradients to further boost the performance of adversarial attacks. Experimental validation demonstrates that, although confined to the JPEG format such as the amount and intensity of alterable DCT coefficients, the proposed JPEG-RAE could still show superior or comparable performance, in terms of attack ability and recover ability, to its counterparts in spatial domain.

Robust reversible watermarking (RRW) is one of the most popular areas in information hiding. Existing schemes have two drawbacks: 1) schemes that can resist conventional attacks often fail to resist geometric attacks, and 2) schemes that can resist geometric attacks often are not robust against conventional attacks and have poor stability. Inspired by the high robustness of fractional-order orthogonal moments (FoOM) and the good feature of resistance to geometric attacks of Zernike moments and pseudo-Zernike moments (ZM/PZM), in this research, FoOM is used to optimize ZM/PZM to obtain FoZM/FoPZM (namely, fractional-order Zernike moments and fractional-order pseudo-Zernike moments). Furthermore, a denoiser is proposed to preprocess the watermarks to improve the robustness against geometric and conventional attacks, the amount of extracted auxiliary information is decreases and the extraction process of auxiliary information is designed to be more stable. Specifically, first, the source of the difference between the watermarked image and the carrier image is identified, that difference is represented with less information, and then that information is embedded into the cover image as auxiliary information. Second, the watermark is embedded in the low-order FoZM/FoPZM component. Finally, the watermarked image is denoised using a denoiser before extracting the watermark. The experimental results show that the scheme has good stability and strong robustness. Compared with existing methods, the proposed scheme has a small and stable auxiliary information size, strong robustness to noise attacks such as Gaussian noise and salt-and-pepper noise attacks, and better resistance to geometric attacks such as rotation and scaling attacks.