Quantization-table-modification (QTM) is widely utilized in current studies of reversible data hiding (RDH) for JPEG images. However, the performance of existing QTM-based methods is far from optimal due to the uniform quantization step division and imperfect distortion modeling. In this letter, by incorporating Syndrome-Trellis-Code (STC) into QTM, a novel hybrid JPEG images RDH method is proposed. Firstly, instead of the uniformly dividing strategy conducted in previous works, by adaptively dividing the quantization steps, a hybrid embedding mechanism combining binary and ternary embedding is proposed. Then, the corresponding capacity-distortion model is established, by which the spatial domain distortion is estimated. Finally, based on the derived capacity-distortion model, for performance optimization, STC is utilized to minimize the cover modification. In this way, JPEG images RDH can be effectively conducted so that the visual quality of the marked image is well maintained. Experimental results demonstrate that the proposed method significantly outperforms some state-of-the-art works in terms of visual quality.

Reversible data hiding (RDH) for JPEG images remains relatively underexplored, with key challenges lying in coefficient selection and modification strategies. Existing methods select coefficients for embedding through block-wise or frequency-band-based operations, resulting in coarse-grained decisions that constrain embedding performance. In this paper, a novel RDH scheme for JPEG images based on gap-driven histograms generation with coefficient-wise selection is proposed. First, a multi-metric weighted complexity and coefficient-wise selection approach is proposed, integrating four local feature criteria to assess each coefficient individually, enabling more precise per-coefficient selection. Then, a gap-driven adaptive multi-histogram generation strategy is introduced, leveraging gap pairs to minimize shifting distortion by segmenting histograms via bisection and avoiding modifications to high-magnitude coefficients. Experimental results confirm that the proposed method achieves improved visual quality and more efficient file size control compared to existing state-of-the-art approaches.

Reversible data hiding (RDH) for JPEG images, particularly those focusing on DCT coefficient modification, has garnered significant attention in recent years. Existing methods primarily select coefficients valued$\pm 1$for expansion embedding to avoid significant file size increases caused by modifying zero-valued DCT coefficients. However, zero-valued coefficients, which constitute the majority of DCT coefficients, are more suitable for data embedding to reduce the shift distortion. To efficiently utilize zero-valued coefficients for high-capacity embedding while controlling the file size increment, this paper introduces a novel JPEG RDH method based on ternary matrix embedding, where ternary syndrome trellis codes (STC) is employed on selected zero-valued coefficients to minimize the expansion embedding distortion, and other non-zero-valued coefficients are shifted for reversibility. Furthermore, a novel DCT coefficients measurement strategy is proposed for coefficient selection to further reduce the shift distortion. Extensive experimental validations demonstrate the superiority of the proposed method in various evaluation criteria. Notably, the proposed method achieves more than twice the embedding capacity of some state-of-the-art methods at the same PSNR while maintaining file size increment within acceptable bounds.

DeepFake, an AI-driven face-swapping technique, has been weaponized to spread disinformation. In response, researchers have developed forensic detectors to identify such manipulations. To circumvent these defenses, a growing body of work now focuses on generating adversarial samples—carefully perturbed forgeries designed to deceive detection tools. However, most existing adversarial generation methods sacrifice image quality to achieve undetectability, introducing perceptible artifacts that ironically make them more detectable under human scrutiny. To address this limitation, we propose a novel spectral fusion approach to multimodally synthesize forgery traces from authentic facial images. Unlike traditional noise injection methods, our technique integrates diffusion-based noise during image preprocessing, embedding perturbations in the forward process of a diffusion model. This approach not only deceives forensic detectors more effectively but also preserves high visual fidelity. Through extensive experiments, our method achieves state-of-the-art DeepFake anti-forensic performance while preserving high visual fidelity, ensuring that the adversarial samples remain indistinguishable from real images.

Recently, matrix embedding (ME), a well-known steganographic technique, has been employed in reversible data hiding (RDH) for the first time, improving the performance of single histogram modification (SHM) methods. In this letter, the ME-based RDH strategy is extended from SHM to the more effective multiple histograms modification (MHM) to further improve the reversible embedding performance. The capacity-distortion model is first established in the novel scenario. Then, some theoretical results for payload partition and expansion-bins-determination are given. Finally, based on the derived theoretical investigations, an efficient RDH method with low computational complexity is proposed. Experimental results show that the proposed method can achieve better visual quality compared to some state-of-the-art methods.

In this paper, a general reversible data hiding (RDH) framework for joint photographic experts group (JPEG) images with multiple two dimensional histograms (2DHs) is proposed. Regardless of whether zero alternating current (AC) coefficients are included to join data embedding or only non-zero AC coefficients are applied, the performance in terms of visual quality and file size increment is improved by using the proposed framework. This framework is mainly composed of the following three parts: histogram generation, adaptive 2DH mapping selection, and improved discrete particle swarm optimization (IDPSO). Unlike existing 2DH-based JPEG RDH methods, in which a uniform threshold is utilized to construct multiple histograms, in histogram generation, thresholds for different histograms are adaptively assigned according to the local properties of histogram coefficients. As a result, as many coefficients in complex regions as possible are excluded from the construction of each histogram. We subtly design multiple 2DH mappings, and adaptively select 2DH mappings for different 2DHs based on their distribution characteristics. Through slight adjustments, each 2DH mapping can be employed in cases where either zero AC coefficients or only non-zero AC coefficients are used for data embedding. Adaptive threshold and 2DH mapping selection provide a better image quality at a given embedding capacity but inevitably cause considerable complexity cost. To significantly reduce the computational cost, we propose IDPSO by combining differential evolution. IDPSO has the advantages of rapid convergence speed as well as satisfactory qualities of the best solutions. With the help of differential evolution, IDPSO expands the diversity of particles and efficiently avoids local optimal trapping problems. The experimental results also demonstrate the effectiveness of the proposed method in terms of visual quality, file size increment and complexity cost.

Reversible data hiding based on joint photographic experts group (JPEG) images has been extensively studied to enhance embedding performance in terms of visual quality and file size preservation at the desired payload. In this paper, an efficient adaptive RDH method for JPEG images with multiple two-dimensional (2D) histogram modification is proposed. Firstly, the proposed method proposes the block smoothness estimator and the band smoothness estimator, and then combines the two estimators to reduce the embedding distortion as much as possible at the desired payload. Instead of adopting a fixed 2D mapping or choosing one from several empirically-designed mappings for each 2D histogram, the proposed method designs an adaptive 2D mapping generation strategy to adaptively generate a large number of mappings with considering the local characteristics of histogram distribution. Since exhaustively searching for the optimal mapping achieving the highest embedding performance for each 2D histogram is time-consuming, an improved discrete particle swarm optimization is utilized in the proposed method to speed up the optimization process. Extensive experimental results also demonstrate the effectiveness of the proposed method in terms of visual quality and file size increment of the stego image.

Prediction-error expansion (PEE) is the most popular reversible data hiding (RDH) technique due to its efficient capacity-distortion tradeoff. With the generated prediction-error histogram (PEH) and adaptively selected expansion bins, the image redundancy is well exploited by PEE. However, for the most widely used rhombus predictor, the rounding operation which groups different prediction-errors into one value is completely unnecessary. The embedding can be extended to a general case by removing the rounding operation, and more histogram bins can be derived for expansion with a new mapping mechanism. Therefore, in this article, instead of pixel prediction-error, we propose to compute the pixel residuals without the rounding operation, and a new embedding mechanism based on pixel-residual histogram (PRH) modification is devised. In PRH, four bins correspond to one bin in PEH. Then, different from the one-to-one mapping between the prediction-error and pixel modification, a four-to-one mapping between the pixel-residual and pixel modification is established, and the performance is optimized by adaptively selecting four expansion bin pairs for embedding. Since more modification selections are considered, better performance can be obtained. Moreover, the proposed scheme is extended to the two-dimensional (2D) histogram and multiple histograms based embedding, and the performance is further enhanced. The superiority of the proposed method is experimentally verified by comparing it with some state-of-the-art works.

Reversible data hiding (RDH) is a research hotspot nowadays. By RDH, after data extraction, the cover image can be restored without information loss. Among numerous existing RDH techniques, multiple histograms modification (MHM) is a general reversible embedding framework, and it is experimentally verified better than the traditional single histogram based methods. However, the expansion-bins-determination process for MHM is conducted through naive exhaustive search, which is time consuming. Based on this consideration, a fast expansion-bins-determination method for MHM is proposed in this paper. Specifically, to determine the optimal expansion bins, instead of solving the optimization problem of discrete variables, we consider a general form of this problem with differentiable objective function and real variables, so that advanced analysis tools such as Lagrange multiplier can be utilized. By the proposed approach, compared with the original MHM, the expansion bins can be determined quickly with only a tiny performance loss, and thus the practicality of MHM is improved.

The problem of general distortion model for reversible data hiding (RDH) is investigated in this letter. Unlike previous RDH schemes that regard the cover image as a memory-less sequence and assign the same distortion for each pixel, in this work, the modification distortion is adaptively defined for each pixel for visual performance enhancement. The situation is totally different compared with the traditional RDH approaches, and the classical histogram based methods can not be utilized. To deal with this new and challenging problem, we then propose a two-steps embedding framework. Considering binary image as cover, firstly, some pixels are selected and losslessly compressed as the reconstruction information for image recovery. Then, with the adaptively defined distortion and by utilizing matrix embedding, the secret message and the reconstruction information are embedded into the cover. In this way, reversible embedding is realized while the total distortion is minimized. By comparing with some previous RDH schemes for binary covers, experimental results show that better visual performance is achieved by the proposed method.

Reversible data hiding (RDH) for JPEG images has attracted extensive attentions in recent years. However, since DCT is a de-correlation operation, it is difficult for JPEG images to employ the image redundancy for RDH design like uncompressed images. In this paper, considering better utilizing the special properties of quantized DCT coefficients, a new RDH scheme for JPEG images based on multiple two-dimensional (2D) histograms modification is proposed. Firstly, by combining every two nonzero alternating current (AC) coefficients of adjacent DCT blocks in each band as a pair, a new coefficients pairing strategy is proposed for a sharper 2D histogram. Then, with a classification process, multiple 1D and 2D histograms are generated, in which the 2D histograms are selected for data embedding and the bins in 1D histograms are shifted for reversibility. Finally, to further enhance the embedding performance, the 2D mappings are adaptively determined for different 2D histograms through a formulated rate-distortion model. Experimental results demonstrate the superiority of the proposed scheme both in visual quality and file size preservation.

Most current reversible data hiding (RDH) techniques are designed for uncompressed images. However, JPEG images are more commonly used in our daily lives. Up to now, several RDH methods for JPEG images have been proposed, yet few of them investigated the adaptive data embedding as the lack of accurate measurement for the embedding distortion. To realize adaptive embedding and optimize the embedding performance, in this article, a novel RDH scheme for JPEG images based on multiple histogram modification (MHM) and rate-distortion optimization is proposed. Firstly, with selected coefficients, the RDH for JPEG images is generalized into a MHM embedding framework. Then, by estimating the embedding distortion, the rate-distortion model is formulated, so that the expansion bins can be adaptively determined for different histograms and images. Finally, to optimize the embedding performance in real time, a greedy algorithm with low computation complexity is proposed to derive the nearly optimal embedding efficiently. Experiments show that the proposed method can yield better embedding performance compared with state-of-the-art methods in terms of both visual quality and file size preservation.