Copy-move forgery causes a big challenge to copy-move forgery detection (CMFD) due to that the photometrical characteristics of genuine and tampered regions in the same image remain highly consistent. A novel U-Net-like architecture with multiple asymmetric cross-layer connections associated with self-correlation and atrous spatial pyramid pooling (ASPP) between feature extraction module (FEM) and tampered region localization module (TRLM), called UCM-Net, is proposed in this article. Different from existing deep learning based CMFD networks which indiscriminately process large or small tampered regions without considering the statistical characteristics of regions, FEM differentially treats large or small tampered regions by exploiting deep backbone networks to extract high-level features with rich semantic information for large tampered regions while utilizing lightweight backbone networks to extract low-level features for small tampered regions. Multiple cross-layer connections between two modules utilize the self-correlation calculation and ASPP to remove as much irrelevant semantic information as possible while retaining multi-scale tampered features from shallow to deep convolutional layers of FEM. Unlike the previous CMFD networks, which cannot capture multi-scale features because of simply stacking convolution blocks in the upsampling step, TRLM exploits multiple U-shaped residual U-block modules with different depths to change the receptive field of each point in the tampered feature maps so as to capture global and local information, greatly improving the localization accuracy of tampered regions. Experimental results on three publicly available databases demonstrate that UCM-Net outperforms several state-of-the-art algorithms in terms of various evaluation metrics.

This letter aims to propose an advanced predictor called NU$^{2}$P in the multiple histogram modification (MHM)-based framework by combining deep learning techniques that take the local characteristics of pixels into account. NU$^{2}$P is firstly designed for reversible data hiding by incorporating U$^{2}$P (an improved version of U$^{2}$-Net) equipped with convolutional block attention module (CBAM). The purpose of U$^{2}$P is to integrate the feature maps with different sizes and receptive fields through U-Net-like residual U-blocks (RSUs), which can make full of strong correlations between adjacent pixels while reducing the computational cost by means of the pooling layers of RSUs. CBAM pays different attention to feature maps and elements of feature maps from the perspectives of channel and spatial attention, thereby helping NU$^{2}$P further enhance the prediction performance. In the MHM-based framework, for multiple categories generated using fuzzy C-means with multiple deliberately-designed features, NU$^{2}$P is conductive to constructing a sharp prediction error histogram (PEH) for each category and the improved discrete particle swarm optimization without significantly increasing the computational cost is used to adaptively select the optimal bins for each PEH. The experimental results show that the proposed method significantly outperforms several state-of-the-art RDH methods in terms of image quality and payload.

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.

Reversible data hiding with contrast enhancement (RDH-CE) is proposed to aim at improving the contrast of images while embedding data. After deeply analyzing and studying the RDH-CE method proposed by Jafaret al., it is found that there are three main problems in their method. Firstly, their method ignores the fact that the left-bottom neighbors of a pixel contribute to increasing the accuracy of the local-complexity evaluation. Secondly, Jafaret al.’s method employs K-means clustering in combination with one single feature to split pixels into five classes, leading to a weak clustering performance. Finally, Jafaret al.’s method uniformly embedded 1 bit into each pixel irrespective of the local complexity, and thus, the embedding capacity is limited. To this end, an improved RDH-CE method is proposed in this paper. Considering that the complexity evaluation plays a vital role in both contrast enhancement and payload increase, we improve embedding performance by including left-bottom neighbors of a pixel into complexity evaluation. Compared with one single feature in Jafaret al.’s method, we extract multiple features to assist K-means clustering such that a better cluster performance is obtained. In addition, our method provides an adaptive pixel modification strategy based on the local complexity, in which we can adaptively embed 1 or 2 bits into a pixel according to the corresponding complexity. By these three improvements, our method is capable of achieving high capacity while enhancing contrast. The experimental results also show that our method achieves higher accuracy of the complexity evaluation, larger payload, and better local contrast enhancement than those existing RDH-CE related methods.