The cover source mismatch (CSM) can be very challenging for steganalysis because different distribution between source and target inevitably leads to poor performance of the steganalyzer on the target domain. In general, some methods from unsupervised domain adaptation, such as contrastive domain discrepancy (CDD), can be directly applied to steganalysis for addressing the CSM problem, but they cannot achieve satisfactory detection accuracy due to the neglect of steganographic characteristics. To solve this problem, reliable steganalysis labeling (RSL)-based CDD (RCDD) taking steganographic characteristics in account is proposed in this paper, which relies on RSL to generate reliable labels for extended target images, rather than utilizing clustering in CDD to obtain unreliable pseudo labels for target images. Through detailed deduction process, we know that RCDD draws closer the distribution of source and target classwisely so as to enhance the classification performance on the target domain. Simultaneously, a corresponding steganalysis network RCDD-Net is yielded by incorporating some backbone into RCDD. A large number of experiments verify that RCDD-Net is an innovation in steganalysis that effectively alleviates performance degradation when CSM occurs. Moreover, RCDD-Net provides better detection performance than several advanced steganalysis networks.

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 letter, a lightweight and effective deep steganalysis network (DSN) with less than 400,000 parameters, called LWENet, is proposed, which focuses on increasing the performance as well as significantly reducing the number of parameters (NP) from three perspectives. Firstly, in the preprocessing part, several lightweight bottleneck residual blocks are combined into the spatial rich model filters to improve the signal-to-noise ratio of stego signals while slightly increasing NP, thereby improving the subsequent performance. Secondly, a depthwise separable convolution layer is exploited at the end of the feature extraction part to largely reduce NP and increase the performance by capturing salient correlations while ignoring trivial ones among feature maps. Finally, to keep LWENet lightweight, we have to select only one fully connected (FC) layer. Simultaneously, multi-view global pooling is employed prior to the FC layer to yield multi-view features and further improve the detection performance. Extensive experiments demonstrate that our network achieves better performance than several state-of-the-art DSNs.