Copy-move forgery detection (CMFD) is a technique tailored to detect the existence of copy-move regions in a query image. In this paper, a dual-view CMFD network named DV-Net is proposed, which integrates the combination of the similarity information and tampered features from shallow features conducive to copy-move region localization by using dual-view self-correlation calculation (DV-SCC) and the shallow similarity attention module (SSAM), and strengthens the ability of distinguishing source/target regions by making deep features pass through three serial multiple serial adaptive receptive field selection modules (ARFSMs). The SCC plays an irreplaceable role in identifying copy-move regions. However, single-view SCC, such as the cosine similarity or the Euclidean distance, can solely capture the similarly information from a single perspective. DV-SCC, a combination of Euclidean distance and cosine similarity, provides more comprehensive similarity information from numerical and directional perspectives. In addition, different from previous CMFD networks that only utilize the similarity information to locate similar regions while neglecting tampered features contained in the shallow features, which are of vital importance to CMFD, we innovatively convert the similarity information into the SSAM and apply SSAM on the shallow features to emphasize the similarity information while preserving tampered features, significantly enhancing the localization accuracy of source/target regions. Multiple serial ARFSMs, each containing two parallel branches controlled by a soft attention, can adaptively select appropriate receptive fields according to the scales of tampered regions, improving the classification accuracy of source/target regions. The experimental results show that DV-Net outperforms several advanced algorithms in source/target region localization and discrimination on three publicly available datasets.

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.