Lightweight and High-Precision Network for Image Copy-Move Forgery Detection

The existing deep learning based copy-move forgery detection (DL-CMFD) networks focus on providing impressive detection accuracy for tampered regions of different sizes, but usually result in high computation cost and a large number of parameters. The focus of this letter is to propose LHCM-Net, a lightweight, high-precision DL-CMFD network, by integrating a low-cost self-correlation calculation (SCC) module (LCSCC), a gated feature fusion module (GFFM) and residual U-blocks (RSU) equipped with FasterNet blocks (FRSU). Considering that SCC, which calculates the similarity between every two pixels, inevitably leads to high computation cost, existing DL-CMFD networks have to carry out SCC only on low-resolution feature maps to reduce the computation cost. To make high-resolution features available for SCC without obviously introducing high computation cost, this letter proposes LCSCC to calculate the similarity between pixels with a certain distance. GFFM is presented to fuse feature maps of different spatial resolutions by adaptively adjusting their weights based on their respective characteristics, thereby fully integrating high-resolution and low-resolution features for subsequent LCSCC and obviously enhancing the detection accuracy. The FRSU allows LHCM-Net to keep the number of parameters (NP) and computation cost low by combining lightweight FasterNet blocks. The experimental results also demonstrate that LHCM-Net outperforms several existing DL-CMFD networks on three publicly available datasets in terms of detection accuracy, NP and computation cost.