With the wide application of visual sensors and development of digital image processing technology, image copy-move forgery detection (CMFD) has become more and more prevalent. Copy-move forgery is copying one or several areas of an image and pasting them into another part of the same image, and CMFD is an efficient means to expose this. There are improper uses of forged images in industry, the military, and daily life. In this paper, we present an efficient end-to-end deep learning approach for CMFD, using a span-partial structure and attention mechanism (SPA-Net). The SPA-Net extracts feature roughly using a pre-processing module and finely extracts deep feature maps using the span-partial structure and attention mechanism as a SPA-net feature extractor module. The span-partial structure is designed to reduce the redundant feature information, while the attention mechanism in the span-partial structure has the advantage of focusing on the tamper region and suppressing the original semantic information. To explore the correlation between high-dimension feature points, a deep feature matching module assists SPA-Net to locate the copy-move areas by computing the similarity of the feature map. A feature upsampling module is employed to upsample the features to their original size and produce a copy-move mask. Furthermore, the training strategy of SPA-Net without pretrained weights has a balance between copy-move and semantic features, and then the module can capture more features of copy-move forgery areas and reduce the confusion from semantic objects. In the experiment, we do not use pretrained weights or models from existing networks such as VGG16, which would bring the limitation of the network paying more attention to objects other than copy-move areas.To deal with this problem, we generated a SPANet-CMFD dataset by applying various processes to the benchmark images from SUN and COCO datasets, and we used existing copy-move forgery datasets, CMH, MICC-F220, MICC-F600, GRIP, Coverage, and parts of USCISI-CMFD, together with our generated SPANet-CMFD dataset, as the training set to train our model. In addition, the SPANet-CMFD dataset could play a big part in forgery detection, such as deepfakes. We employed the CASIA and CoMoFoD datasets as testing datasets to verify the performance of our proposed method. The Precision, Recall, and F1 are calculated to evaluate the CMFD results. Comparison results showed that our model achieved a satisfactory performance on both testing datasets and performed better than the existing methods.

The proliferation of modern image editing tools has raised concerns about image manipulation, particularly regarding the potential to mislead the public and compromise privacy and security. Consequently, detecting and localizing tampered regions has become a critical research challenge. Traditional methods struggle with subtle manipulations, such as splicing, copy-move, and removal, which are often more discernible in the frequency domain than in the spatial domain. Additionally, the size imbalance between the tampered and background regions further complicates the detection process. To address these challenges, we propose DFFormer, an end-to-end network that leverages frequency feature differences and a dynamic token strategy for precise manipulation localization. DFFormer combines the Conventional Neural Network (CNN) and Transformer in a hybrid architecture with three key modules: the Adaptive Frequency Transformer (AFT), the Prototype Learning Module (PLM), and the Cascaded Progressive Token Fusion Head (CPTF-Head). AFT integrates high- and low-frequency components into self-attention via the Parallel Adaptive Frequency Attention (PAFA) block, enhancing tampering feature representation while preserving fine details. PLM employs KNN-based density peak clustering (DPC-KNN) and weighted token aggregation to optimize dynamic token reduction. The CPTF-Head adopts a hierarchical coarse-to-fine strategy to integrate multiscale features, thereby improving localization accuracy and edge refinement. Experiments demonstrate that DFFormer outperforms state-of-the-art models across four benchmark datasets and one real-world dataset, exhibiting superior generalization and robustness. The source code is publicly available at https://github.com/XiangGD/DFFormer.git.