Detecting Biomedical Copy-Move Forgery by Attention-Based Multiscale Deep Descriptors

Hao-Chiang Shao, Tse-Yu Tseng, Yuan-Rong Liao, Chi-Chun Chen, Chung-Yang Hung, Ming-Hsin Liang

Abstract: Continual revelations of academic fraud have raised concerns regarding the detection of forged experimental images in the public domain. To address this issue, we introduce a multiscale attention-based deep (MAD) descriptor scheme for detecting copy-move image forgery in biomedical research scenarios. Our method utilizes the common object detection network as a backbone and incorporates the positional embedding module, the channel-attention module, and the self-attention module to generate a dense feature field for input images. The proposed method demonstrates robustness against common attacks encountered during research manuscript preparation, low-contrast biomedical images featuring small foreground objects, and unseen or unlearned object patterns. Extensive experiments substantiate that our approach outperforms previous copy-move forgery methods when applied to real-world cases across various domains. Our proposed method can serve as an efficient screening tool for the rapid identification of biomedical image forgeries.

External IDs:dblp:conf/icip/ShaoTLCHL24