Copy-Move Detection in Optical Microscopy: A Segmentation Network and a Dataset

• Published in: IEEE signal processing letters Vol. 32; pp. 1106 - 1110
• Main Authors: Shao, Hao-Chiang, Liao, Yuan-Rong, Tseng, Tse-Yu, Chuo, Yen-Liang, Lin, Fong-Yi
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: attention; co-saliency; Copy-move forgery detection; Correlation; Datasets; Decoding; Encoders-Decoders; Feature extraction; Forgery; Fraud; Image segmentation; Microscopy; multiresolution; Object recognition; Optical imaging; Optical microscopy; optical microscopy images; Tensors
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2025.3547273

With increasing revelations of academic fraud, detecting forged experimental images in the biomedical field has become a public concern. The challenge lies in the fact that copy-move targets can include background tissue, small foreground objects, or both, which may be out of the training domain and subject to unseen attacks, rendering standard object-detection-based approaches less effective. To address this, we reformulate the problem of detecting biomedical copy-move forgery regions as an intra-image co-saliency detection task and propose CMSeg-Net, a copy-move forgery segmentation network capable of identifying unseen duplicated areas. Built on a multi-resolution encoder-decoder architecture, CMSeg-Net incorporates self-correlation and correlation-assisted spatial-attention modules to detect intra-image regional similarities within feature tensors at each observation scale. This design helps distinguish even small copy-move targets in complex microscopic images from other similar objects. Furthermore, we created a copy-move forgery dataset of optical microscopic images, named FakeParaEgg, using open data from the ICIP 2022 Challenge to support CMSeg-Net's development and verify its performance. Extensive experiments demonstrate that our approach outperforms previous state-of-the-art methods on the FakeParaEgg dataset and other open copy-move detection datasets, including CASIA-CMFD , CoMoFoD , and CMF .