TY - JOUR
AU - Lin, Fong-Yi
AB - Abstract: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. The FakeParaEgg dataset, our source code, and the CMF dataset with our manually defined segmentation ground truths available at ``this https URL.
TI - Copy-Move Detection in Optical Microscopy: A Segmentation Network and A Dataset
JF - Electrical Engineering and Systems Science
DO - 10.48550/arxiv.2412.10258
DA - 2024-12-13
UR - https://www.deepdyve.com/lp/arxiv-cornell-university/copy-move-detection-in-optical-microscopy-a-segmentation-network-and-a-G0SG8i8uB2
VL - 2024
IS - 2412
DP - DeepDyve
ER -