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 ``https://github.com/YoursEver/FakeParaEgg''.

翻译：随着学术不端行为的日益曝光，生物医学领域实验图像的伪造检测已成为公众关注的焦点。该任务的挑战在于复制-移动目标可能包含背景组织、小型前景对象或两者兼具，这些目标可能超出训练数据分布范围并遭受未知攻击，导致基于标准对象检测的方法效果有限。为此，我们将生物医学复制-移动伪造区域检测问题重新定义为图像内协同显著性检测任务，并提出CMSeg-Net——一种能够识别未知重复区域的复制-移动伪造分割网络。该网络基于多分辨率编码器-解码器架构，通过自相关模块与相关性辅助的空间注意力模块，在每一观测尺度的特征张量中检测图像内部区域相似性。这种设计有助于在复杂显微图像中，将微小复制-移动目标与其他相似对象进行区分。此外，我们利用ICIP 2022挑战赛的公开数据，创建了名为FakeParaEgg的光学显微图像复制-移动伪造数据集，以支持CMSeg-Net的开发并验证其性能。大量实验表明，我们的方法在FakeParaEgg数据集及其他公开复制-移动检测数据集（包括CASIA-CMFD、CoMoFoD和CMF）上均优于现有先进方法。FakeParaEgg数据集、源代码及我们手动标注分割真值的CMF数据集可通过``https://github.com/YoursEver/FakeParaEgg''获取。