Recently, a surge in biomedical academic publications suspected of image
manipulation has led to numerous retractions, turning biomedical image
forensics into a research hotspot. While manipulation detectors are concerning,
the specific detection of splicing traces in biomedical images remains
underexplored. The disruptive factors within biomedical images, such as
artifacts, abnormal patterns, and noises, show misleading features like the
splicing traces, greatly increasing the challenge for this task. Moreover, the
scarcity of high-quality spliced biomedical images also limits potential
advancements in this field. In this work, we propose an Uncertainty-guided
Refinement Network (URN) to mitigate the effects of these disruptive factors.
Our URN can explicitly suppress the propagation of unreliable information flow
caused by disruptive factors among regions, thereby obtaining robust features.
Moreover, URN enables a concentration on the refinement of uncertainly
predicted regions during the decoding phase. Besides, we construct a dataset
for Biomedical image Splicing (BioSp) detection, which consists of 1,290
spliced images. Compared with existing datasets, BioSp comprises the largest
number of spliced images and the most diverse sources. Comprehensive
experiments on three benchmark datasets demonstrate the superiority of the
proposed method. Meanwhile, we verify the generalizability of URN when against
cross-dataset domain shifts and its robustness to resist post-processing
approaches. Our BioSp dataset will be released upon acceptance