  Recently, a surge in scientific publications suspected of image manipulation
has led to numerous retractions, bringing the issue of image integrity into
sharp focus. Although research on forensic detectors for image plagiarism and
image synthesis exists, the detection of image splicing traces in scientific
publications remains unexplored. Compared to image duplication and synthesis,
image splicing detection is more challenging due to the lack of reference
images and the typically small tampered areas. Furthermore, disruptive factors
in scientific images, such as artifacts from digital compression, abnormal
patterns, and noise from physical operations, present misleading features like
splicing traces, significantly increasing the difficulty of this task.
Moreover, the scarcity of high-quality datasets of spliced scientific images
limits potential advancements. In this work, we propose an Uncertainty-guided
Refinement Network (URN) to mitigate the impact of these disruptive factors.
Our URN can explicitly suppress the propagation of unreliable information flow
caused by disruptive factors between regions, thus obtaining robust splicing
features. Additionally, the URN is designed to concentrate improvements in
uncertain prediction areas during the decoding phase. We also construct a
dataset for image splicing detection (SciSp) containing 1,290 spliced images.
Compared to existing datasets, SciSp includes the largest number of spliced
images and the most diverse sources. Comprehensive experiments conducted on
three benchmark datasets demonstrate the superiority of our approach. We also
validate the URN's generalisability in resisting cross-dataset domain shifts
and its robustness against various post-processing techniques, including
advanced deep-learning-based inpainting.
