Exposing image splicing traces in scientific publications via uncertainty-guided refinement

IF 6.7 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
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引用次数: 0

Abstract

Recently, a surge in image manipulations in scientific publications has led to numerous retractions, highlighting the importance of image integrity. Although forensic detectors for image duplication and synthesis have been researched, the detection of image splicing in scientific publications remains largely unexplored. Splicing detection is more challenging than duplication detection due to the lack of reference images and more difficult than synthesis detection because of the presence of smaller tampered-with areas. Moreover, disruptive factors in scientific images, such as artifacts, abnormal patterns, and noise, present misleading features like splicing traces, rendering this task difficult. In addition, the scarcity of high-quality datasets of spliced scientific images has limited advancements. Therefore, we propose the uncertainty-guided refinement network (URN) to mitigate these disruptive factors. We also construct a dataset for image splicing detection (SciSp) with 1,290 spliced images by collecting and manually splicing. Comprehensive experiments demonstrate the URN’s superior splicing detection performance.

通过不确定性引导的精炼揭示科学出版物中的图像拼接痕迹
最近,科学出版物中的图像篡改现象激增,导致许多出版物被撤回,这凸显了图像完整性的重要性。尽管针对图像复制和合成的法证检测器已经得到研究,但科学出版物中的图像拼接检测在很大程度上仍未得到探索。由于缺乏参考图像,拼接检测比复制检测更具挑战性;由于存在较小的篡改区域,拼接检测比合成检测更加困难。此外,科学图像中的干扰因素,如人工痕迹、异常模式和噪声,会呈现出拼接痕迹等误导性特征,从而使这项任务变得困难。此外,高质量拼接科学图像数据集的稀缺也限制了研究的进展。因此,我们提出了不确定性引导细化网络(URN)来减少这些干扰因素。我们还通过收集和手动拼接的方式,构建了一个包含 1,290 幅拼接图像的图像拼接检测数据集(SciSp)。综合实验证明了 URN 的卓越拼接检测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Patterns
Patterns Decision Sciences-Decision Sciences (all)
CiteScore
10.60
自引率
4.60%
发文量
153
审稿时长
19 weeks
期刊介绍:
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