Manipulation of biomedical images to misrepresent experimental results has plagued the biomedical community for a while. Recent interest in the problem led to the curation of a dataset and associated tasks to promote the development of biomedical forensic methods. Of these, the largest manipulation detection task focuses on the detection of duplicated regions between images. Traditional computer-vision based forensic models trained on natural images are not designed to overcome the challenges presented by biomedical images. We propose a multi-scale overlap detection model to detect duplicated image regions. Our model is structured to find duplication hierarchically, so as to reduce the number of patch operations. It achieves state-of-the-art performance overall and on multiple biomedical image categories.
翻译:将生物医学图像用于歪曲实验结果的操作已经困扰生物医学界一段时间了。最近对该问题的兴趣导致一个数据集的整理和相关的促进生物医学法医学方法发展的任务,其中最大的操纵检测任务侧重于探测图像之间的重复区域;以自然图像为根据培训的传统计算机观点法医学模型的设计不是为了克服生物医学图像带来的挑战;我们提出了一个多尺度重叠检测模型,以探测重复的图像区域。我们的模型的结构是按等级排列的,以便找出重复之处,从而减少补丁操作的数量。它实现了最先进的总体性能和多种生物医学图像类别。