论文标题
基于噪声不一致的科学图像篡改检测：一种方法和数据集
Scientific Image Tampering Detection Based On Noise Inconsistencies: A Method And Datasets
论文作者
论文摘要
篡改科学形象是一个问题，不仅影响作者，而且影响研究界的一般看法。尽管以前的研究人员已经开发了识别自然图像中篡改的方法，但由于科学图像具有不同的统计，格式，质量和意图，因此这些方法可能不会在科学环境下蓬勃发展。因此，我们提出了一种基于噪声不一致的科学图像特定的篡改检测方法，该方法能够学习和推广到不同科学领域。我们将方法训练并测试我们的方法，这些方法是由操纵的Western印迹和显微镜图像的新数据集，旨在模拟科学中有问题的图像。测试结果表明，我们的方法可以在不同的情况下检测到各种类型的图像操纵，并且表现优于现有的通用图像篡改检测方案。我们讨论了这两种类型的图像以外的应用程序，并提出了将有问题图像检测到同行评审和科学的系统步骤的下一步。
Scientific image tampering is a problem that affects not only authors but also the general perception of the research community. Although previous researchers have developed methods to identify tampering in natural images, these methods may not thrive under the scientific setting as scientific images have different statistics, format, quality, and intentions. Therefore, we propose a scientific-image specific tampering detection method based on noise inconsistencies, which is capable of learning and generalizing to different fields of science. We train and test our method on a new dataset of manipulated western blot and microscopy imagery, which aims at emulating problematic images in science. The test results show that our method can detect various types of image manipulation in different scenarios robustly, and it outperforms existing general-purpose image tampering detection schemes. We discuss applications beyond these two types of images and suggest next steps for making detection of problematic images a systematic step in peer review and science in general.
