Background: 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.
Methods: 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.
Results: With an average AUC score of 0.927 and an average F1 score of 0.770, it is shown that our method can detect various types of image manipulation in different scenarios robustly. It outperforms other existing general-purpose image tampering detection schemes.
Conclusions: The experiment results shows that our method is capable of detecting manipulations in scientific images in a more reliable manner. 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.
Keywords: Scientific images; Digital image forensics; Noise inconsistency; Scientific image manipulation dataset