Detecting and Localizing Copy-Move and Image-Splicing Forgery
This addresses the issue of image tampering in contexts like fake news and legal evidence, but it appears incremental as it compares existing methods without introducing a new paradigm.
The paper tackled the problem of detecting and localizing copy-move and image-splicing forgeries in images, comparing deep learning and image transformation methods to evaluate their performance and robustness, and provided suggestions for a more robust framework.
In the world of fake news and deepfakes, there have been an alarmingly large number of cases of images being tampered with and published in newspapers, used in court, and posted on social media for defamation purposes. Detecting these tampered images is an important task and one we try to tackle. In this paper, we focus on the methods to detect if an image has been tampered with using both Deep Learning and Image transformation methods and comparing the performances and robustness of each method. We then attempt to identify the tampered area of the image and predict the corresponding mask. Based on the results, suggestions and approaches are provided to achieve a more robust framework to detect and identify the forgeries.