LAUSR

Image forgeries created using easy-to-use image editing tools like Adobe Photoshop have become prime sources of fake news, and are often used in malevolent ways in politics, courtrooms, and in scientific publishing as well. To reduce the problems caused by manipulated images in these domains, it is important to have reliable and fast mechanisms in place for verifying their authenticity before their consumption. Automated detection of image forgeries, is, therefore an ongoing challenge for the research community. One of a commonly used image forgeries is Copy-Move Forgery (CMF), where a region of an image is copied and pasted to some other location within the same image to alter its contents. To detect copy-move forgeries, several Copy Move Forgery Detection (CMFD) techniques have been proposed in the past. However, existing techniques are highly sensitive to post-processing attacks such as noise and compression. In this paper, we propose a new CMFD method in which Cellular Automata (CA) is used for feature extraction. The method works by dividing the input image into overlapping blocks and extracting features from these blocks using the CA inversion procedure. Then, the repeated regions are identified by matching the set of extracted feature vectors. The experimental results show that the detection under noise and compression attacks is accurate compared to many existing methods. As such, the proposed approach may be useful for a variety of CMFD scenarios and may have implications for researchers in related fields.