A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns

Abstract

Copy-Move Forgery Detection (CMFD) methods aim to forensically analyze a digital image for a possible content duplication manipulation. In the past, many block-based algorithms have been proposed for detection and localization of CMF. However, the existing solutions show limited efficacy for images compressed in JPEG and lack robustness against post-processing attacks such as noise addition, blurring, etc. To address this problem, we propose a new block-based passive method for detection and localization of CMF in this paper. Passive methods, as opposed to active methods, are used to authenticate the image content in the absence of any pre-embedded information such as watermarks. In our proposed scheme, a suspicious input image to be analyzed is first low pass filtered and converted to Local Binary Patterns (LBP) image. The LBP texture image is then divided into overlapping blocks. Next, a compact five-dimensional feature vector is extracted from each block by employing thresholding and Cellular Automata. The set of feature vectors is sorted lexicographically to bring the copy-pasted blocks nearer to each other. Finally, the feature matching step is used to reveal the duplicate blocks. Our experimental results indicate that the proposed method performs exceptionally well relative to other state-of-art-methods, under different image manipulation scenarios.