Content-aware detection of JPEG grid inconsistencies for intuitive image forensics

The paper proposes a novel method for detecting indicators of image forgery by locating grid alignment abnormalities in JPEG compressed image bitmaps. The method evaluates multiple grid positions with respect to a fitting function, and areas of lower contribution are identified as grid discontinuities and possibly tampered areas. An image segmentation step is introduced to differentiate between discontinuities produced by tampering and those that are attributed to image content, making the output maps easier to interpret by suppressing non-relevant activations. Our evaluations, on both synthetically produced datasets and real world tampering cases against seven methods from the literature, highlight the effectiveness of the proposed method in its ability to produce output maps that are clear and readable, and which can achieve successful detections on cases where other algorithms fail.