Exposing Inpainting Forgery in JPEG Images under Recompression Attacks

Inpainting, originally designed in computer vision to reconstruct lost or deteriorated parts of images and videos, has been used for image tampering, including region filling and object removal to alter the truth. While several types of tampering including copy-move and seam carving forgery can now be successfully exposed in image forensics, there has been very little study to tackle inpainting forgery in JPEG images, the detection of which is extremely challenging due to the post-recompression attacks performed to cover or compromise original inpainting traces. To date, there is no effective way to detect inpainting image forgery under combined recompression attacks. To fill such a gap in image forensics and reveal inpainting forgery from the post-recompression attacks in JPEG images, we propose in this paper an approach that begins with large feature mining in discrete transform domain, ensemble learning is then applied to deal with the high feature dimensionality and to prevent the overfitting that generally happens to some regular classifiers under high feature dimensions. Our study shows the proposed approach effectively exposes inpainting forgery under post recompression attacks; especially, it noticeably improves the detection accuracy while the recompression quality is lower than the original JPEG image quality, and thus bridges a gap in image forgery detection.