An Improved Approach to Exposing JPEG Seam Carving Under Recompression

As a popular method for image and video retargeting, seam carving has been used for image/video forgery manipulation. Although significant advances have been made in detecting seam-carving forgery, there are very few contributions in exposing the forgery from recompressed JPEG images, especially the doctored images that are recompressed at the same or a lower quality. The detection is generally challenging because the recompression after tampering compromises the existing forgery traces. Aiming to address this problem, we propose a hybrid large-feature mining-based approach that contains multiple types of large features. Ensemble learning is used to deal with the high-feature dimensionality. This paper shows that the proposed approach effectively distinguishes the seam-carved JPEG images from untouched JPEG images and improves the detection accuracy. In our proposed multiple types of features, directional derivative-based feature set and Gabor residual-based feature set generally perform the best. This paper also indicates that feature selection may play an important role to greatly reduce the feature number while maintaining a better or comparable detection accuracy.