X. Q. Wu, Y. Cao, X. F. Zhao, C. J. Liu
This paper proposes a content adaptive method to detect the digital image tampered by Seam-carving. Taking into account the operational characteristics of Seam-carving, in this method, the highly suspectable tampered areas in the image are located first, and then feature extraction and classification are carried out based on those located areas. The extracted features in this method are more representative for the influence of the area with less possibility is eliminated in feature calculation. Based on extended Markov feature, this paper measures the damage degree of pixels correlation caused by Seam-carving. Compared with previous non-adaptive methods, the experiment shows that the proposed method is more effective in targeted detection.
Cite this paper as:
 X. Q. Wu, Y. Cao, X. F. Zhao, C. J. Liu. Detection of Seam-Carving Forgery Based on Adaptive Feature Extraction Method. In Proc. 14th China Information Hiding Workshop (CIHW 2018), Mar.31-Apr.1, 2018, Guangzhou, China.