Detection of seam carving-based video retargeting using forensics hash

Seam carving is a content-aware multimedia retargeting technique to adaptively resize multimedia data for different display sizes. However, it can also be used to remove objects from digital object or video for malicious purposes. In this paper, a forensics hash-based tampering detection and localization approach is proposed for seam carving-based video retargeting. It extracts the invariant Speeded-up Robust Feature points from every spatiotemporal image to represent the matching surface, and the relative position change of the neighboring matching surface is used to build the forensic hash in a compact and scalable way. Experimental results show that the proposed forensics approach can effectively estimate the exact amount and rough locations of deleted seam carving surfaces. It achieves desirable detection performance even when there are frames deleted. If the hash length is reasonably increased, it can estimate the rough location and exact amount of deleted frames. Moreover, the built forensics hash is of good robustness, scalability, and compactness. Copyright (c) 2014 John Wiley & Sons, Ltd.