A Detection Method of Temporally Operated Videos Using Robust Hashing
This addresses the challenge for SNS providers and forensic analysts in identifying tampered videos despite common operations like recompression and resizing, though it appears incremental as it builds on robust hashing techniques.
The paper tackles the problem of detecting temporally operated videos, such as frame insertion or permutation, which are difficult to detect with conventional methods, and proposes a robust hashing algorithm that works even under resizing and compression, achieving detection in such scenarios.
SNS providers are known to carry out the recompression and resizing of uploaded videos/images, but most conventional methods for detecting tampered videos/images are not robust enough against such operations. In addition, videos are temporally operated such as the insertion of new frames and the permutation of frames, of which operations are difficult to be detected by using conventional methods. Accordingly, in this paper, we propose a novel method with a robust hashing algorithm for detecting temporally operated videos even when applying resizing and compression to the videos.