Fast and Blind Detection of Rate-Distortion-Preserving Video Watermarks

Mareen, Hannes, Van Walledael, Glenn, Lambert, Peter and Khelifi, Fouad (2022) Fast and Blind Detection of Rate-Distortion-Preserving Video Watermarks. In: Proceedings of 15th International Workshop on Digital Forensics (WSDF) 2022. ACM, New York, NY, United States. (In Press)

Fast and Blind Detection of Rate-Distortion-Preserving Video Watermarks Latest.pdf - Accepted Version

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Forensic watermarking enables the tracing of digital pirates that leak copyright-protected multimedia. To prevent a negative impact on the video quality or bit rate, rate-distortion-preserving watermarking exists, which represents a watermark as compression artifacts. However, this method has two main disadvantages; the detection has a high complexity and it is non-blind. Although a method based on perceptual hashing exists that speeds up the detection of a fallback watermarking system, it decreases its robustness. Therefore, this paper proposes a novel fast detection method that has less impact on the robustness than related work. Our method optimized NS-DCT-DST hashes for rate-distortion-preserving watermarking, which are more robust to content-preserving attacks. Moreover, a blind version is proposed which does not require the original video for hash extraction. As such, the detection is up to 5700 times faster, at the cost of a modest decrease in robustness. In fact, the proposed method shows good robustness to content preserving recompression attacks when using hashes that are as small as 432 bytes. This is much smaller than comparable performance of related work. In conclusion, this paper enables fast adversary tracing using watermarks that do not impact the video’s compression efficiency.

Item Type: Book Section
Additional Information: The 15th International Workshop on Digital Forensics<br/>, WSDF 2022 ; Conference date: 23-08-2022 Through 26-08-2022
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 21 Jun 2022 08:36
Last Modified: 21 Jun 2022 08:45

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