Secure Video Quality Assessment Resisting Adversarial Attacks
Ao-Xiang Zhang, Yuan-Gen Wang, Yu Ran, Weixuan Tang, Qingxiao Guan, and Chunsheng Yang

TL;DR
This paper introduces SecureVQA, a novel video quality assessment model designed to resist adversarial attacks by employing spatial and temporal defense mechanisms, achieving high security without sacrificing assessment accuracy.
Contribution
The paper proposes a security-oriented VQA framework with innovative defense strategies, including spatial grid sampling and pixel-wise randomization, to enhance robustness against adversarial attacks.
Findings
SecureVQA outperforms existing models in security benchmarks.
The proposed defenses effectively neutralize adversarial perturbations.
SecureVQA maintains competitive accuracy in video quality assessment.
Abstract
The exponential surge in video traffic has intensified the imperative for Video Quality Assessment (VQA). Leveraging cutting-edge architectures, current VQA models have achieved human-comparable accuracy. However, recent studies have revealed the vulnerability of existing VQA models against adversarial attacks. To establish a reliable and practical assessment system, a secure VQA model capable of resisting such malicious attacks is urgently demanded. Unfortunately, no attempt has been made to explore this issue. This paper first attempts to investigate general adversarial defense principles, aiming at endowing existing VQA models with security. Specifically, we first introduce random spatial grid sampling on the video frame for intra-frame defense. Then, we design pixel-wise randomization through a guardian map, globally neutralizing adversarial perturbations. Meanwhile, we extract…
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Taxonomy
TopicsDigital Media Forensic Detection · Advanced Steganography and Watermarking Techniques · Advanced Image Processing Techniques
