A Robust Adversarial Example Attack Based on Video Augmentation

Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems. Video adversarial attacks add subtle noise to the original example, resulting in a false classification result. Thorough...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 3; p. 1914
Main Authors Yin, Mingyong, Xu, Yixiao, Hu, Teng, Liu, Xiaolei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.02.2023
Subjects
adversarial example
Augmentation
Classification
Classification systems
Deep learning
Defense
Efficiency
Methods
Noise
Success
video augmentation
video classification
Video data
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Summary:Despite the success of learning-based systems, recent studies have highlighted video adversarial examples as a ubiquitous threat to state-of-the-art video classification systems. Video adversarial attacks add subtle noise to the original example, resulting in a false classification result. Thorough studies on how to generate video adversarial examples are essential to prevent potential attacks. Despite much research on this, existing research works on the robustness of video adversarial examples are still limited. To generate highly robust video adversarial examples, we propose a video-augmentation-based adversarial attack (v3a), focusing on the video transformations to reinforce the attack. Further, we investigate different transformations as parts of the loss function to make the video adversarial examples more robust. The experiment results show that our proposed method outperforms other adversarial attacks in terms of robustness. We hope that our study encourages a deeper understanding of adversarial robustness in video classification systems with video augmentation.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13031914