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Detecting and Segmenting Adversarial Graphics Patterns from Images

arXiv 2108.09383 ↗First posted 20 Aug 2021cs.CV cs.MMcited by 0

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Abstract (arXiv)

Adversarial attacks pose a substantial threat to computer vision system security, but the social media industry constantly faces another form of "adversarial attack" in which the hackers attempt to upload inappropriate images and fool the automated screening systems by adding artificial graphics patterns. In this paper, we formulate the defense against such attacks as an artificial graphics pattern segmentation problem. We evaluate the efficacy of several segmentation algorithms and, based on observation of their performance, propose a new method tailored to this specific problem. Extensive experiments show that the proposed method outperforms the baselines and has a promising generalization capability, which is the most crucial aspect in segmenting artificial graphics patterns.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 62 references that are in the map. First posted on arXiv 20 Aug 2021.

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31 of 62 references.

References · 31

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19 more
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