  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.
