Summary of Cross-task Attack: a Self-supervision Generative Framework Based on Attention Shift, by Qingyuan Zeng et al.
Cross-Task Attack: A Self-Supervision Generative Framework Based on Attention Shift
by Qingyuan Zeng, Yunpeng Gong, Min Jiang
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a self-supervised framework, Cross-Task Attack (CTA), designed to create realistic and challenging adversarial perturbations that can be used to evaluate the robustness of artificial intelligence systems. The existing methods focus on single-task or multi-task scenarios, neglecting the fact that AI systems often perform multiple tasks simultaneously. CTA utilizes co-attention and anti-attention maps to generate cross-task adversarial perturbations, which are then tested on various vision tasks. The experimental results demonstrate the effectiveness of this approach in creating robust attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI researchers want to make sure their artificial intelligence (AI) systems are strong enough to handle tricky situations. Right now, most AI attack methods focus on just one thing or a few things that an AI can do. But real-life AI does many different tasks at the same time! This paper creates a new way to attack AI, called Cross-Task Attack (CTA). CTA uses special maps to figure out where different AI models look at pictures and where they don’t pay attention. Then, it changes those areas to make the AI system behave in unexpected ways. The results show that this method is very good at creating realistic attacks. |
Keywords
» Artificial intelligence » Attention » Multi task » Self supervised
