Summary of Attack Anything: Blind Dnns Via Universal Background Adversarial Attack, by Jiawei Lian et al.
Attack Anything: Blind DNNs via Universal Background Adversarial Attack
by Jiawei Lian, Shaohui Mei, Xiaofei Wang, Yi Wang, Lefan Wang, Yingjie Lu, Mingyang Ma, Lap-Pui Chau
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 A deep neural network (DNN) adversarial attack framework is proposed to attack any object, model, or task, leveraging iterative optimization and ensemble strategies. The background adversarial attack generalizes well across diverse objects, models, and tasks, exploiting the discrepancy between human and machine vision on the value of background variations. This research reevaluates the robustness and reliability of DNNs, highlighting their vulnerability to attacks. The proposed method is demonstrated through comprehensive experiments in digital and physical domains, showcasing its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a superpower that can trick machines into making wrong decisions. This power is called an “adversarial attack”. Researchers have been trying to develop ways to create these attacks. But they usually focus on specific objects or images. Our team took it to the next level by creating a way to make attacks on anything, without affecting the target itself. We showed that this method can work across different objects, models, and tasks, making machines more vulnerable than we thought. This changes how we think about machine learning and what makes it reliable. |
Keywords
» Artificial intelligence » Machine learning » Neural network » Optimization
