Summary of Transform-dependent Adversarial Attacks, by Yaoteng Tan et al.
Transform-Dependent Adversarial Attacks
by Yaoteng Tan, Zikui Cai, M. Salman Asif
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 The paper proposes a new type of adversarial attack that exploits the dependence of perturbations on image transforms, showcasing metamorphic properties that enable diverse adversarial effects as a function of transformation parameters. The transform-dependent attacks are demonstrated to be effective across various architectures (CNN and transformer), vision tasks (image classification and object detection), and a wide range of image transforms. Additionally, the authors show that these perturbations can serve as a defense mechanism against sensitive information disclosure when image enhancement transforms pose a risk of revealing private content. The attacks achieve high targeted attack success rates, outperforming state-of-the-art transfer attacks by 17-31% in blackbox scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers see things that aren’t really there. It’s like showing a picture to a computer and then changing the picture just enough so the computer thinks something else is there. The researchers found out that computers are vulnerable to these changes, no matter what kind of computer or task they’re doing. They even showed that this vulnerability can be used to keep private information secret by making small changes to an image. This new way of attacking computers could make it harder for people to trust the things computers say are true. |
Keywords
» Artificial intelligence » Cnn » Image classification » Object detection » Transformer
