Summary of Seeing Through the Mask: Rethinking Adversarial Examples For Captchas, by Yahya Jabary et al.
Seeing Through the Mask: Rethinking Adversarial Examples for CAPTCHAs
by Yahya Jabary, Andreas Plesner, Turlan Kuzhagaliyev, Roger Wattenhofer
First submitted to arxiv on: 9 Sep 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 challenges modern CAPTCHAs that rely on vision tasks. Recent advances in image recognition models pose a threat to these CAPTCHAs, making them vulnerable to noise-based attacks or object hiding. To address this issue, the authors propose a new approach that adds masks of varying intensities to images while preserving semantic information and human solvability. The results show that this method can significantly reduce the accuracy of state-of-the-art models by over 50% points, with robust models like vision transformers seeing an 80% point drop in Accuracy @ 1 (Acc@1). This work highlights the importance of developing more robust CAPTCHAs to counter emerging threats. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computer programs that are hard for computers but easy for humans. Computers can recognize images really well, which makes it hard for them to distinguish between real and fake pictures. The authors came up with a new way to make pictures harder for computers by adding “masks” of different kinds. This makes it much harder for even the best computer vision models to understand what’s in the picture. The results show that this method can reduce the accuracy of these models by a lot, making CAPTCHAs more secure. |
