Summary of Revisiting Min-max Optimization Problem in Adversarial Training, by Sina Hajer Ahmadi et al.
Revisiting Min-Max Optimization Problem in Adversarial Training
by Sina Hajer Ahmadi, Hassan Bahrami
First submitted to arxiv on: 20 Aug 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 A novel approach is introduced to construct robust deep neural networks resistant to adversarial attacks by reformulating the saddle point optimization problem. This method offers significant resistance and a concrete security guarantee against multiple adversaries, providing a rebuttal to the issue of convolutional neural networks being susceptible to adversarial examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to make computer vision models more secure is presented. These models are often used in real-world applications, but they can be tricked into making mistakes by adding small imperfections to images. The proposed method makes these models more resilient to these kinds of attacks, providing a guarantee of security against multiple types of attackers. |
Keywords
» Artificial intelligence » Optimization
