Summary of Adversarial Attack Against Images Classification Based on Generative Adversarial Networks, by Yahe Yang
Adversarial Attack Against Images Classification based on Generative Adversarial Networks
by Yahe Yang
First submitted to arxiv on: 21 Dec 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 novel approach to generate adversarial attacks on image classification systems using generative adversarial networks (GANs). Specifically, the authors utilize GANs to create small perturbations in images that can deceive even advanced classifiers. The proposed method aims to improve the anti-attack ability of these systems by generating adversarial samples through an adversarial learning process between the generator and classifier. The effectiveness of this approach is evaluated on a classical image classification dataset, demonstrating its capability to successfully deceive various advanced classifiers while maintaining the naturalness of adversarial samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers called generative adversarial networks (GANs) to create fake images that can trick other computer systems into making mistakes. The researchers want to understand how these GANs work and how they can be used to make image classification systems more secure. They created a new way to use GANs to generate small changes in images that can fool even the best image classifiers. The scientists tested their method on a big dataset of pictures and found that it was very good at tricking the classifiers while still looking like real pictures. |
Keywords
» Artificial intelligence » Image classification
