Summary of Log-normal Mutations and Their Use in Detecting Surreptitious Fake Images, by Ismail Labiad et al.
Log-normal Mutations and their Use in Detecting Surreptitious Fake Images
by Ismail Labiad, Thomas Bäck, Pierre Fernandez, Laurent Najman, Tom Sander, Furong Ye, Mariia Zameshina, Olivier Teytaud
First submitted to arxiv on: 23 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 explores alternative methods for generating adversarial attacks on automatic image classifiers. Unlike traditional approaches that rely on customized algorithms, this research leverages generic optimization tools to create more effective and undetectable attacks. The study focuses on the log-normal algorithm as a promising black-box attack strategy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Adversarial attacks are sneaky ways to trick artificial intelligence into making mistakes. Right now, these attacks usually use special formulas designed just for image classification machines. These formulas work well because they follow a certain pattern at first. But, that pattern makes them easy to detect! This paper looks at new types of attacks that don’t rely on those special formulas. Instead, it uses general-purpose tools that can be used in many different situations. |
Keywords
» Artificial intelligence » Image classification » Optimization
