Summary of Trojflow: Flow Models Are Natural Targets For Trojan Attacks, by Zhengyang Qi et al.
TrojFlow: Flow Models are Natural Targets for Trojan Attacks
by Zhengyang Qi, Xiaohua Xu
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 Faced with the challenge of developing a reliable and efficient generative model, researchers have turned to flow-based models (FMs). As a variant of diffusion models (DMs), FMs possess an inherent ability to map noise to data. Their training and sampling process is particularly noteworthy for its efficiency and applicability across various fields. However, despite their advantages, FMs are not immune to attacks. In fact, Trojan/Backdoor attacks have been shown to be a significant threat to DMs, allowing malicious patterns to be embedded at the input level. This study demonstrates that these attacks can also compromise FMs, leveraging their unique ability to fit arbitrary distributions. By exploring the vulnerabilities of FMs through Trojan attacks, this paper proposes TrojFlow, an innovative method for attacking FMs. The authors examine various attack settings and combinations, as well as existing defense methods for DMs, to determine their effectiveness against these proposed scenarios. Evaluations on CIFAR-10 and CelebA datasets reveal that TrojFlow can successfully compromise FMs with high utility and specificity, even bypassing existing defenses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Faced with the challenge of developing a reliable and efficient generative model, researchers have turned to flow-based models (FMs). As a variant of diffusion models (DMs), FMs possess an inherent ability to map noise to data. Their training and sampling process is particularly noteworthy for its efficiency and applicability across various fields. However, despite their advantages, FMs are not immune to attacks. In fact, Trojan/Backdoor attacks have been shown to be a significant threat to DMs, allowing malicious patterns to be embedded at the input level. This study demonstrates that these attacks can also compromise FMs, leveraging their unique ability to fit arbitrary distributions. |
Keywords
» Artificial intelligence » Generative model
