Summary of Defending Deep Regression Models Against Backdoor Attacks, by Lingyu Du et al.
Defending Deep Regression Models against Backdoor Attacks
by Lingyu Du, Yupei Liu, Jinyuan Jia, Guohao Lan
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 In this paper, the authors propose a novel defense mechanism called DRMGuard to detect backdoor attacks in deep regression models. These models are commonly used in safety-critical applications but are vulnerable to malicious attacks that manipulate their predictions. The existing defenses for classification models are ineffective against regression models because of their continuous output values and activation patterns. DRMGuard tackles this problem by formulating an optimization problem based on the unique characteristics of backdoored deep regression models. The authors conduct extensive evaluations on two regression tasks and four datasets, showing that DRMGuard can consistently defend against various backdoor attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep learning models are used in many important jobs, like self-driving cars and medical diagnosis. But these models can be tricked by bad guys to make wrong predictions. This is called a “backdoor” attack. Most defenses for image recognition models don’t work well on regression models because they have different output values and patterns. The authors of this paper came up with a new way to detect backdoors in deep regression models, which they call DRMGuard. They tested it on many different tasks and datasets, and it worked really well. |
Keywords
* Artificial intelligence * Classification * Deep learning * Optimization * Regression
