Summary of A General Approach to Enhance the Survivability Of Backdoor Attacks by Decision Path Coupling, By Yufei Zhao et al.
A general approach to enhance the survivability of backdoor attacks by decision path coupling
by Yufei Zhao, Dingji Wang, Bihuan Chen, Ziqian Chen, Xin Peng
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR)
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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 The paper proposes a novel approach, called Venom, which enhances the survivability of existing backdoor attacks against model reconstruction-based defenses. This is achieved by formalizing Venom as a binary-task optimization problem that improves both the attack’s original capability and its ability to survive defense mechanisms. The authors introduce attention imitation loss to couple the decision paths of poisoned samples in backdoored models with those of benign samples, making it harder for defenses to eliminate backdoors. The paper demonstrates Venom’s effectiveness on two DNNs and three datasets, showcasing significant improvements in survivability without compromising the original attack capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Venom is a new way to make sure existing backdoor attacks can still work even if someone tries to fix them. Backdoored models are special kinds of artificial intelligence that have been secretly trained to do something they’re not supposed to. Some people have developed ways to remove these backdoors, but Venom helps the backdoors survive those attempts. It does this by making sure the decisions made by the poisoned data in the backdoored model match the decisions made by normal data. This makes it harder for defenses to get rid of the backdoors. The researchers tested Venom on different types of models and datasets and found that it really works well. |
Keywords
» Artificial intelligence » Attention » Optimization
