Summary of Rethinking Pruning For Backdoor Mitigation: An Optimization Perspective, by Nan Li and Haiyang Yu and Ping Yi
Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective
by Nan Li, Haiyang Yu, Ping Yi
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 Medium Difficulty summary: The abstract discusses the vulnerability of Deep Neural Networks (DNNs) to backdoor attacks, which can be devastating if left unchecked. Researchers have discovered that certain neurons in infected DNNs can be pruned to erase the backdoors, but identifying and removing these neurons remains a challenge. To address this issue, the authors propose an Optimized Neuron Pruning (ONP) method combining Graph Neural Network (GNN), Reinforcement Learning (RL), and pruning policies. The ONP method models DNNs as graphs based on neuron connectivity and uses GNN-based RL agents to learn graph embeddings and find a suitable pruning policy. This approach achieves state-of-the-art performance in backdoor mitigation, even with only a small amount of clean data. The results demonstrate the potential for effective backdoor defense using ONP. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: A big problem with Deep Neural Networks (DNNs) is that they can be hacked or “poisoned” to make them do bad things. This is called a backdoor attack, and it’s very difficult to fix once it happens. Scientists have found a way to remove the bad parts of the DNN by getting rid of certain connections between neurons. But figuring out which ones to get rid of is still a challenge. To solve this problem, researchers came up with a new method called Optimized Neuron Pruning (ONP). ONP uses special computer programs and algorithms to find the right way to fix the bad parts of the DNN without making it work any worse than before. This approach has shown great promise in fixing backdoor attacks and keeping our DNNs safe. |
Keywords
* Artificial intelligence * Gnn * Graph neural network * Pruning * Reinforcement learning
