Summary of Ten-guard: Tensor Decomposition For Backdoor Attack Detection in Deep Neural Networks, by Khondoker Murad Hossain et al.
TEN-GUARD: Tensor Decomposition for Backdoor Attack Detection in Deep Neural Networks
by Khondoker Murad Hossain, Tim Oates
First submitted to arxiv on: 6 Jan 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 This research paper introduces a novel approach to detecting backdoors in deep neural networks. The authors propose using two tensor decomposition methods on network activations, which offers several advantages over existing detection methods. The new method can analyze multiple models simultaneously, works across various network architectures, makes no assumptions about trigger types, and is computationally efficient. The authors provide detailed results on models trained on the MNIST digit dataset, CIFAR-10 dataset, and two difficult datasets from NIST’s TrojAI competition, showing that their method detects backdoored networks more accurately and efficiently than current state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Backdoors in deep neural networks can have malicious behavior. This paper has a new way to find these hidden backdoors. It uses special math to look at how the network works and figure out if it’s been changed to do something bad. This method is better than others because it can check many models at once, works with different types of networks, doesn’t make assumptions about how the network was changed, and is fast. |
