Name
Title | ||
|---|---|---|
Interpretability Derived Backdoor Attacks Detection in Deep Neural Networks: Work-in-Progress |
Abstract | ||
|---|---|---|
Backdoor attacks to deep neural networks (DNNs) have received increasing attentions, particularly in applications from edge computing. The detection of backdoor attacks is a challenging task, due to the lack of transparency in DNN. In this paper, we design a novel method to detect backdoor attacks in deep neural networks, which is derived from the interpretability of a DNN. A comprehensive analysis of the critical path in DNN is conducted, based on which two indicators are proposed, including the correlation coefficient and the discrete degree. Conseqently, an efficient backdoor detection algorithm is proposed, which only needs a few runtime images to identify the backdoor attacks. Initial experiments indicated the efficiency. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMSOFT51651.2020.9244019 | 2020 International Conference on Embedded Software (EMSOFT) |
Keywords | DocType | ISBN |
discrete degree,correlation coefficient,DNN,deep neural networks,interpretability derived backdoor attack detection | Conference | 978-1-7281-9196-6 |
Citations | PageRank | References |
1 | 0.36 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyu Wen | 1 | 1 | 2.05 |
| Wei Jiang | 2 | 5 | 2.81 |
| Jinyu Zhan | 3 | 8 | 6.23 |
| Xupeng Wang | 4 | 9 | 9.33 |
| Zhiyuan He | 5 | 2 | 1.73 |