SoK: Explainable Machine Learning in Adversarial Environments
Author:
Affiliation:
1. Karlsruhe Institute of Technology,KASTEL Security Research Labs,Germany
Funder
Ministry of Education
Helmholtz Association
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10646615/10646598/10646794.pdf?arnumber=10646794
Reference202 articles.
1. Interpretations cannot be trusted: Stealthy and effective adversarial perturbations against interpretable deep learning;Abdukhamidov;CoRR,2022
2. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
3. Fairwashing: The risk of rationalization;Aïvodji
4. Characterizing the risk of fairwashing;Aïvodji
5. On Smoothed Explanations
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Toward Learning Model-Agnostic Explanations for Deep Learning-Based Signal Modulation Classifiers;IEEE Transactions on Reliability;2024-09
2. SoK: Unintended Interactions among Machine Learning Defenses and Risks;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19
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