Evading Black-box Classifiers Without Breaking Eggs
Author:
Affiliation:
1. ETH Zurich
2. Google DeepMind
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10516617/10516618/10516642.pdf?arnumber=10516642
Reference41 articles.
1. “Real Attackers Don't Compute Gradients”: Bridging the Gap Between Adversarial ML Research and Practice
2. Evasion Attacks against Machine Learning at Test Time
3. Decision-based adversarial attacks: Reliable attacks against black-box machine learning models;Brendel,2017
4. Guessing Smart: Biased Sampling for Efficient Black-Box Adversarial Attacks
5. The market of Facebook accounts for sale,2020
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1. Towards Non-adversarial Algorithmic Recourse;Communications in Computer and Information Science;2024
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