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Backdoors Stuck At The Frontdoor: Multi-Agent Backdoor Attacks That Backfire. (arXiv:2201.12211v1 [cs.LG])
Jan. 31, 2022, 2:20 a.m. | Siddhartha Datta, Nigel Shadbolt
cs.CR updates on arXiv.org arxiv.org
Malicious agents in collaborative learning and outsourced data collection
threaten the training of clean models. Backdoor attacks, where an attacker
poisons a model during training to successfully achieve targeted
misclassification, are a major concern to train-time robustness. In this paper,
we investigate a multi-agent backdoor attack scenario, where multiple attackers
attempt to backdoor a victim model simultaneously. A consistent backfiring
phenomenon is observed across a wide range of games, where agents suffer from a
low collective attack success rate. We …
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