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As collaborative learning and the outsourcing of data collection become morecommon, malicious actors (or agents) which attempt to manipulate the learningprocess face an additional obstacle as they compete with each other. Inbackdoor attacks, where an adversary attempts to poison a model by introducingmalicious samples into the training data, adversaries have to consider that thepresence of additional backdoor attackers may hamper the success of their ownbackdoor. In this paper, we investigate the scenario of a multi-agent backdoorattack, where multiple non-colluding attackers craft and insert triggeredsamples in a shared dataset which is used by a model (a defender) to learn atask. We discover a clear backfiring phenomenon: increasing the number ofattackers shrinks each attacker's attack success rate (ASR). We then exploitthis phenomenon to minimize the collective ASR of attackers and maximizedefender's robustness accuracy by (i) artificially augmenting the number ofattackers, and (ii) indexing to remove the attacker's sub-dataset from themodel for inference, hence proposing 2 defenses.

Widen The Backdoor To Let More Attackers In