Poster (Contributed)
Workshop: AI for Agent-Based Modelling (AI4ABM)

Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks

Siddhartha Datta


Recent exploration of the multi-agent backdoor attack demonstrated the backfiring effect, a natural defense against backdoor attacks where backdoored inputs are randomly classified. This yields a side-effect of low accuracy w.r.t. clean labels, which motivates this paper's work on the construction of multi-agent backdoor defenses that maximize accuracy w.r.t. clean labels and minimize that of poison labels. Founded upon agent dynamics and low-loss subspace construction, we contribute three defenses that yield improved multi-agent backdoor robustness.

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