Meet Trick With Trick: Revealing Collusion Intentions in Highly Concealed Poisoning Behavior

Summary

This paper proposes METT, a divide-and-conquer detection method for data poisoning attacks on recommender systems. It combines causality inference over group-level and individual-level unfairness sequences, an early attack-target detection method named ideaT, and a disturbance tolerance mechanism for fake injections. On synthetic and real data, METT reduces the false alarm rate (FAR) by averages of 21% for S-attacks, 18% for profile injection attacks, 26% for reverse attacks, and 17% for optimal-injection attacks, and by 10% and 20% for hybrid promotion and demotion attacks.

Mitigation

METT, a divide-and-conquer detection method for data poisoning attacks on recommender systems.

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