Understanding Backdoor Attacks in Scene Graph Generation via Global Context Pathway

Summary

This paper studies training-time backdoor attacks on Scene Graph Generation (SGG), focusing on the explicit global context pathway in recent architectures. The authors instantiate an attack that selectively corrupts relations involving a designated object category, and evaluate it on Visual Genome with open-vocabulary and closed-set SGG models using a three-axis protocol. Mechanistic analysis finds the global context pathway is a major carrier of the backdoor effect. Replacing the poisoned global context representation with its clean counterpart at inference cuts attack success by 100% for the OvSGTR family and 97% for RelationFormer.

Topics

• Adversarial machine learning

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