The paper introduces a framework for collusion between train- and inference-time adversaries in ML pipelines, proposes a guideline for conjecturing collusion potential, explains prior work, and empirically validates five cases.
Backdooring bias into text-to-image models
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SoK: Colluding Adversaries in Machine Learning Pipelines
The paper introduces a framework for collusion between train- and inference-time adversaries in ML pipelines, proposes a guideline for conjecturing collusion potential, explains prior work, and empirically validates five cases.