Backdooring Explainable Machine Learning

Understand

Explainable machine learning holds great potential for analyzing and understanding learning-based systems.

• These methods can, however, be manipulated to present unfaithful explanations, giving rise to powerful and stealthy adversaries.
• In this paper, we demonstrate blinding attacks that can fully disguise an ongoing attack against the machine learning model.
• Similar to neural backdoors, we modify the model's prediction upon trigger presence but simultaneously also fool the provided explanation.