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The inability of DNNs to explain their black-box behavior has led to a recentsurge of explainability methods. However, there are growing concerns that theseexplainability methods are not robust and trustworthy. In this work, we performthe first robustness analysis of Neuron Explanation Methods under a unifiedpipeline and show that these explanations can be significantly corrupted byrandom noises and well-designed perturbations added to their probing data. Wefind that even adding small random noise with a standard deviation of 0.02 canalready change the assigned concepts of up to 28% neurons in the deeper layers.Furthermore, we devise a novel corruption algorithm and show that our algorithmcan manipulate the explanation of more than 80% neurons by poisoning less than10% of probing data. This raises the concern of trusting Neuron ExplanationMethods in real-life safety and fairness critical applications.

Corrupting Neuron Explanations of Deep Visual Features