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With machine learning models being used for more sensitive applications, werely on interpretability methods to prove that no discriminating attributeswere used for classification. A potential concern is the so-called"fair-washing" - manipulating a model such that the features used in realityare hidden and more innocuous features are shown to be important instead.  In our work we present an effective defence against such adversarial attackson neural networks. By a simple aggregation of multiple explanation methods,the network becomes robust against manipulation. This holds even when theattacker has exact knowledge of the model weights and the explanation methodsused.

A simple defense against adversarial attacks on heatmap explanations