  CAM-based methods are widely-used post-hoc interpretability method that
produce a saliency map to explain the decision of an image classification
model. The saliency map highlights the important areas of the image relevant to
the prediction. In this paper, we show that most of these methods can
incorrectly attribute an important score to parts of the image that the model
cannot see. We show that this phenomenon occurs both theoretically and
experimentally. On the theory side, we analyze the behavior of GradCAM on a
simple masked CNN model at initialization. Experimentally, we train a VGG-like
model constrained to not use the lower part of the image and nevertheless
observe positive scores in the unseen part of the image. This behavior is
evaluated quantitatively on two new datasets. We believe that this is
problematic, potentially leading to mis-interpretation of the model's behavior.
