  Neural networks have shown remarkable performance in computer vision, but
their deployment in numerous scientific and technical fields is challenging due
to their black-box nature. Scientists and practitioners need to evaluate the
reliability of a decision, i.e., to know simultaneously if a model relies on
the relevant features and whether these features are robust to image
corruptions. Existing attribution methods aim to provide human-understandable
explanations by highlighting important regions in the image domain, but fail to
fully characterize a decision process's reliability. To bridge this gap, we
introduce the Wavelet sCale Attribution Method (WCAM), a generalization of
attribution from the pixel domain to the space-scale domain using wavelet
transforms. Attribution in the wavelet domain reveals where and on what scales
the model focuses, thus enabling us to assess whether a decision is reliable.
Our code is accessible here:
\url{https://github.com/gabrielkasmi/spectral-attribution}.
