  Attribution methods correspond to a class of explainability methods (XAI)
that aim to assess how individual inputs contribute to a model's
decision-making process. We have identified a significant limitation in one
type of attribution methods, known as ``white-box" methods. Although highly
efficient, as we will show, these methods rely on a gradient signal that is
often contaminated by high-frequency artifacts. To overcome this limitation, we
introduce a new approach called "FORGrad". This simple method effectively
filters out these high-frequency artifacts using optimal cut-off frequencies
tailored to the unique characteristics of each model architecture. Our findings
show that FORGrad consistently enhances the performance of already existing
white-box methods, enabling them to compete effectively with more accurate yet
computationally demanding "black-box" methods. We anticipate that our research
will foster broader adoption of simpler and more efficient white-box methods
for explainability, offering a better balance between faithfulness and
computational efficiency.
