AIM: Adversarial Information Masking for Faithfulness Evaluation of Saliency Maps

ArXi:2605.16905v1 Announce Type: new Post-hoc saliency methods are widely used to interpret deep neural networks, but their faithfulness is difficult to evaluate reliably. Existing evaluations mask features according to saliency-induced feature ordering and measure performance degradation, but this degradation can be confounded by the masking operator: zero masking may create out-of-distribution artifacts, while interpolation-based masking may preserve residual predictive information.