[2604.02532] Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions?

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.02532 (cs) [Submitted on 2 Apr 2026] Title:Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions? Authors:Kamalasankari Subramaniakuppusamy, Jugal Gajjar View a PDF of the paper titled Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions?, by Kamalasankari Subramaniakuppusamy and Jugal Gajjar View PDF HTML (experimental) Abstract:Post-hoc feature attribution methods are widely deployed in safety-critical vision systems, yet their stability under realistic input perturbations remains poorly characterized. Existing metrics evaluate explanations primarily under additive noise, collapse stability to a single scalar, and fail to condition on prediction preservation, conflating explanation fragility with model sensitivity. We introduce the Feature Attribution Stability Suite (FASS), a benchmark that enforces prediction-invariance filtering, decomposes stability into three complementary metrics: structural similarity, rank correlation, and top-k Jaccard overlap-and evaluates across geometric, photometric, and compression perturbations. Evaluating four attribution methods (Integrated Gradients, GradientSHAP, Grad-CAM, LIME) across four architectures and three datasets-ImageNet-1K, MS COCO, and CIFAR-10, FASS shows that stability estimates depend critically on perturbation family and prediction-invariance filtering. Geometric perturbations expose substantially greater attr...