# Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions?

Source: [Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions?](<https://www.sota2.com/research/paper/69d33e177d0172140ec73771>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-07T05:00:04.607Z

Authors: Kamalasankari Subramaniakuppusamy, Jugal Gajjar

Published: 2026-04-02T21:32:54.000Z

## 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 attribution instability than photometric changes, and without conditioning on prediction preservation, up to 99% of evaluated pairs involve changed predictions. Under this controlled evaluation, we observe consistent method-level trends, with Grad-CAM achieving the highest stability across datasets.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.02532>)

[PDF](<https://arxiv.org/pdf/2604.02532.pdf>)