Training for Trustworthy Saliency Maps: Adversarial Training Meets Feature-Map Smoothing

TL;DR

This paper introduces a training-based approach combining adversarial training with feature-map smoothing to produce more stable and trustworthy saliency maps for image classifiers, addressing noise and instability issues.

Contribution

It demonstrates that integrating feature-map smoothing into adversarial training enhances the stability and trustworthiness of saliency maps, a novel training-centered solution.

Findings

Sparser and more input-stable saliency maps from adversarial training.

Smoothing improves both input-side and output-side stability.

Human study confirms increased perceived trustworthiness.

Abstract

Gradient-based saliency methods such as Vanilla Gradient (VG) and Integrated Gradients (IG) are widely used to explain image classifiers, yet the resulting maps are often noisy and unstable, limiting their usefulness in high-stakes settings. Most prior work improves explanations by modifying the attribution algorithm, leaving open how the training procedure shapes explanation quality. We take a training-centered view and first provide a curvature-based analysis linking attribution stability to how smoothly the input-gradient field varies locally. Guided by this connection, we study adversarial training and identify a consistent trade-off: it yields sparser and more input-stable saliency maps, but can degrade output-side stability, causing explanations to change even when predictions remain unchanged and logits vary only slightly. To mitigate this, we propose augmenting adversarial training with a lightweight feature-map smoothing block that applies a differentiable Gaussian filter in an intermediate layer. Across FMNIST, CIFAR-10, and ImageNette, our method preserves the sparsity benefits of adversarial training while improving both input-side stability and output-side stability. A human study with 65 participants further shows that smoothed adversarial saliency maps are perceived as more sufficient and trustworthy. Overall, our results demonstrate that explanation quality is critically shaped by training, and that simple smoothing with robust training provides a practical path toward saliency maps that are both sparse and stable.