首个为像素级解释提供严格鲁棒性保证的认证框架。
Pixel-level Certified Explanations via Randomized Smoothing
- 用随机平滑重构解释任务为分割问题，实现像素级鲁棒性认证。
- 在5个ImageNet模型上验证，12种解释方法均通过ℓ₂扰动下的可靠性测试。
- 适用于需要可信解释的高风险场景，如医疗影像与自动驾驶。
事后归因方法通过突出影响预测的关键输入像素来解释深度学习模型。然而，这些解释极不稳定：微小且不可察觉的输入扰动可显著改变归因图，而预测结果保持不变。这种脆弱性削弱了其可信度，亟需对像素级归因分数提供严格的鲁棒性保障。本文提出首个基于随机平滑的认证框架，可为任意黑箱归因方法提供像素级鲁棒性保证。通过稀疏化与平滑归因图，将任务重定义为分割问题，并对ℓ₂有界扰动下每个像素的重要性进行认证。我们进一步设计三种评估指标，用于衡量认证鲁棒性、定位精度和忠实性。在5个ImageNet模型上对12种归因方法的广泛评估表明，本方法生成的归因具有鲁棒性、可解释性和忠实性，可安全用于下游任务。代码已开源：https://github.com/AlaaAnani/certified-attributions。
原文摘要 · Abstract (English)
Post-hoc attribution methods aim to explain deep learning predictions by highlighting influential input pixels. However, these explanations are highly non-robust: small, imperceptible input perturbations can drastically alter the attribution map while maintaining the same prediction. This vulnerability undermines their trustworthiness and calls for rigorous robustness guarantees of pixel-level attribution scores. We introduce the first certification framework that guarantees pixel-level robustness for any black-box attribution method using randomized smoothing. By sparsifying and smoothing attribution maps, we reformulate the task as a segmentation problem and certify each pixel's importance against $\ell_2$-bounded perturbations. We further propose three evaluation metrics to assess certified robustness, localization, and faithfulness. An extensive evaluation of 12 attribution methods across 5 ImageNet models shows that our certified attributions are robust, interpretable, and faithful, enabling reliable use in downstream tasks. Our code is at https://github.com/AlaaAnani/certified-attributions.
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