# Enhanced Regularizers for Attributional Robustness

arXiv: https://arxiv.org/abs/2012.14395 · HTML: https://mlsanity.com/paper/2012.14395/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Certifiably Robust Interpretation via Renyi Differential Privacy](https://mlsanity.com/paper/2107.01561/) (2021)
- [Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection](https://mlsanity.com/paper/2205.07279/) (2022)
- [On the Robustness of Explanations of Deep Neural Network Models: A Survey](https://mlsanity.com/paper/2211.04780/) (2022)
- [Rethinking Robustness of Model Attributions](https://mlsanity.com/paper/2312.10534/) (2023)
- [A Practical Upper Bound for the Worst-Case Attribution Deviations](https://mlsanity.com/paper/2303.00340/) (2023)
- [Locally Invariant Explanations: Towards Stable and Unidirectional Explanations through Local Invariant Learning](https://mlsanity.com/paper/2201.12143/) (2022)
- [Provable Robust Saliency-based Explanations](https://mlsanity.com/paper/2212.14106/) (2022)
- [Certified ℓ2 Attribution Robustness via Uniformly Smoothed Attributions](https://mlsanity.com/paper/2405.06361/) (2024)
- [Explanation-Guided Adversarial Training for Robust and Interpretable Models](https://mlsanity.com/paper/2603.01938/) (2026)
- [Robust Ranking Explanations](https://mlsanity.com/paper/2307.04024/) (2023)
- [Rethinking Robustness: A New Approach to Evaluating Feature Attribution Methods](https://mlsanity.com/paper/2512.06665/) (2025)
- [Improving Adversarial Robustness of Attribution via Implicit Regularization](https://mlsanity.com/paper/2605.29983/) (2026)

## Abstract (arXiv)

Deep neural networks are the default choice of learning models for computer vision tasks. Extensive work has been carried out in recent years on explaining deep models for vision tasks such as classification. However, recent work has shown that it is possible for these models to produce substantially different attribution maps even when two very similar images are given to the network, raising serious questions about trustworthiness. To address this issue, we propose a robust attribution training strategy to improve attributional robustness of deep neural networks. Our method carefully analyzes the requirements for attributional robustness and introduces two new regularizers that preserve a model's attribution map during attacks. Our method surpasses state-of-the-art attributional robustness methods by a margin of approximately 3% to 9% in terms of attribution robustness measures on several datasets including MNIST, FMNIST, Flower and GTSRB.