# MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks

arXiv: https://arxiv.org/abs/2312.13630 · HTML: https://mlsanity.com/paper/2312.13630/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [RISE: Randomized Input Sampling for Explanation of Black-box Models](https://mlsanity.com/paper/1806.07421/) (2018)
- [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](https://mlsanity.com/paper/1910.01279/) (2019)
- [Full-Gradient Representation for Neural Network Visualization](https://mlsanity.com/paper/1905.00780/) (2019)
- [Visual Interpretability for Deep Learning: a Survey](https://mlsanity.com/paper/1802.00614/) (2018)
- [XRAI: Better Attributions Through Regions](https://mlsanity.com/paper/1906.02825/) (2019)
- [Guided Integrated Gradients: An Adaptive Path Method for Removing Noise](https://mlsanity.com/paper/2106.09788/) (2021)
- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [Robust Models Are More Interpretable Because Attributions Look Normal](https://mlsanity.com/paper/2103.11257/) (2021)
- [DANAA: Towards transferable attacks with double adversarial neuron attribution](https://mlsanity.com/paper/2310.10427/) (2023)
- [Interpreting Neural Ranking Models using Grad-CAM](https://mlsanity.com/paper/2005.05768/) (2020)

## Built on by (most cited first, 7 of 7)

- [Probabilistic Stability Guarantees for Feature Attributions](https://mlsanity.com/paper/2504.13787/) (2025)
- [Enhancing Model Interpretability with Local Attribution over Global Exploration](https://mlsanity.com/paper/2408.07736/) (2024)
- [Narrowing Information Bottleneck Theory for Multimodal Image-Text Representations Interpretability](https://mlsanity.com/paper/2502.14889/) (2025)
- [Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks](https://mlsanity.com/paper/2408.12670/) (2024)
- [Attribution for Enhanced Explanation with Transferable Adversarial eXploration](https://mlsanity.com/paper/2412.19523/) (2024)
- [ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability](https://mlsanity.com/paper/2505.06258/) (2025)
- [Frequency-Aware Model Parameter Explorer: A new attribution method for improving explainability](https://mlsanity.com/paper/2510.03245/) (2025)

## Abstract (arXiv)

To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each input dimension to indicate its importance towards the model outcome. Notably, the attribution methods use the axioms of sensitivity and implementation invariance to ensure the validity and reliability of attribution results. Yet, the existing attribution methods present challenges for effective interpretation and efficient computation. In this work, we introduce MFABA, an attribution algorithm that adheres to axioms, as a novel method for interpreting DNN. Additionally, we provide the theoretical proof and in-depth analysis for MFABA algorithm, and conduct a large scale experiment. The results demonstrate its superiority by achieving over 101.5142 times faster speed than the state-of-the-art attribution algorithms. The effectiveness of MFABA is thoroughly evaluated through the statistical analysis in comparison to other methods, and the full implementation package is open-source at: https://github.com/LMBTough/MFABA