# Adversarial Feature Alignment: Balancing Robustness and Accuracy in Deep Learning via Adversarial Training

arXiv: https://arxiv.org/abs/2402.12187 · HTML: https://mlsanity.com/paper/2402.12187/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 94 references that are in the map.
- First posted on arXiv 19 Feb 2024.

## Builds on (most cited first, 30 of 62)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Learning deep representations by mutual information estimation and maximization](https://mlsanity.com/paper/1808.06670/) (2018)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Robustness May Be at Odds with Accuracy](https://mlsanity.com/paper/1805.12152/) (2018)
- [Fast is better than free: Revisiting adversarial training](https://mlsanity.com/paper/2001.03994/) (2020)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Overfitting in adversarially robust deep learning](https://mlsanity.com/paper/2002.11569/) (2020)
- [Lipschitz regularity of deep neural networks: analysis and efficient estimation](https://mlsanity.com/paper/1805.10965/) (2018)
- [Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models](https://mlsanity.com/paper/1805.06605/) (2018)
- [HopSkipJumpAttack: A Query-Efficient Decision-Based Attack](https://mlsanity.com/paper/1904.02144/) (2019)
- [Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser](https://mlsanity.com/paper/1712.02976/) (2017)
- [Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks](https://mlsanity.com/paper/1906.04893/) (2019)
- [Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality](https://mlsanity.com/paper/1801.02613/) (2018)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [MagNet: a Two-Pronged Defense against Adversarial Examples](https://mlsanity.com/paper/1705.09064/) (2017)
- [Improving Robustness using Generated Data](https://mlsanity.com/paper/2110.09468/) (2021)
- [Parametric Contrastive Learning](https://mlsanity.com/paper/2107.12028/) (2021)
- [Better Diffusion Models Further Improve Adversarial Training](https://mlsanity.com/paper/2302.04638/) (2023)

## Built on by (most cited first, 5 of 5)

- [Hierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction](https://mlsanity.com/paper/2412.08243/) (2024)
- [Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training](https://mlsanity.com/paper/2411.02871/) (2024)
- [ST-DAI: Single-shot 2.5D Spatial Transcriptomics with Intra-Sample Domain Adaptive Imputation for Cost-efficient 3D Reconstruction](https://mlsanity.com/paper/2507.21516/) (2025)
- [SynthGenNet: a self-supervised approach for test-time generalization using synthetic multi-source domain mixing of street view images](https://mlsanity.com/paper/2509.02287/) (2025)
- [Enhancing the QA Model through a Multi-domain Debiasing Framework](https://mlsanity.com/paper/2601.11581/) (2026)

## Abstract (arXiv)

Deep learning models continue to advance in accuracy, yet they remain vulnerable to adversarial attacks, which often lead to the misclassification of adversarial examples. Adversarial training is used to mitigate this problem by increasing robustness against these attacks. However, this approach typically reduces a model's standard accuracy on clean, non-adversarial samples. The necessity for deep learning models to balance both robustness and accuracy for security is obvious, but achieving this balance remains challenging, and the underlying reasons are yet to be clarified. This paper proposes a novel adversarial training method called Adversarial Feature Alignment (AFA), to address these problems. Our research unveils an intriguing insight: misalignment within the feature space often leads to misclassification, regardless of whether the samples are benign or adversarial. AFA mitigates this risk by employing a novel optimization algorithm based on contrastive learning to alleviate potential feature misalignment. Through our evaluations, we demonstrate the superior performance of AFA. The baseline AFA delivers higher robust accuracy than previous adversarial contrastive learning methods while minimizing the drop in clean accuracy to 1.86% and 8.91% on CIFAR10 and CIFAR100, respectively, in comparison to cross-entropy. We also show that joint optimization of AFA and TRADES, accompanied by data augmentation using a recent diffusion model, achieves state-of-the-art accuracy and robustness.