# BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation

arXiv: https://arxiv.org/abs/2411.09265 · HTML: https://mlsanity.com/paper/2411.09265/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 14 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Dataset Condensation with Gradient Matching](https://mlsanity.com/paper/2006.05929/) (2020)
- [Empirical Analysis of the Hessian of Over-Parametrized Neural Networks](https://mlsanity.com/paper/1706.04454/) (2017)
- [Dataset Distillation](https://mlsanity.com/paper/1811.10959/) (2018)
- [Dataset Meta-Learning from Kernel Ridge-Regression](https://mlsanity.com/paper/2011.00050/) (2020)
- [Torchattacks: A PyTorch Repository for Adversarial Attacks](https://mlsanity.com/paper/2010.01950/) (2020)
- [Towards Adversarially Robust Dataset Distillation by Curvature Regularization](https://mlsanity.com/paper/2403.10045/) (2024)
- [Towards Trustworthy Dataset Distillation](https://mlsanity.com/paper/2307.09165/) (2023)
- [Towards Robust Dataset Learning](https://mlsanity.com/paper/2211.10752/) (2022)
- [A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness](https://mlsanity.com/paper/2305.03355/) (2023)
- [DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation](https://mlsanity.com/paper/2403.13322/) (2024)
- [MVPatch: More Vivid Patch for Adversarial Camouflaged Attacks on Object Detectors in the Physical World](https://mlsanity.com/paper/2312.17431/) (2023)
- [BACON: Bayesian Optimal Condensation Framework for Dataset Distillation](https://mlsanity.com/paper/2406.01112/) (2024)

## Built on by (most cited first, 2 of 2)

- [The Evolution of Dataset Distillation: Toward Scalable and Generalizable Solutions](https://mlsanity.com/paper/2502.05673/) (2025)
- [InkDrop: Invisible Backdoor Attacks Against Dataset Condensation](https://mlsanity.com/paper/2603.28092/) (2026)

## Abstract (arXiv)

Dataset Distillation (DD) is an emerging technique that compresses large-scale datasets into significantly smaller synthesized datasets while preserving high test performance and enabling the efficient training of large models. However, current research primarily focuses on enhancing evaluation accuracy under limited compression ratios, often overlooking critical security concerns such as adversarial robustness. A key challenge in evaluating this robustness lies in the complex interactions between distillation methods, model architectures, and adversarial attack strategies, which complicate standardized assessments. To address this, we introduce BEARD, an open and unified benchmark designed to systematically assess the adversarial robustness of DD methods, including DM, IDM, and BACON. BEARD encompasses a variety of adversarial attacks (e.g., FGSM, PGD, C&W) on distilled datasets like CIFAR-10/100 and TinyImageNet. Utilizing an adversarial game framework, it introduces three key metrics: Robustness Ratio (RR), Attack Efficiency Ratio (AE), and Comprehensive Robustness-Efficiency Index (CREI). Our analysis includes unified benchmarks, various Images Per Class (IPC) settings, and the effects of adversarial training. Results are available on the BEARD Leaderboard, along with a library providing model and dataset pools to support reproducible research. Access the code at BEARD.