BEARD
BEnchmarking the Adversarial Robustness for Dataset distillation
The goal of BEARD is to systematically explore
the adversarial robustness in Dataset Distillation (DD). A critical gap exists in the field, where the adversarial robustness of models trained on distilled datasets remains underexplored. Despite advancements in DD, deep neural networks continue to be vulnerable to adversarial attacks, posing significant security risks across various applications. Current research shows that while DD can partially improve adversarial robustness, it falls short of fully addressing these vulnerabilities. Additionally, the lack of a unified, open-source benchmark for evaluating adversarial robustness in DD has slowed progress in the field.
In response, we introduce BEARD, an open benchmark designed to systematically evaluate the adversarial robustness of DD methods. BEARD provides a comprehensive framework for testing DD methods across various datasets using different adversarial attacks, introduces new evaluation metrics, and offers open-source tools to support ongoing research and development.
TO DO List:
Supplement More DD Methods: Our benchmark evaluates the following distillation methods: DC, DSA, DM, MTT, IDM, BACON, TESLA, RDED, and et cetera. We will periodically update this list as new methods are developed and evaluated.
✓ Completed methods
✗ Not yet completed methods
We will periodically update this list as new methods are developed and evaluated.
Training phase with
6+ distillation methods
and 3+ datasets
Evaluation phase with
5+ adversarial attacks
to assess model robustness
Training Configuration
# Configuration for model training
config = {
"dataset": "CIFAR10",
"model": "ConvNet",
"method": "XX",
"ipc": "50",
"dsa_strategy": "color_crop_cutout_flip_scale_rotate",
"syn_ce": true,
"ce_weight": 1,
"aug": false,
"data_file": "./data_pool/XX/XXX.pt",
"save_path": "./model_pool/XX/",
"train_attack": "PGD",
"target_attack": false,
"test_attack": "None",
"src_dataset": false
}
Evaluating Configuration
# Configuration for model evaluation
config = {
"dataset": "CIFAR10",
"model": "ConvNet",
"method": "XX",
"ipc": "50",
"dsa_strategy": "color_crop_cutout_flip_scale_rotate",
"syn_ce": true,
"ce_weight": 1,
"aug": false,
"load_file": "。/model_pool/XX/XXX.pth",
"save_path": "。/model_pool/XX/",
"train_attack": "None",
"test_attack": ["Clean", "FGSM", "PGD", XXX],
"target_attack": [true, false],
"src_dataset": true,
"pgd_eva": false
}
FAQ
➤ How does the BEARD leaderboard differ from the
DC-Bench leaderboard? 🤔
The DC-Bench leaderboard is
a leaderboard to evaluate the distillation performance of DD methods. BEARD aims to evaluate the adversarial robustness of DD methods.
➤ How does the BEARD leaderboard differ from
RobustBench? 🤔
RobustBench focuses on
adversarial robustness evaluations of general methods, but we provide the evaluation of adversarial robustness for DD methods.
Citation
@inproceedings{zhou2025beard, title={BEARD: Benchmarking the Adversarial Robustness for Dataset Distillation}, author={Zhou, Zheng and Feng, Wenquan and Lyu, Shuchang and Cheng, Guangliang and Huang, Xiaowei and Zhao, Qi}, journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2025} }
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