用现成扩散模型做预训练，低成本提升图像分类与检测的抗攻击能力。
Diffusion Models are Robust Pretrainers
- 基于现成扩散模型提取特征，冻结特征仅训练轻量分类头。
- 在ImageNet等数据集上实现显著抗干扰性能，计算开销极低。
- 适合资源受限场景，为高效鲁棒模型提供新路径。
扩散模型因其高质量图像生成受到广泛关注。本文探究其在图像分类与目标检测任务中提升对抗鲁棒性的潜力。对抗攻击通过扰动输入使模型误判，现有方法多依赖耗时的对抗训练。本文提出利用现成扩散模型构建下游模型，证明其可提供低成本的鲁棒表征：仅需在冻结特征上训练轻量头部，无需完整对抗训练。在ImageNet、CIFAR-10和PASCAL VOC上的实验表明，基于扩散模型的分类器与检测器在保持较低计算成本的同时，展现出有意义的对抗鲁棒性。尽管纯净与对抗准确率仍低于最先进的对抗训练CNN或ViT，但该方法在效率与鲁棒性间取得良好平衡。本工作为资源受限环境下的鲁棒部署开辟了新方向。
原文摘要 · Abstract (English)
Diffusion models have gained significant attention for high-fidelity image generation. Our work investigates the potential of exploiting diffusion models for adversarial robustness in image classification and object detection. Adversarial attacks challenge standard models in these tasks by perturbing inputs to force incorrect predictions. To address this issue, many approaches use training schemes for forcing the robustness of the models, which increase training costs. In this work, we study models built on top of off-the-shelf diffusion models and demonstrate their practical significance: they provide a low-cost path to robust representations, allowing lightweight heads to be trained on frozen features without full adversarial training. Our empirical evaluations on ImageNet, CIFAR-10, and PASCAL VOC show that diffusion-based classifiers and detectors achieve meaningful adversarial robustness with minimal compute. While clean and adversarial accuracies remain below state-of-the-art adversarially trained CNNs or ViTs, diffusion pretraining offers a favorable tradeoff between efficiency and robustness. This work opens a promising avenue for integrating diffusion models into resource-constrained robust deployments.
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