Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing
<|reference_start|>Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized Smoothing: Randomized smoothing is the primary certified robustness method for accessing the robustness of deep learning models to adversarial perturbations in the l2-norm, by adding isotropic Gaussian noise to the input image and returning the majority votes over the base classifier. Theoretically, it provides a certified norm bound, ensuring predictions of adversarial examples are stable within this bound. A notable constraint limiting widespread adoption is the necessity to retrain base models entirely from scratch to attain a robust version. This is because the base model fails to learn the noise-augmented data distribution to give an accurate vote. One intuitive way to overcome this challenge is to involve a custom-trained denoiser to eliminate the noise. However, this approach is inefficient and sub-optimal. Inspired by recent large model training procedures, we explore an alternative way named PEFTSmoothing to adapt the base model to learn the Gaussian noise-augmented data with Parameter-Efficient Fine-Tuning (PEFT) methods in both white-box and black-box settings. Extensive results demonstrate the effectiveness and efficiency of PEFTSmoothing, which allow us to certify over 98% accuracy for ViT on CIFAR-10, 20% higher than SoTA denoised smoothing, and over 61% accuracy on ImageNet which is 30% higher than CNN-based denoiser and comparable to the Diffusion-based denoiser.<|reference_end|>
@article{fu2024certified,
    title={Certified PEFTSmoothing: Parameter-Efficient Fine-Tuning with Randomized
  Smoothing},
    author={Chengyan Fu and Wenjie Wang},
    journal={arXiv preprint arXiv:2404.05350},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05350},
    primaryClass={cs.LG cs.CR}
}