Summary of Certifying Adapters: Enabling and Enhancing the Certification Of Classifier Adversarial Robustness, by Jieren Deng et al.
Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness
by Jieren Deng, Hanbin Hong, Aaron Palmer, Xin Zhou, Jinbo Bi, Kaleel Mahmood, Yuan Hong, Derek Aguiar
First submitted to arxiv on: 25 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research introduces a novel framework for achieving certified robustness in deep classifiers against l_p-norm adversarial perturbations, building upon randomized smoothing methods. The certifying adapters framework (CAF) enables the certification of classifier adversarial robustness without requiring expensive training procedures that tune large models for different Gaussian noise levels. CAF is broadly applicable to different feature extractor architectures and smoothing algorithms. Experiments demonstrate improved certified accuracies compared to random or denoised smoothing, and the framework’s insensitivity to certifying adapter hyperparameters. Furthermore, an ensemble of adapters allows a single pre-trained feature extractor to defend against various noise perturbation scales. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Certified robustness in deep classifiers is crucial for their reliability in real-world applications. Researchers have developed methods like data augmentation with Gaussian noise and adversarial training to achieve this. However, these approaches require expensive training procedures that tune large models for different noise levels. A new framework called certifying adapters (CAF) can certify the robustness of pre-trained neural networks without requiring such tuning. This means that high-performance pre-trained models can be used for certification. The CAF is broadly applicable to various feature extractor architectures and smoothing algorithms. It even enables the defense against different noise perturbation scales. |
Keywords
» Artificial intelligence » Data augmentation
