Denoiser Module for Robust Pre-trained Classifiers
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Solution Overview
Problem
Deep learning models are vulnerable to adversarial attacks, and existing defense mechanisms often sacrifice clean image accuracy, making them impractical for deployment. Additionally, these defenses are not directly effective on pre-trained classifiers, especially when subjected to Gaussian noise.
Innovation Solution
The proposed solution involves using a denoiser as a preprocessing step to remove Gaussian noise from inputs, thereby robustifying pre-trained classifiers without altering their weights. This is achieved by training a denoiser using objectives like mean-squared-error and stability loss, and then integrating it with a pre-trained classifier and a rejector module to form a (K+1)-class classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing defense mechanisms are applied to protect against adversarial attacks, then robustness against adversarial attacks is improved, but clean image accuracy deteriorates
Solution Approach 1:
A denoiser module is introduced as an intermediary component between the input image and the pre-trained classifier. The denoiser processes the input image to remove Gaussian noise before passing it to the classifier, thereby protecting the classifier from adversarial perturbations while maintaining high accuracy on clean images. This intermediary structure allows the system to achieve both robustness and accuracy without retraining the classifier.
2Reliability
If randomized smoothing is applied to provide robustness guarantees, then provable robustness is improved, but clean accuracy deteriorates
Solution Approach 1:
Instead of retraining the entire classifier with expensive and time-consuming procedures, the invention uses a lightweight denoiser module that can be trained independently and applied to the pre-trained classifier. This disposable-like approach allows achieving robustness guarantees without the high cost of retraining, maintaining both provable robustness and clean accuracy.
3Reliability
If a denoiser is trained with stability loss to robustify the classifier, then robustness against Gaussian noise is improved, but training time and complexity increase
Solution Approach 1:
The training process is segmented into two independent stages: first training the denoiser module separately using stability loss, and then applying it to the pre-trained classifier without requiring joint training. This segmentation reduces the overall training time and complexity compared to training the entire system together, while still achieving robustness against Gaussian noise.
4Productivity
If pre-trained classifiers are used directly, then deployment speed is improved, but vulnerability to adversarial attacks increases
Solution Approach 1:
The denoiser module is trained in advance on clean images and adversarial examples to learn how to remove Gaussian noise and adversarial perturbations. This preliminary training action allows the denoiser to be applied to the pre-trained classifier without requiring retraining of the classifier itself, thus maintaining fast deployment speed while reducing vulnerability to adversarial attacks.
Data Source
AI summary
A system includes a machine-learning network. The network includes an input interface configured to receive input data from a sensor. The processor is programmed to receive the input data, generate a perturbed input data set utilize the input data, wherein the perturbed input data set includes perturbations of the input data, denoise the perturbed input data set utilizing a denoiser, wherein the denoiser is configured to generate a denoised data set, send the denoised data set to both a pre-trained classifier and a rejector, wherein the pre-trained classifier is configured to classify the denoised data set and the rejector is configured to reject a classification of the denoised data set, train, utilizing the denoised input data set, the a rejector to achieve a trained rejector, and in response to obtaining the trained rejector, output an abstain classification associated with the input data, wherein the abstain classification is ignored for classification.