Pre-trained Robustifier for Adversarial Noise Removal
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Solution Overview
Problem
Pre-trained classifiers are susceptible to adversarial attacks due to lack of robust training, and existing methods for enhancing robustness, such as denoised-smoothing, are costly and dependent on the quality of the denoiser, limiting their empirical robustness and efficiency.
Innovation Solution
A method that trains an image-to-image translation model as a robustifier, optimizing for both image reconstruction fidelity and adversarial performance, which is prepended to a pre-trained classifier, allowing for empirical robustness enhancement without re-optimizing the classifier's parameters, and is transferable across different models and architectures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple copies of input data are made with random noise and denoising is applied, then robustness against adversarial attacks is improved, but computational cost and inference time increase
Solution Approach 1:
The robustifier is trained in advance on adversarially perturbed data to learn robust feature representations before deployment. During inference, the pre-trained robustifier processes input data without requiring multiple copies or denoising operations, thus achieving robustness while maintaining efficient inference time.
Solution Approach 2:
The invention extracts and separates the robustification function into an independent robustifier module that can be trained and deployed separately from the main classifier. This extracted robustifier component handles adversarial perturbation removal independently, avoiding the computational overhead of multiple denoising iterations required by previous methods.
2Reliability
If the robustifier is trained to remove perturbations, then robustness is improved, but training complexity and computational resources increase
Solution Approach 1:
The training process is segmented into distinct phases: first training the robustifier on adversarially perturbed data to learn robust features, then freezing the robustifier and training the classifier on clean data. This segmentation allows each component to be optimized independently, reducing overall training complexity compared to joint training of the entire system.
Solution Approach 2:
Instead of retraining the entire classifier system, the invention applies partial action by training only the robustifier component on adversarial data, then separately training the classifier on clean data. This partial retraining approach reduces computational resources and training complexity while still achieving robustness improvements.
3Productivity
If pre-trained classifiers are used, then deployment speed is improved, but susceptibility to adversarial attacks increases
Solution Approach 1:
The robustifier acts as an intermediary component between the input data and the pre-trained classifier. It processes and cleans adversarially perturbed input data before passing it to the classifier, thereby protecting the classifier from adversarial attacks while maintaining the classifier's original architecture and deployment efficiency.
Solution Approach 2:
The robustifier is pre-trained on adversarially perturbed data to learn how to remove adversarial perturbations. This preliminary training equips the robustifier with the ability to handle adversarial attacks, allowing the pre-trained classifier to maintain its deployment speed while gaining robustness through the robustifier's protective preprocessing.
Data Source
AI summary
A computer-implemented method for training a machine-learning network. The method includes receiving an input data from a sensor, wherein the input data is indicative of image, radar, sonar, or sound information, generating an input data set utilizing the input data, wherein the input data set includes perturbed data, sending the input data set to a robustifier, wherein the robustifier is configured to clean the input data set by removing perturbations associated with the input data set to create a modified input data set, sending the modified input data set to a pretrained machine learning task, training the robustifier to obtain a trained robustifier utilizing the modified input data set, and in response to convergence of the trained robustifier to a first threshold, output the trained robustifier.