Bilateral Adversarial Training for Robust Model Efficiency
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Solution Overview
Problem
Deep learning models are vulnerable to adversarial attacks, and existing adversarial training methods are computationally expensive and resource-intensive, making them impractical for real-world applications.
Innovation Solution
Bilateral Adversarial Training (BAT) method, which simultaneously perturbs both input images and labels during training using one-step PGD with random start and targeted attacks, to achieve robustness against adversarial attacks, reducing the computational resources required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-step adversarial training is used to improve model robustness against adversarial attacks, then model robustness is improved, but computational cost and training time increase significantly
Solution Approach 1:
The patent segments the adversarial training process into two independent components: (1) generating adversarial images using one-step PGD with random start, and (2) generating adversarial labels through label distribution adjustment. This segmentation allows each component to be optimized independently, reducing the overall computational burden while maintaining robustness effectiveness.
Solution Approach 2:
The patent applies partial action by using only one step of PGD instead of multiple steps, which is sufficient to generate effective adversarial examples for training. This partial action approach achieves comparable robustness to multi-step methods while significantly reducing computational time and resources.
2Reliability
If multi-step adversarial training is used to improve model robustness against adversarial attacks, then model robustness is improved, but memory cost and computational resources increase
Solution Approach 1:
The patent uses cheap short-living adversarial examples generated by one-step PGD with random start. These adversarial images and labels are generated on-the-fly during training and discarded after use, avoiding the need to store large amounts of pre-generated adversarial data, thus reducing memory cost and computational resource requirements.
3Reliability
If adversarial training with adversarial images is used to improve model robustness, then model robustness is improved, but training efficiency decreases
Solution Approach 1:
The patent applies preliminary action by adding random noise to the input image before the PGD attack. This preliminary perturbation helps the model generalize better and improves training efficiency by preventing overfitting to specific adversarial patterns, while still maintaining the effectiveness of the adversarial training process.
Data Source
AI summary
Described herein are embodiments for fast training of adversarially robust models against adversarial attacks. Embodiments for model training by perturbing both the image and the label, which may be referred to as Bilateral Adversarial Training (BAT), are disclosed. To generate the adversarial label, one or more closed-form heuristic solutions are derived. One-step targeted attack is used with the target label being the most confusing class. It is shown in various experiments that random start and the most confusing target attack effectively prevent the label leaking and gradient masking problem. Coupled with the adversarial label part, embodiments of presented models significantly improve the state-of-the-art results. Experiments on one or more computationally challenging dataset also demonstrate the effectiveness of the presented BAT method embodiments.