The potential trade-offs between clean accuracy and robust accuracy under different types of attacks can vary based on the specific characteristics of the model and the nature of the attacks. Here are some key considerations and strategies to optimize this trade-off:
Balancing Act: Clean accuracy represents the model's performance on clean data, while robust accuracy reflects its resilience to adversarial attacks. Finding the right balance between the two is crucial.
Regularization Techniques: Implement regularization techniques such as adversarial training, data augmentation, and model distillation to improve robust accuracy without significantly compromising clean accuracy.
Hyperparameter Tuning: Fine-tune hyperparameters related to the adversarial training process to optimize the trade-off between clean and robust accuracy.
Ensemble Methods: Utilize ensemble methods to combine multiple models trained with different trade-offs between clean and robust accuracy to achieve a more balanced overall performance.
Continuous Evaluation: Continuously evaluate the model's performance under different attack scenarios and adjust the training strategy to maintain an optimal trade-off between clean and robust accuracy.
By carefully considering these factors and implementing appropriate strategies, it is possible to optimize the trade-off between clean accuracy and robust accuracy under different types of attacks, ensuring a well-balanced and effective defense mechanism for the model.