Does Saliency-Based Training bring Robustness for Deep Neural Networks in Image Classification?
This addresses the problem of model robustness for researchers and practitioners in AI security, but the findings are incremental as they confirm existing vulnerabilities rather than proposing a new solution.
The paper investigates whether saliency-based training improves the robustness of deep neural networks against adversarial attacks in image classification, finding that despite producing visually explainable features, these models exhibit lower performance against adversarial examples.
Deep Neural Networks are powerful tools to understand complex patterns and making decisions. However, their black-box nature impedes a complete understanding of their inner workings. While online saliency-guided training methods try to highlight the prominent features in the model's output to alleviate this problem, it is still ambiguous if the visually explainable features align with robustness of the model against adversarial examples. In this paper, we investigate the saliency trained model's vulnerability to adversarial examples methods. Models are trained using an online saliency-guided training method and evaluated against popular algorithms of adversarial examples. We quantify the robustness and conclude that despite the well-explained visualizations in the model's output, the salient models suffer from the lower performance against adversarial examples attacks.