Does Saliency-Based Training bring Robustness for Deep Neural Networks in Image Classification?

TL;DR

This paper investigates whether saliency-guided training improves the robustness of deep neural networks in image classification, finding that despite better visual explanations, such models are more vulnerable to adversarial attacks.

Contribution

The study evaluates the robustness of saliency-trained models against adversarial examples, revealing limitations despite improved interpretability.

Findings

Saliency-guided models have lower adversarial robustness.

Visual explanations do not necessarily correlate with model robustness.

Saliency training does not enhance resistance to adversarial attacks.

Abstract

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.