Predicting Adversarial Examples with High Confidence
This addresses the problem of adversarial vulnerability in deep learning models for security-critical applications, offering a novel perspective but is incremental in its approach.
The paper argues that overly confident deep learning models are more vulnerable to adversarial examples, linking robustness to uncalibrated confidence on noisy images without data augmentation, and suggests that adversarial examples arise from increased model capacity without corresponding dataset diversity.
It has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence. In this work, we take the opposite stance: an overly confident model is more likely to be vulnerable to adversarial examples. This work is one of the most proactive approaches taken to date, as we link robustness with non-calibrated model confidence on noisy images, providing a data-augmentation-free path forward. The adversarial examples phenomenon is most easily explained by the trend of increasing non-regularized model capacity, while the diversity and number of samples in common datasets has remained flat. Test accuracy has incorrectly been associated with true generalization performance, ignoring that training and test splits are often extremely similar in terms of the overall representation space. The transferability property of adversarial examples was previously used as evidence against overfitting arguments, a perceived random effect, but overfitting is not always random.