  Despite achieving great success, Deep Neural Networks (DNNs) are vulnerable
to adversarial examples. How to accurately evaluate the adversarial robustness
of DNNs is critical for their deployment in real-world applications. An ideal
indicator of robustness is adversarial risk. Unfortunately, since it involves
maximizing the 0-1 loss, calculating the true risk is technically intractable.
The most common solution for this is to compute an approximate risk by
replacing the 0-1 loss with a surrogate one. Some functions have been used,
such as Cross-Entropy (CE) loss and Difference of Logits Ratio (DLR) loss.
However, these functions are all manually designed and may not be well suited
for adversarial robustness evaluation. In this paper, we leverage AutoML to
tighten the error (gap) between the true and approximate risks. Our main
contributions are as follows. First, AutoLoss-AR, the first method to search
for surrogate losses for adversarial risk, with an elaborate search space, is
proposed. The experimental results on 10 adversarially trained models
demonstrate the effectiveness of the proposed method: the risks evaluated using
the best-discovered losses are 0.2% to 1.6% better than those evaluated using
the handcrafted baselines. Second, 5 surrogate losses with clean and readable
formulas are distilled out and tested on 7 unseen adversarially trained models.
These losses outperform the baselines by 0.8% to 2.4%, indicating that they can
be used individually as some kind of new knowledge. Besides, the possible
reasons for the better performance of these losses are explored.

Machine Learning;Artificial Intelligence