  Though deep neural networks have achieved the state of the art performance in
visual classification, recent studies have shown that they are all vulnerable
to the attack of adversarial examples. In this paper, we develop improved
techniques for defending against adversarial examples. First, we propose an
enhanced defense technique denoted Attention and Adversarial Logit
Pairing(AT+ALP), which encourages both attention map and logit for the pairs of
examples to be similar. When being applied to clean examples and their
adversarial counterparts, AT+ALP improves accuracy on adversarial examples over
adversarial training. We show that AT+ALP can effectively increase the average
activations of adversarial examples in the key area and demonstrate that it
focuses on discriminate features to improve the robustness of the model.
Finally, we conduct extensive experiments using a wide range of datasets and
the experiment results show that our AT+ALP achieves the state of the art
defense performance. For example, on 17 Flower Category Database, under strong
200-iteration PGD gray-box and black-box attacks where prior art has 34% and
39% accuracy, our method achieves 50% and 51%. Compared with previous work, our
work is evaluated under highly challenging PGD attack: the maximum perturbation
$\epsilon \in \{0.25,0.5\}$ i.e. $L_\infty \in \{0.25,0.5\}$ with 10 to 200
attack iterations. To the best of our knowledge, such a strong attack has not
been previously explored on a wide range of datasets.
