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Title: ATLPA:ADVERSARIAL TOLERANT LOGIT PAIRING WITH ATTENTION FOR CONVOLUTIONAL NEURAL NETWORK

Paper content:
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.
To solve the problem, some regularization adversarial training methods, constraining the output label or logit, have been studied.
In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention.
Instead of constraining a hard distribution (e.g., one-hot vectors or logit) in adversarial training, ATLPA uses Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level.
Specifically, in addition to minimizing the empirical loss, ATLPA encourages attention map for pairs of examples to be similar.
When applied to clean examples and their adversarial counterparts, ATLPA improves accuracy on adversarial examples over adversarial training.
We evaluate ATLPA with the state of the art algorithms, the experiment results show that our method outperforms these baselines with higher accuracy.
Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation $\epsilon$ is 64 and 128 with 10 to 200 attack iterations.
In recent years, deep neural networks have been extensively deployed for computer vision tasks, particularly visual classification problems, where new algorithms reported to achieve or even surpass the human performance (Krizhevsky et al., 2012; He et al., 2015; Li et al., 2019a) .
Success of deep neural networks has led to an explosion in demand.
Recent studies (Szegedy et al., 2013; Goodfellow et al., 2014; Carlini & Wagner, 2016; Moosavi-Dezfooli et al., 2016; Bose & Aarabi, 2018) have shown that they are all vulnerable to the attack of adversarial examples.
Small and often imperceptible perturbations to the input images are sufficient to fool the most powerful deep neural networks.
In order to solve this problem, many defence methods have been proposed, among which adversarial training is considered to be the most effective one .Adversarial
training (Goodfellow et al., 2014; Madry et al., 2017; Kannan et al., 2018; Tramèr et al., 2017; Pang et al., 2019) defends against adversarial perturbations by training networks on adversarial images that are generated on-the-fly during training. Although aforementioned
methods demonstrated the power of adversarial training in defence, we argue that we need to perform research on at least the following two aspects in order to further improve current defence methods.
Strictness vs. Tolerant.
Most existing defence methods only fit the outputs of adversarial examples to the one-hot vectors of clean examples counterparts.
Kannan et al. (2018) also fit confidence distribution on the all logits of clean examples counterparts, they call it as Logits Pair.
Despite its effectiveness, this is not necessarily the optimal target to fit, because except for maximizing the confidence score of the primary class (i.e., the ground-truth), allowing for some secondary classes (i.e., those visually similar ones to the ground-truth) to be preserved may help to alleviate the risk of over-fitting (Yang et al., 2018) .
We fit Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level.
We believe that limited attention should be devoted to top-k classes of the confidence score, rather than strictly fitting the confidence distribution of all classes.
A More Tolerant Teacher Educates Better Students.
Process vs. Result.
In Fig. 1 , we visualize the spatial attention map of a flower and its corresponding adversarial image on ResNet-101 (He et al., 2015) pretrained on ImageNet (Russakovsky et al., 2015) .
The figure suggests that adversarial perturbations, while small in the pixel space, lead to very substantial noise in the attention map of the network.
Whereas the features for the clean image appear to focus primarily on semantically informative content in the image, the attention map for the adversarial image are activated across semantically irrelevant regions as well.
The state of the art adversarial training methods only encourage hard distribution of deep neural networks output (e.g., one-hot vectors (Madry et al., 2017; Tramèr et al., 2017) or logit (Kannan et al., 2018) ) for pairs of clean examples and adversarial counterparts to be similar.
In our opinion, it is not enough to align the difference between the clean examples and adversarial counterparts only at the output layer of the network, and we need to align the attention maps of middle layers of the whole network, e.g.,o uter layer outputs of conv2.x, conv3.x, conv4.x, conv5.x in ResNet-101.
We can't just focus on the result, but also on the process.
(Russakovsky et al., 2015) .
(a) is original image and
(b) is corresponding adversarial image.For ResNet-101, which we use exclusively in this paper, we grouped filters into stages as described in (He et al., 2015) .
These stages are conv2.x, conv3.x, conv4.x, conv5.x.
The contributions of this paper are the following:
• We propose a novel regularized adversarial training framework ATLPA : a method that uses Tolerant Logit and encourages attention map for pairs of examples to be similar.
When applied to clean examples and their adversarial counterparts, ATLPA improves accuracy on adversarial examples over adversarial training.
Instead of constraining a hard distribution in adversarial training, Tolerant Logit consists of confidence distribution on top-k classes and captures inter-class similarities at the image level.
• We explain the reason why our ATLPA can improve the robustness of the model from three dimensions: average activations on discriminate parts, the diversity among learned features of different classes and trends of loss landscapes.
• We show that our ATLPA achieves the state of the art defense on a wide range of datasets against strong PGD gray-box and black-box attacks.
Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation ∈ {0.25, 0.5} i.e. L ∞ ∈ {0.25, 0.5} with 10 to 200 attack iterations.
To our knowledge, such a strong attack has not been previously explored on a wide range of datasets.
The rest of the paper is organized as follows: in Section 2 related works are summarized, in Section 3 definitions and threat models are introduced, in Section 4 our ATLPA is introduced, in Section 5 experimental results are presented and discussed, and finally in Section 6 the paper is concluded.
2 RELATED WORK evaluate the robustness of nine papers (Buckman et al., 2018; Ma et al., 2018; Guo et al., 2017; Dhillon et al., 2018; Xie et al., 2017; Song et al., 2017; Samangouei et al., 2018; Madry et al., 2017; Na et al., 2017) accepted to ICLR 2018 as non-certified white-box-secure defenses to adversarial examples.
They find that seven of the nine defenses use obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples.Obfuscated gradients provide a limited increase in robustness and can be broken by improved attack techniques they develop.
The only defense they observe that significantly increases robustness to adversarial examples within the threat model proposed is adversarial training (Madry et al., 2017) .
Adversarial training (Goodfellow et al., 2014; Madry et al., 2017; Kannan et al., 2018; Tramèr et al., 2017; Pang et al., 2019) defends against adversarial perturbations by training networks on adversarial images that are generated on-the-fly during training.
For adversarial training, the most relevant work to our study is (Kannan et al., 2018) , which introduce a technique they call Adversarial Logit Pairing(ALP), a method that encourages logits for pairs of examples to be similar.
(Engstrom et al., 2018; Mosbach et al., 2018 ) also put forward different opinions on the robustness of ALP.
Our ATLPA encourages attention map for pairs of examples to be similar.
When applied to clean examples and their adversarial counterparts, ATLPA improves accuracy on adversarial examples over adversarial training.
(Araujo et al., 2019) adds random noise at training and inference time, adds denoising blocks to the model to increase adversarial robustness, neither of the above approaches focuses on the attention map.
Following (Pang et al., 2018; Yang et al., 2018; Pang et al., 2019) , we propose Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level.
In terms of methodologies, our work is also related to deep transfer learning and knowledge distillation problems, the most relevant work to our study are (Zagoruyko & Komodakis, 2016; Li et al., 2019b) , which constrain the L 2 -norm of the difference between their behaviors (i.e., the feature maps of outer layer outputs in the source/target networks).
Our ATLPA constrains attention map for pairs of clean examples and their adversarial counterparts to be similar.
In this paper, we propose a novel regularized adversarial training framework ATLPA a method that uses Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level, and encourages attention map for pairs of examples to be similar.
We show that our ATLPA achieves the state of the art defense on a wide range of datasets against strong PGD gray-box and black-box attacks.
We explain the reason why our ATLPA can improve the robustness of the model from three dimensions: average activations on discriminate parts, the diversity among learned features of different classes and trends of loss landscapes.
The results of visualization and quantitative calculation show that our method is helpful to improve the robustness of the model.

assistant: text: In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention.