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Title: How Aggressive Can Adversarial Attacks Be: Learning Ordered Top-k Attacks

Paper content:
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks, especially white-box targeted attacks.
This paper studies the problem of how aggressive white-box targeted attacks can be to go beyond widely used Top-1 attacks.
We propose to learn ordered Top-k attacks (k>=1), which enforce the Top-k predicted labels of an adversarial example to be the k (randomly) selected and ordered labels (the ground-truth label is exclusive).
Two methods are presented.
First, we extend the vanilla Carlini-Wagner (C&W) method and use it as a strong baseline.
Second, we present an adversarial distillation framework consisting of two components:
(i) Computing an adversarial probability distribution for any given ordered Top-$k$ targeted labels.
(ii) Learning adversarial examples by minimizing the Kullback-Leibler (KL) divergence between the adversarial distribution and the predicted distribution, together with the perturbation energy penalty.
In computing adversarial distributions, we explore how to leverage label semantic similarities, leading to knowledge-oriented attacks.
In experiments, we test Top-k (k=1,2,5,10) attacks in the ImageNet-1000 val dataset using two popular DNNs trained with the clean ImageNet-1000  train dataset, ResNet-50 and DenseNet-121.
Overall, the adversarial distillation approach obtains the best results, especially by large margin when computation budget is limited..
It reduces the perturbation energy consistently with the same attack success rate on all the four k's, and improve the attack success rate by large margin against the modified C&W method for k=10.
Despite the recent dramatic progress, deep neural networks (DNNs) (LeCun et al., 1998; Krizhevsky et al., 2012; He et al., 2016; Szegedy et al., 2016) trained for visual recognition tasks (e.g., image classification) can be easily fooled by so-called adversarial attacks which utilize visually imperceptible, carefully-crafted perturbations to cause networks to misclassify inputs in arbitrarily chosen ways in the close set of labels used in training (Nguyen et al., 2015; Szegedy et al., 2014; Athalye & Sutskever, 2017; Carlini & Wagner, 2016) , even with one-pixel attacks (Su et al., 2017) .
The existence of adversarial attacks hinders the deployment of DNNs-based visual recognition systems in a wide range of applications such as autonomous driving and smart medical diagnosis in the long-run.
In this paper, we are interested in learning visually-imperceptible targeted attacks under the whitebox setting in image classification tasks.
In the literature, most methods address targeted attacks in the Top-1 manner, in which an adversarial attack is said to be successful if a randomly selected label (not the ground-truth label) is predicted as the Top-1 label with the added perturbation satisfying to be visually-imperceptible.
One question arises,
• The "robustness" of an attack method itself : How far is the attack method able to push the underlying ground-truth label in the prediction of the learned adversarial examples?
Table 1 shows the evaluation results of the "robustness" of different attack methods.
The widely used C&W method (Carlini & Wagner, 2016) does not push the GT labels very far, especially when smaller perturbation energy is aimed using larger search range (e.g., the average rank of the GT label is 2.6 for C&W 9×1000 ).
Consider Top-5, if the ground-truth labels of adversarial examples still largely appear in the Top-5 of the prediction, we may be over-confident about the 100% ASR, (He et al., 2016) .
Please see Sec. 4 for detail of experimental settings.
Method ASR Proportion of GT Labels in Top-k (smaller is better) Average Rank of GT Labels (larger is better)
Top-3 Top-5 Top-10 Top-50 Top-100 C&W9×30 (Carlini & Wagner, 2016) 99.9 36.9 50.5 66.3 90.0 95.1 20.4 C&W9×1000 (Carlini & Wagner, 2016) 100 71.9 87.0 96.1 99.9 100 2.6 FGSM (Goodfellow et al., 2015) 80.7 25.5 37.8 52.8 81.2 89.2 44.2 PGD10 (Madry et al., 2018) 100 3.3 6.7 12 34.7 43.9 306.5 MIFGSM10 (Dong et al., 2018) 99.9 0.7 1.9 6.0 22.5 32.3 404.4
especially when some downstream modules may rely on Top-5 predictions in their decision making.
But, the three untargeted attack approaches are much better in terms of pushing the GT labels since they are usually move against the GT label explicitly in the optimization, but their perturbation energies are usually much larger.
As we shall show, more "robust" attack methods can be developed by harnessing the advantages of the two types of attack methods.
In addition, the targeted Top-1 attack setting could limit the flexibility of attacks, and may lead to less rich perturbations.
To facilitate explicit control of targeted attacks and enable more "robust" attack methods, one natural solution, which is the focus of this paper, is to develop ordered Top-k targeted attacks which enforce the Top-k predicted labels of an adversarial example to be the k (randomly) selected and ordered labels (k ≥ 1, the GT label is exclusive).
In this paper, we present two methods of learning ordered Top-k attacks.
The basic idea is to design proper adversarial objective functions that result in imperceptible perturbations for any test image through iterative gradient-based back-propagation.
First, we extend the vanilla Carlini-Wagner (C&W) method (Carlini & Wagner, 2016) and use it as a strong baseline.
Second, we present an adversarial distillation (AD) framework consisting of two components: (i) Computing an adversarial probability distribution for any given ordered Top-k targeted labels.
(ii) Learning adversarial examples by minimizing the Kullback-Leibler (KL) divergence between the adversarial distribution and the predicted distribution, together with the perturbation energy penalty.
The proposed AD framework can be viewed as applying the network distillation frameworks (Hinton et al., 2015; Bucila et al., 2006; Papernot et al., 2016) for "the bad" induced by target adversarial distributions.
To compute a proper adversarial distribution for any given ordered Top-k targeted labels, the AD framework is motivated by two aspects:
(i) The difference between the objective functions used by the C&W method and the three untargeted attack methods (Table 1) respectively.
The former maximizes the margin of the logits between the target and the runner-up (either GT or ResNet-50.
AD is better than the modified C&W method (CW * ).
The thickness represents the 2 energy (thinner is better).
Please see Sec. 4 for detail of experimental settings.
not), while the latter maximizes the cross-entropy between the prediction probabilities (softmax of logits) and the one-hot distribution of the ground-truth.
(ii) The label smoothing methods Pereyra et al., 2017) , which are often used to improve the performance of DNNs by addressing the over-confidence issue in the one-hot vector encoding of labels.
More specifically, we explore how to leverage label semantic similarities in computing "smoothed" adversarial distributions, leading to knowledge-oriented attacks.
We measure label semantic similarities using the cosine distance between some off-the-shelf word2vec embedding of labels such as the pretrained Glove embedding (Pennington et al., 2014) .
Along this direction, another question of interest is further investigated: Are all Top-k targets equally challenging for an attack approach?
In experiments, we test Top-k (k = 1, 2, 5, 10) in the ImageNet-1000 (Russakovsky et al., 2015) val dataset using two popular DNNs trained with clean ImageNet-1000 train dataset, ResNet-50 (He et al., 2016) and DenseNet-121 (Huang et al., 2017) respectively.
Overall, the adversarial distillation approach obtains the best results.
It reduces the perturbation energy consistently with the same attack success rate on all the four k's, and improve the attack success rate by large margin against the modified C&W method for k = 10 (see Fig. 1 ).
We observe that Top-k targets that are distant from the GT label in terms of either label semantic distance or prediction scores of clean images are actually more difficulty to attack.
In summary, not only can ordered Top-k attacks improve the "robustness" of attacks, but also they provide insights on how aggressive adversarial attacks can be (under affordable optimization budgets).
Our Contributions.
This paper makes three main contributions to the field of learning adversarial attacks:
(i) The problem in study is novel.
Learning ordered Top-k adversarial attacks is an important problem that reflects the robustness of attacks themselves, but has not been addressed in the literature.
(ii) The proposed adversarial distillation framework is effective, especially when k is large (such as k = 5, 10).
(iii) The proposed knowledge-oriented adversarial distillation is novel.
It worth exploring the existing distillation framework for a novel problem (ordered Top-k adversarial attacks) with some novel modifications (knowledge-oriented target distributions as "teachers").
This paper proposes to extend the traditional Top-1 targeted attack setting to the ordered Top-k setting (k ≥ 1) under the white-box attack protocol.
The ordered Top-k targeted attacks can improve the robustness of attacks themselves.
To our knowledge, it is the first work studying this ordered Top-k attacks.
To learn the ordered Top-k attacks, we present a conceptually simple yet effective adversarial distillation framework motivated by network distillation.
We also develop a modified C&W method as the strong baseline for the ordered Top-k targeted attacks.
In experiments, the proposed method is tested in ImageNet-1000 using two popular DNNs, ResNet-50 and DenseNet-121, with consistently better results obtained.
We investigate the effectiveness of label semantic knowledge in designing the adversarial distribution for distilling the ordered Top-k targeted attacks.
Discussions.
We have shown that the proposed AD method is generally applicable to learn ordered Top-k attacks.
But, we note that the two components of the AD framework are in their simplest forms in this paper, and need to be more thoroughly studied: designing more informative adversarial distributions to guide the optimization to learn adversarial examples better and faster, and investigating loss functions other than KL divergence such as the Jensen-Shannon (JS) divergence or the Earth-Mover distance.
On the other hand, we observed that the proposed AD method is more effective when computation budget is limited (e.g., using the 9 × 30 search scheme).
This leads to the theoretically and computationally interesting question whether different attack methods all will work comparably well if the computation budget is not limited.
Of course, in practice, we prefer more powerful ones when only limited computation budget is allowed.
Furthermore, we observed that both the modified C&W method and the AD method largely do not work in learning Top-k (k ≥ 20) attacks with the two search schema (9 × 30 and 9 × 1000).
We are working on addressing the aforementioned issues to test the Top-k (k ≥ 20) cases, thus providing a thorough empirical answer to the question: how aggressive can adversarial attacks be?

assistant: text: ordered Top-k adversarial attacks