ARMOURED: Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity

Adversarial attacks pose a major challenge for modern deep neural networks . Recent advancements show that adversarially robust generalization requires a large amount of labeled data for training . If annotation becomes a burden , can unlabeled data help bridge the gap ? In this paper , we propose ARMOURED , an adversarially robust training method based on semi-supervised learning that consists of two components . The first component applies multi-view learning to simultaneously optimize multiple independent networks and utilizes unlabeled data to enforce labeling consistency . The second component reduces adversarial transferability among the networks via diversity regularizers inspired by determinantal point processes and entropy maximization . Experimental results show that under small perturbation budgets , ARMOURED is robust against strong adaptive adversaries . Notably , ARMOURED does not rely on generating adversarial samples during training . When used in combination with adversarial training , ARMOURED yields competitive performance with the state-of-the-art adversariallyrobust benchmarks on SVHN and outperforms them on CIFAR-10 , while offering higher clean accuracy . 1 INTRODUCTION . Modern deep neural networks have met or even surpassed human-level performance on a variety of image classification tasks . However , they are vulnerable to adversarial attacks , where small , calculated perturbations in the input sample can fool a network into making unintended behaviors , e.g. , misclassification . ( Szegedy et al. , 2014 ; Biggio et al. , 2013 ) . Such adversarial attacks have been found to transfer between different network architectures ( Papernot et al. , 2016 ) and are a serious concern , especially when neural networks are used in real-world applications . As a result , much work has been done to improve the robustness of neural networks against adversarial attacks ( Miller et al. , 2020 ) . Of these techniques , adversarial training ( AT ) ( Goodfellow et al. , 2015 ; Madry et al. , 2018 ) is widely used and has been found to provide the most robust models in recent evaluation studies ( Dong et al. , 2020 ; Croce & Hein , 2020 ) . Nonetheless , even models trained with AT have markedly reduced performance on adversarial samples in comparison to clean samples . Models trained with AT also have worse accuracy on clean samples when compared to models trained with standard classification losses . Schmidt et al . ( 2018 ) suggest that one reason for such reductions in model accuracy is that training adversarially robust models requires substantially more labeled data . Due to the high costs of obtaining such labeled data in real-world applications , recent work has explored semi-supervised AT-based approaches that are able to leverage unlabeled data instead ( Uesato et al. , 2019 ; Najafi et al. , 2019 ; Zhai et al. , 2019 ; Carmon et al. , 2019 ) . ∗Equal contribution . †Corresponding author . Orthogonal to AT-based approaches that focus on training robust single models , a few works have explored the use of diversity regularization for learning adversarially robust classifiers . These works rely on encouraging ensemble diversity through regularization terms , whether on model predictions ( Pang et al. , 2019 ) or model gradients ( Dabouei et al. , 2020 ) , guided by the intuition that diversity amongst the model ensemble will make it difficult for adversarial attacks to transfer between individual models , thus making the ensemble as a whole more resistant to attack . In this work , we propose ARMOURED : Adversarially Robust MOdels using Unlabeled data by REgularizing Diversity , a novel algorithm for adversarially robust model learning that elegantly unifies semi-supervised learning and diversity regularization through a multi-view learning framework . ARMOURED applies a pseudo-label filter similar to co-training ( Blum & Mitchell , 1998 ) to enforce consistency of different networks ’ predictions on the unlabeled data . In addition , we derive a regularization term inspired by determinantal point processes ( DPP ) ( Kulesza & Taskar , 2012 ) that encourages the two networks to predict differently for non-target classes . Lastly , ARMOURED maximizes the entropy of the combined multi-view output on the non-target classes . We show in empirical evaluations that ARMOURED achieves state-of-the-art robustness against strong adaptive adversaries as long as the perturbations are within small ` ∞ or ` 2 norm-bounded balls . Notably , unlike previous semi-supervised methods , ARMOURED does not use adversarial samples during training . When used in combination with AT , ARMOURED is competitive with the state-of-the-art methods on SVHN and outperforms them on CIFAR-10 , while offering higher clean accuracy . In summary , the major contributions of this work are as follows : 1 . We propose ARMOURED , a novel semi-supervised method based on multi-view learning and diversity regularization for training adversarially robust models . 2 . We perform an extensive comparison , including standard semi-supervised learning approaches in addition to methods for learning adversarially robust models . 3 . We show that ARMOURED+AT achieves state-of-the-art adversarial robustness while maintaining high accuracy on clean data . 2 RELATED WORK . To set the stage for ARMOURED , in this section , we briefly review adversarially robust learning and semi-supervised learning - two paradigms in the literature that are related to our work . 2.1 ADVERSARIALLY ROBUST LEARNING . Adversarial attacks : We consider attacks where adversarial samples stay within a ` p ball with fixed radius around the clean sample . In this setting , the two standard white-box attacks are the Fast Gradient Sign Method ( FGSM ) ( Goodfellow et al. , 2015 ) that computes a one-step perturbation that maximizes the cross entropy loss function , and Projected Gradient Descent ( PGD ) ( Madry et al. , 2018 ) , a stronger attack that performs multiple iterations of gradient updates to maximize the loss ; this may be seen as a multi-step version of FGSM . Auto-PGD attack ( APGD ) ( Croce & Hein , 2020 ) is a parameter-free , budget-aware variant of PGD which aims at better convergence . However , robustness against these gradient-based attacks may give a false sense of security due to gradient-masking . This phenomenon happens when the defense does not produce useful gradients to generate adversarial samples ( Athalye et al. , 2018 ) . Gradient-masking is known to affect PGD by preventing its convergence to the actual adversarial samples ( Tramèr & Boneh , 2019 ) . There exists gradient-based attacks such as Fast Adaptive Boundary attack ( Croce & Hein , 2019 ) ( FAB ) which is invariant to rescaling , thus is unaffected by gradient-masking . FAB minimizes the perturbation norm as long as misclassification is achieved . Black box attacks that rely on random search alone without gradient information , such as Square attack ( Andriushchenko et al. , 2020 ) , are also unaffected by gradient masking . Finally , AutoAttack ( Croce & Hein , 2020 ) is a strong ensemble adversary which applies four attacks sequentially ( APGD with cross entropy loss , followed by targeted APGD with difference-of-logits-ratio loss , targeted FAB , then Square ) . Adversarial training : Adversarial training ( AT ) is a popular approach that performs well in practice ( Dong et al. , 2020 ) . Madry et al . ( 2018 ) formulate AT as a min-max problem , where the model is trained with adversarial samples found via PGD . Variants of this method such as TRADES ( Zhang et al. , 2019b ) and ALP ( Kannan et al. , 2018 ) further decompose the error into natural error and boundary error for higher robustness . Zhang et al . ( 2019a ) ; Wang et al . ( 2019 ) theoretically prove the convergence of AT . Two drawbacks of AT are its slow training due to adversarial example generation requiring multiple gradient computations , and the significant reduction in model accuracy on clean samples . Several recent works have focused on speeding up AT ( Zhang et al. , 2019a ; Qin et al. , 2019 ; Shafahi et al. , 2019 ) ; ARMOURED addresses the second limitation , enabling significantly improved performance on clean samples . Semi-supervised adversarial training : Schmidt et al . ( 2018 ) showed that adversarial robust generalization requires much more labeled data . To relieve the annotation burden , several semi-supervised adversarially robust learning ( SSAR ) methods have been developed to exploit unlabeled data instead . Uesato et al . ( 2019 ) introduced unsupervised adversarial training , a simple self-training model which optimizes a smoothness loss and a classification loss using pseudo-labels . Carmon et al . ( 2019 ) revisited the Gaussian model by Schmidt et al . ( 2018 ) and introduced robust self-training ( RST ) , another self-training model that computes a regularization loss from unlabeled data , either via adversarial training or stability training . Zhai et al . ( 2019 ) applied a generalized virtual adversarial training to optimize the prediction stability of their model in the presence of perturbations . Najafi et al . ( 2019 ) proposed a semi-supervised extension of the distributionally robust optimization framework by Sinha et al . ( 2018 ) . They replace pseudo-labels with soft-labels for unlabeled data and train them together with labeled data . It is worth noting that all of these four state-of-the-art SSAR methods apply AT in their training procedure . Diversity regularization : Diversity regularization is an orthogonal direction to AT that has the potential to further improve the performance of AT . In earlier work , Pang et al . ( 2018 ) showed that for a single network , adversarial robustness can be improved when the features learned for different classes are diverse . Pang et al . ( 2019 ) further developed this concept by introducing Adaptive Promoting Diversity regularization ( ADP ) . Given an ensemble of neural network classifiers , ADP promotes diversity among non-target predictions of the networks . ADP is inspired by determinantal point processes ( Hough et al. , 2006 ) , an elegant statistical tool to model repulsive interactions among items of a fixed ground set ; applications to machine learning are reviewed in ( Kulesza & Taskar , 2012 ) . Dabouei et al . ( 2020 ) enforce diversity on the gradients of individual networks in the ensemble instead of their predictions . We note that unlike ARMOURED , the methods described here are developed for the fully-supervised setting , and are not able to utilize unlabeled data . 2.2 SEMI-SUPERVISED LEARNING . Semi-supervised learning : Semi-supervised learning ( SSL ) is an effective strategy to learn from low-cost unlabeled data . There is considerable recent work in this practically relevant and active research area ; we will not be able to cover all these works here . Existing SSL methods can be broadly categorized into three groups : consistency-based , graph-based , and generative models . Recent methods , such as Mean Teacher ( Tarvainen & Valpola , 2017 ) and MixMatch ( Berthelot et al. , 2019 ) , are consistency-based as this approach can be adapted to generic problems and have superior performance in practice . The key idea behind consistency-based methods is that model predictions on different augmentations of the same input should be consistent . Multi-view learning : Multi-view learning is a SSL paradigm that is capable of representing diversity in addition to consistency . A dataset is considered to have multiple views when its data samples are represented by more than one set of features and each set is sufficient for the learning task . In this setting , a multi-view method assigns one modeling function to each view and jointly optimizes the functions to improve generalization performance ( Zhao et al. , 2017 ) . By analyzing various multi-view algorithms , Xu et al . ( 2013 ) summarized consensus and complementary as the two underpinning principles of multi-view learning . The consensus principle states that a multiview technique must aim at maximizing the prediction agreement on different views , similar to the consistency-based SSL methods discussed above . The complementary principle states that in order to make improvement , each view must contain some information that the other views do not carry , that the views should be sufficiently diverse . This principle has been applied to boost generalization capability in regular SSL ( Qiao et al. , 2018 ) and learning with label noise ( Han et al. , 2018 ) . In this paper , we argue that multi-view complementarity also plays a critical role in improving adversarial robustness , by reducing the transferability of adversarial attacks across different views .

Summary: This paper considers training an adversarially robust model in a semi-supervised setting. The authors propose an ensemble-based algorithm for this goal. The algorithm uses a regularization term to induce diversity in the ensemble. The algorithm also leverages the idea of multi-view training in semi-supervised learning to make use of unlabeled data. The experimental results show that the proposed algorithm outperforms several baselines.