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Title: Defending Against Adversarial Examples by Regularized Deep Embedding

Paper content:
Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.
Inspired by the observation that the intrinsic dimension of image data is much smaller than its pixel space dimension and the vulnerability of neural networks grows with the input dimension, we propose to embed high-dimensional input images into a low-dimensional space to perform classification.
However, arbitrarily projecting the input images to a low-dimensional space without regularization will not improve the robustness of deep neural networks.
We propose a new framework, Embedding Regularized Classifier (ER-Classifier), which improves the adversarial robustness of the classifier through embedding regularization.
Experimental results on several benchmark datasets show that, our proposed framework achieves state-of-the-art performance against strong adversarial attack methods.
Deep neural networks (DNNs) have been widely used for tackling numerous machine learning problems that were once believed to be challenging.
With their remarkable ability of fitting training data, DNNs have achieved revolutionary successes in many fields such as computer vision, natural language progressing, and robotics.
However, they were shown to be vulnerable to adversarial examples that are generated by adding carefully crafted perturbations to original images.
The adversarial perturbations can arbitrarily change the network's prediction but often too small to affect human recognition (Szegedy et al., 2013; Kurakin et al., 2016) .
This phenomenon brings out security concerns for practical applications of deep learning.
Two main types of attack settings have been considered in recent research (Goodfellow et al.; Carlini & Wagner, 2017a; Chen et al., 2017; Papernot et al., 2017) : black-box and white-box settings.
In the black-box setting, the attacker can provide any inputs and receive the corresponding predictions.
However, the attacker cannot get access to the gradients or model parameters under this setting; whereas in the white-box setting, the attacker is allowed to analytically compute the model's gradients, and have full access to the model architecture and weights.
In this paper, we focus on defending against the white-box attack which is the harder task.
Recent work (Simon-Gabriel et al., 2018) presented both theoretical arguments and an empirical one-to-one relationship between input dimension and adversarial vulnerability, showing that the vulnerability of neural networks grows with the input dimension.
Therefore, reducing the data dimension may help improve the robustness of neural networks.
Furthermore, a consensus in the highdimensional data analysis community is that, a method working well on the high-dimensional data is because the data is not really of high-dimension (Levina & Bickel, 2005) .
These high-dimensional data, such as images, are actually embedded in a low dimensional space.
Hence, carefully reducing the input dimension may improve the robustness of the model without sacrificing performance.
Inspired by the observation that the intrinsic dimension of image data is actually much smaller than its pixel space dimension (Levina & Bickel, 2005) and the vulnerability of a model grows with its input dimension (Simon-Gabriel et al., 2018) , we propose a defense framework that embeds input images into a low-dimensional space using a deep encoder and performs classification based on the latent embedding with a classifier network.
However, an arbitrary projection does not guarantee improving the robustness of the model, because there are a lot of mapping functions including non-robust ones from the raw input space to the low-dimensional space capable of minimizing the classification loss.
To constrain the mapping function, we employ distribution regularization in the embedding space leveraging optimal transport theory.
We call our new classification framework Embedding Regularized Classifier (ER-Classifier).
To be more specific, we introduce a discriminator in the latent space which tries to separate the generated code vectors from the encoder network and the ideal code vectors sampled from a prior distribution, i.e., a standard Gaussian distribution.
Employing a similar powerful competitive mechanism as demonstrated by Generative Adversarial Networks (Goodfellow et al., 2014) , the discriminator enforces the embedding space of the model to follow the prior distribution.
In our ER-Classifier framework, the encoder and discriminator structures together project the input data to a low-dimensional space with a nice shape, then the classifier performs prediction based on the lowdimensional embedding.
Based on the optimal transport theory, the proposed ER-Classifier minimizes the discrepancy between the distribution of the true label and the distribution of the framework output, thus only retaining important features for classification in the embedding space.
With a small embedding dimension, the effect of the adversarial perturbation is largely diminished through the projection process.
We compare ER-Classifier with other state-of-the-art defense methods on MNIST, CIFAR10, STL10 and Tiny Imagenet.
Experimental results demonstrate that our proposed ER-Classifier outperforms other methods by a large margin.
To sum up, this paper makes the following three main contributions:
• A novel unified end-to-end robust deep neural network framework against adversarial attacks is proposed, where the input image is first projected to a low-dimensional space and then classified.
• An objective is induced to minimize the optimal transport cost between the true class distribution and the framework output distribution, guiding the encoder and discriminator to project the input image to a low-dimensional space without losing important features for classification.
• Extensive experiments demonstrate the robustness of our proposed ER-Classifier framework under the white-box attacks, and show that ER-Classifier outperforms other state-ofthe-art approaches on several benchmark image datasets.
As far as we know, our approach is the first that applies optimal transport theory, i.e., a Wasserstein distance regularization, to a bottleneck embedding layer of a deep neural network in a purely supervised learning setting without considering any reconstruction loss, although optimal transport theory or a discriminator loss has been applied to generative models in an unsupervised learning setting (Makhzani et al., 2015; Tolstikhin et al., 2017) ; (2) Our method is also the first that establishes the connection between a Wasserstein distance regularization and the robustness of deep neural networks for defending against adversarial examples.
In this paper, we propose a new defense framework, ER-Classifier, which projects the input images to a low-dimensional space to remove adversarial perturbation and stabilize the model through minimizing the discrepancy between the true label distribution and the framework output distribution.
We empirically show that ER-Classifier is much more robust than other state-of-the-art defense methods on several benchmark datasets.
Future work will include further exploration of the low-dimensional space to improve the robustness of deep neural network.

assistant: text: A general and easy-to-use framework that improves the adversarial robustness of deep classification models through embedding regularization.