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Title: Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

Paper content:
In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification.
However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images.
We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks.
Defense-GAN is trained to model the distribution of unperturbed images.
At inference time, it finds a close output to a given image which does not contain the adversarial changes.
This output is then fed to the classifier.
Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure.
It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples.
We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies.
Despite their outstanding performance on several machine learning tasks, deep neural networks have been shown to be susceptible to adversarial attacks BID20 BID4 .
These attacks come in the form of adversarial examples: carefully crafted perturbations added to a legitimate input sample.
In the context of classification, these perturbations cause the legitimate sample to be misclassified at inference time BID20 BID4 BID16 BID11 .
Such perturbations are often small in magnitude and do not affect human recognition but can drastically change the output of the classifier.Recent literature has considered two types of threat models: black-box and white-box attacks.
Under the black-box attack model, the attacker does not have access to the classification model parameters; whereas in the white-box attack model, the attacker has complete access to the model architecture and parameters, including potential defense mechanisms BID21 BID2 .Various
defenses have been proposed to mitigate the effect of adversarial attacks. These defenses
can be grouped under three different approaches: (1) modifying the training data to make the classifier more robust against attacks, e.g., adversarial training which augments the training data of the classifier with adversarial examples BID20 BID4 , (2) modifying the training procedure of the classifier to reduce the magnitude of gradients, e.g., defensive distillation BID18 , and (3) attempting to remove the adversarial noise from the input samples BID6 BID13 . All of these approaches
have limitations in the sense that they are effective against either white-box attacks or black-box attacks, but not both BID21 BID13 . Furthermore, some of these
defenses are devised with specific attack models in mind and are not effective against new attacks.In this paper, we propose a novel defense mechanism which is effective against both white-box and black-box attacks. We propose to leverage the
representative power of Generative Adversarial Networks (GAN) to diminish the effect of the adversarial perturbation, by "projecting" input images onto the range of the GAN's generator prior to feeding them to the classifier. In the GAN framework, two
models are trained simultaneously in an adversarial setting: a generative model that emulates the data distribution, and a discriminative model that predicts whether a certain input came from real data or was artificially created. The generative model learns
a mapping G from a low-dimensional vector z ∈ R k to the high-dimensional input sample space R n . During training of the GAN,
G is encouraged to generate samples which resemble the training data. It is, therefore, expected
that legitimate samples will be close to some point in the range of G, whereas adversarial samples will be further away from the range of G. Furthermore, "projecting" the adversarial examples onto the range of the generator G can have the desirable effect of reducing the adversarial perturbation. The projected output, computed
using Gradient Descent (GD), is fed into the classifier instead of the original (potentially adversarially modified) image. We empirically demonstrate that
this is an effective defense against both black-box and white-box attacks on two benchmark image datasets.The rest of the paper is organized as follows. We introduce the necessary background
regarding known attack models, defense mechanisms, and GANs in Section 2. Our defense mechanism, which we call
Defense-GAN, is formally motivated and introduced in Section 3. Finally, experimental results, under
different threat models, as well as comparisons to other defenses are presented in Section 4.
In this paper, we proposed Defense-GAN, a novel defense strategy utilizing GANs to enhance the robustness of classification models against black-box and white-box adversarial attacks.
Our method does not assume a particular attack model and was shown to be effective against most commonly considered attack strategies.
We empirically show that Defense-GAN consistently provides adequate defense on two benchmark computer vision datasets, whereas other methods had many shortcomings on at least one type of attack.It is worth mentioning that, although Defense-GAN was shown to be a feasible defense mechanism against adversarial attacks, one might come across practical difficulties while implementing and deploying this method.
The success of Defense-GAN relies on the expressiveness and generative power of the GAN.
However, training GANs is still a challenging task and an active area of research, and if the GAN is not properly trained and tuned, the performance of Defense-GAN will suffer on both original and adversarial examples.
Moreover, the choice of hyper-parameters L and R is also critical to the effectiveness of the defense and it may be challenging to tune them without knowledge of the attack.

assistant: text: Defense-GAN uses a Generative Adversarial Network to defend against white-box and black-box attacks in classification models.