Yan Zhou

Vibha Belavadi, Yan Zhou, Murat Kantarcioglu et al.

As machine learning (ML) techniques are being increasingly used in many applications, their vulnerability to adversarial attacks becomes well-known. Test time attacks, usually launched by adding adversarial noise to test instances, have been shown effective against the deployed ML models. In practice, one test input may be leveraged by different ML models. Test time attacks targeting a single ML model often neglect their impact on other ML models. In this work, we empirically demonstrate that naively attacking the classifier learning one concept may negatively impact classifiers trained to learn other concepts. For example, for the online image classification scenario, when the Gender classifier is under attack, the (wearing) Glasses classifier is simultaneously attacked with the accuracy dropped from 98.69 to 88.42. This raises an interesting question: is it possible to attack one set of classifiers without impacting the other set that uses the same test instance? Answers to the above research question have interesting implications for protecting privacy against ML model misuse. Attacking ML models that pose unnecessary risks of privacy invasion can be an important tool for protecting individuals from harmful privacy exploitation. In this paper, we address the above research question by developing novel attack techniques that can simultaneously attack one set of ML models while preserving the accuracy of the other. In the case of linear classifiers, we provide a theoretical framework for finding an optimal solution to generate such adversarial examples. Using this theoretical framework, we develop a multi-concept attack strategy in the context of deep learning. Our results demonstrate that our techniques can successfully attack the target classes while protecting the protected classes in many different settings, which is not possible with the existing test-time attack-single strategies.

Yan Zhou, Murat Kantarcioglu, Bowei Xi

We investigate the role of transferability of adversarial attacks in the observed vulnerabilities of Deep Neural Networks (DNNs). We demonstrate that introducing randomness to the DNN models is sufficient to defeat adversarial attacks, given that the adversary does not have an unlimited attack budget. Instead of making one specific DNN model robust to perfect knowledge attacks (a.k.a, white box attacks), creating randomness within an army of DNNs completely eliminates the possibility of perfect knowledge acquisition, resulting in a significantly more robust DNN ensemble against the strongest form of attacks. We also show that when the adversary has an unlimited budget of data perturbation, all defensive techniques would eventually break down as the budget increases. Therefore, it is important to understand the game saddle point where the adversary would not further pursue this endeavor. Furthermore, we explore the relationship between attack severity and decision boundary robustness in the version space. We empirically demonstrate that by simply adding a small Gaussian random noise to the learned weights, a DNN model can increase its resilience to adversarial attacks by as much as 74.2%. More importantly, we show that by randomly activating/revealing a model from a pool of pre-trained DNNs at each query request, we can put a tremendous strain on the adversary's attack strategies. We compare our randomization techniques to the Ensemble Adversarial Training technique and show that our randomization techniques are superior under different attack budget constraints.