In the process, it outperforms adversarial training, the most noteworthy defense mechanism,byawidemargin.

In the process, it outperforms adversarial training, the most noteworthy defense mechanism,byawidemargin.

Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models, and in many settings the adversarial perturbation need not be bounded by small $\ell_p$-norms. Motivated by attacks in program analysis and security tasks, we investigate $\textit{relational adversaries}$, a broad class of attackers who create adversarial examples in a reflexive-transitive closure of a logical relation. We analyze the conditions for robustness against relational adversaries and investigate different levels of robustness-accuracy trade-off due to various patterns in a relation. Inspired by the insights, we propose $\textit{normalize-and-predict}$, a learning framework that leverages input normalization to achieve provable robustness. The framework solves the pain points of adversarial training against relational adversaries and can be combined with adversarial training for the benefits of both approaches. Guided by our theoretical findings, we apply our framework to source code authorship attribution and malware detection. Results of both tasks show our learning framework significantly improves the robustness of models against relational adversaries. In the process, it outperforms adversarial training, the most noteworthy defense mechanism, by a wide margin.

Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models, and in many settings the adversarial perturbation need not be bounded by small \ell_p -norms. Motivated by attacks in program analysis and security tasks, we investigate \textit{relational adversaries}, a broad class of attackers who create adversarial examples in a reflexive-transitive closure of a logical relation. We analyze the conditions for robustness against relational adversaries and investigate different levels of robustness-accuracy trade-off due to various patterns in a relation.

Test-time adversarial attacks have posed serious challenges to the robustness of machine-learning models, and in many settings the adversarial perturbation need not be bounded by small $\ell_p$-norms. Motivated by the semantics-preserving attacks in vision and security domain, we investigate $\textit{relational adversaries}$, a broad class of attackers who create adversarial examples that are in a reflexive-transitive closure of a logical relation. We analyze the conditions for robustness and propose $\textit{normalize-and-predict}$ -- a learning framework with provable robustness guarantee. We compare our approach with adversarial training and derive an unified framework that provides benefits of both approaches. Guided by our theoretical findings, we apply our framework to image classification and malware detection. Results of both tasks show that attacks using relational adversaries frequently fool existing models, but our unified framework can significantly enhance their robustness.