CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning
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CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning
This paper proposes CuRTAIL, an endtoend computing framework for characterizing and thwarting adversarial space in the context of Deep Learning (DL). The framework protects deep neural networks against adversarial samples, which are perturbed inputs carefully crafted by malicious entities to mislead the underlying DL model. The precursor for the proposed methodology is a set of new quantitative metrics to assess the vulnerability of various deep learning architectures to adversarial samples. CuRTAIL formalizes the goal of preventing adversarial samples as a minimization of the space unexplored by the pertinent DL model that is characterized in CuRTAIL vulnerability analysis step. To thwart the adversarial machine learning attack, CuRTAIL introduces the concept of Modular Robust Redundancy (MRR) as a viable solution to achieve the formalized minimization objective. The MRR methodology explicitly characterizes the geometry of the input data and the DL model parameters. It then learns a set of complementary but disjoint models which maximally cover the unexplored subspaces of the target DL model, thus reducing the risk of integrity attacks. We extensively evaluate CuRTAIL performance against the stateoftheart attack models including fastsigngradient, Jacobian Saliency Map Attack, and Deepfool. Proofofconcept implementations for analyzing various data collections including MNIST, CIFAR10, and ImageNet corroborate CuRTAIL effectiveness to detect adversarial samples in different settings. The computations in each MRR module can be performed independently. As such, CuRTAIL detection algorithm can be completely parallelized among multiple hardware settings to achieve maximum throughput. We further provide an accompanying API to facilitate the adoption of the proposed framework for various applications.
CuRTAIL: ChaRacterizing and Thwarting AdversarIal deep Learning
by Bita Darvish Rouhani, Mohammad Samragh, Tara Javidi, Farinaz Koushanfar
https://arxiv.org/pdf/1709.02538v1.pdf
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