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Creators/Authors contains: "Murtagh, Jack"

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  1. ; ; ; (, Theory of Computing)
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  2. ; ; ; (, Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques)
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  3. ; ; ; ; ; (, 2020 IEEE 61st Annual Symposium on Foundations of Computer Science (FOCS))
    null (Ed.)
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  4. ; ; ; (, 47th International Colloquium on Automata, Languages, and Programming (ICALP 2020))
    null (Ed.)
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  5. ; ; (, Advances in neural information processing systems)
    We give a new algorithm for approximating the Discrete Fourier transform of an approximately sparse signal that has been corrupted by worst-case L0 noise, namely a bounded number of coordinates of the signal have been corrupted arbitrarily. Our techniques generalize to a wide range of linear transformations that are used in data analysis such as the Discrete Cosine and Sine transforms, the Hadamard transform, and their high-dimensional analogs. We use our algorithm to successfully defend against well known L0 adversaries in the setting of image classification. We give experimental results on the Jacobian-based Saliency Map Attack (JSMA) and the Carlini Wagner (CW) L0 attack on the MNIST and Fashion-MNIST datasets as well as the Adversarial Patch on the ImageNet dataset. 
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