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/Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057)
/Publisher (Curran Associates\054 Inc\056)
/Language (en\055US)
/Created (2017)
/EventType (Poster)
/Description-Abstract (Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines\056 So far\054 most existing methods only work for classification and are not designed to alter the true performance measure of the problem at hand\056 We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered\054 be it combinatorial and non\055decomposable\056 We successfully apply Houdini to a range of applications such as speech recognition\054 pose estimation and semantic segmentation\056 In all cases\054 the attacks based on Houdini achieve higher success rate than those based on the traditional surrogates used to train the models while using a less perceptible adversarial perturbation\056)
/Producer (PyPDF2)
/Title (Houdini\072 Fooling Deep Structured Visual and Speech Recognition Models with Adversarial Examples)
/Date (2017)
/ModDate (D\07220180212204846\05508\04700\047)
/Published (2017)
/Type (Conference Proceedings)
/firstpage (6977)
/Book (Advances in Neural Information Processing Systems 30)
/Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051)
/Editors (I\056 Guyon and U\056V\056 Luxburg and S\056 Bengio and H\056 Wallach and R\056 Fergus and S\056 Vishwanathan and R\056 Garnett)
/Author (Moustapha M\056 Cisse\054 Yossi Adi\054 Natalia Neverova\054 Joseph Keshet)
/lastpage (6987)
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