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August 17 2018

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July 04 2018

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Adversarial Reprogramming of Neural Networks


Abstract: Deep neural networks are susceptible to adversarial attacks. In computer vision, well-crafted perturbations to images can cause neural networks to make mistakes such as identifying a panda as a gibbon or confusing a cat with a computer. Previous adversarial examples have been designed to degrade performance of models or cause machine learning models to produce specific outputs chosen ahead of time by the attacker. We introduce adversarial attacks that instead reprogram the target model to perform a task chosen by the attacker—without the attacker needing to specify or compute the desired output for each test-time input. This attack is accomplished by optimizing for a single adversarial perturbation, of unrestricted magnitude, that can be added to all test-time inputs to a machine learning model in order to cause the model to perform a task chosen by the adversary when processing these inputs—even if the model was not trained to do this task. These perturbations can be thus considered a program for the new task. We demonstrate adversarial reprogramming on six ImageNet classification models, repurposing these models to perform a counting task, as well as two classification tasks: classification of MNIST and CIFAR-10 examples presented within the input to the ImageNet model.
https://arxiv.org/pdf/1806.11146.pdf ;
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July 03 2018

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July 02 2018

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June 30 2018

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Play fullscreen
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June 29 2018

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June 28 2018

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June 27 2018

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June 26 2018

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June 25 2018

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June 24 2018

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June 20 2018

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