Using this approach to provide examples for adversarial training, we reduce the test set error of a maxout network on the MNIST dataset. Ian J. Goodfellow, "Explaining and Harnessing Adversarial Examples" https://arxiv.org/abs/1412.6572 ニ…

Moreover, this view yields a simple and fast method of generating adversarial examples. Ian J. Goodfellow, "Explaining and Harnessing Adversarial Examples" https://arxiv.org/abs/1412.6572 ニューラルネットワークを騙すような入力となるAdversa…

This explanation is supported by new quantitative results while giving the first explanation of the most intriguing fact about them: their generalization across architectures and training sets. Ian J. Goodfellow, "Explaining and Harnessing…

We argue instead that the primary cause of neural networks’ vulnerability to adversarial perturbation is their linear nature. Ian J. Goodfellow, "Explaining and Harnessing Adversarial Examples" https://arxiv.org/abs/1412.6572 ニューラルネ…

Early attempts at explaining this phenomenon focused on nonlinearity and overfitting. Ian J. Goodfellow, "Explaining and Harnessing Adversarial Examples" https://arxiv.org/abs/1412.6572 ニューラルネットワークを騙すような入力となるAdversari…

Several machine learning models, including neural networks, consistently misclassify adversarial examples—inputs formed by applying small but intentionally worst-case perturbations to examples from the dataset, such that the perturbed inpu…