Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification. It was recently shown that given a dataset and classifier, there exists so called universal adversarial perturbations, a single perturbation that causes a misclassification when applied to any input. In this work, we introduce universal adversarial networks, a generative network that is capable of fooling a target classifier when it's generated output is added to a clean sample from a dataset. We show that this technique improves on known universal adversarial attacks
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image...
State-of-the-art deep networks for image classification are vulnerable to adversarial examples—miscl...
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples—perturbe...
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentiona...
Machine learning models are susceptible to adversarial perturbations: small changes to input that ca...
Machine learning classification models are vulnerable to adversarial examples -- effective input-spe...
Deep Neural Networks have been found vulnerable re-cently. A kind of well-designed inputs, which cal...
Image classification systems are known to be vulnerable to adversarial attacks, which are impercepti...
Adversarial attacks in image classification are optimization problems that estimate the minimum pert...
The literature on adversarial attacks in computer vision typically focuses on pixel-level perturbati...
The previous study has shown that universal adversarial attacks can fool deep neural networks over a...
A well-trained neural network is very accurate when classifying data into different categories. Howe...
In adversarial attacks intended to confound deep learning models, most studies have focused on limit...
Deep learning has improved the performance of many computer vision tasks. However, the features that...
Standard adversarial attacks change the predicted class label of a selected image by adding speciall...
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image...
State-of-the-art deep networks for image classification are vulnerable to adversarial examples—miscl...
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples—perturbe...
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentiona...
Machine learning models are susceptible to adversarial perturbations: small changes to input that ca...
Machine learning classification models are vulnerable to adversarial examples -- effective input-spe...
Deep Neural Networks have been found vulnerable re-cently. A kind of well-designed inputs, which cal...
Image classification systems are known to be vulnerable to adversarial attacks, which are impercepti...
Adversarial attacks in image classification are optimization problems that estimate the minimum pert...
The literature on adversarial attacks in computer vision typically focuses on pixel-level perturbati...
The previous study has shown that universal adversarial attacks can fool deep neural networks over a...
A well-trained neural network is very accurate when classifying data into different categories. Howe...
In adversarial attacks intended to confound deep learning models, most studies have focused on limit...
Deep learning has improved the performance of many computer vision tasks. However, the features that...
Standard adversarial attacks change the predicted class label of a selected image by adding speciall...
Given a state-of-the-art deep neural network classifier, we show the existence of a universal (image...
State-of-the-art deep networks for image classification are vulnerable to adversarial examples—miscl...
Deep Convolutional Networks (DCNs) have been shown to be vulnerable to adversarial examples—perturbe...