Add to ORKGGraduation date: 2017Access restricted to the OSU Community at author's request, from September 26, 2016 - March 29, 2017Deep learning has greatly improved visual recognition in recent years. However,\ud recent research has shown that there exist many adversarial examples that can\ud negatively impact the performance of such an architecture. Different from previous\ud perspectives that focus on improving the classifiers to detect the adversarial examples,\ud this work focuses on detecting those adversarial examples by analyzing whether they\ud come from the same distribution as the normal examples. An approach is proposed\ud based on spectral analysis deeply inside the network. The insights gained from such\ud an approach help to develop a ...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
Deep neural networks have been recently achieving high accuracy on many important tasks, most notabl...
Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman perfor...
Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman perfor...
Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman perfor...
Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman perfor...
Deep Neural Networks (DNNs) have demonstrated remarkable performance in a diverse range of applicati...
A recent article in which it is claimed that adversarial examples exist in deep artificial neural ne...
Deep learning technology achieves state of the art result in many computer vision missions. However,...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
International audienceRecent studies have demonstrated that the deep neural networks (DNNs) are vuln...
Deep neural networks have been recently achieving high accuracy on many important tasks, most notabl...
Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman perfor...
Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman perfor...
Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman perfor...
Over the past few years, convolutional neural networks (CNNs) have proved to reach superhuman perfor...
Deep Neural Networks (DNNs) have demonstrated remarkable performance in a diverse range of applicati...
A recent article in which it is claimed that adversarial examples exist in deep artificial neural ne...
Deep learning technology achieves state of the art result in many computer vision missions. However,...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...
Adversarial examples, generated by adding small but intentionally imperceptible perturbations to nor...