Add to ORKGWith the growing popularity of artificial intelligence and machine learning, a wide spectrum of attacks against deep learning models have been proposed in the literature. Both the evasion attacks and the poisoning attacks attempt to utilize adversarially altered samples to fool the victim model to misclassify the adversarial sample. While such attacks claim to be or are expected to be stealthy, i.e., imperceptible to human eyes, such claims are rarely evaluated. In this paper, we present the first large-scale study on the stealthiness of adversarial samples used in the attacks against deep learning. We have implemented 20 representative adversarial ML attacks on six popular benchmarking datasets. We evaluate the stealthiness of the attack s...
Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as ima...
As deep learning become more popular and have grown to become crucial components in the daily device...
Machine learning is used in myriad aspects, both in academic research and in everyday life, includin...
We develop and study new adversarial perturbations that enable an attacker to gain control over deci...
Over the last decade, machine learning (ML) and artificial intelligence (AI) solutions have been wid...
Data-driven deep learning tasks for security related applications are gaining increasing popularity ...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Adversarial attacks are considered a potentially serious security threat for machine learning system...
Learning-based pattern classifiers, including deep networks, have shown impressive performance in se...
In recent years, machine learning (ML) has become an important part to yield security and privacy in...
Backdoor attack is a type of serious security threat to deep learning models.An adversary can provid...
With the success of deep learning algorithms in various domains, studying adversarial attacks to sec...
Thesis (Ph.D.)--University of Washington, 2019Deep neural networks have achieved remarkable success ...
Despite the popularity and success of deep learning architectures in recent years, they have shown t...
Göpfert JP, Wersing H, Hammer B. Adversarial attacks hidden in plain sight. 2019.Convolutional neur...
Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as ima...
As deep learning become more popular and have grown to become crucial components in the daily device...
Machine learning is used in myriad aspects, both in academic research and in everyday life, includin...
We develop and study new adversarial perturbations that enable an attacker to gain control over deci...
Over the last decade, machine learning (ML) and artificial intelligence (AI) solutions have been wid...
Data-driven deep learning tasks for security related applications are gaining increasing popularity ...
Deep learning is a machine learning technique that enables computers to learn directly from images, ...
Adversarial attacks are considered a potentially serious security threat for machine learning system...
Learning-based pattern classifiers, including deep networks, have shown impressive performance in se...
In recent years, machine learning (ML) has become an important part to yield security and privacy in...
Backdoor attack is a type of serious security threat to deep learning models.An adversary can provid...
With the success of deep learning algorithms in various domains, studying adversarial attacks to sec...
Thesis (Ph.D.)--University of Washington, 2019Deep neural networks have achieved remarkable success ...
Despite the popularity and success of deep learning architectures in recent years, they have shown t...
Göpfert JP, Wersing H, Hammer B. Adversarial attacks hidden in plain sight. 2019.Convolutional neur...
Deep Neural Networks (DNNs) have achieved great success in a wide range of applications, such as ima...
As deep learning become more popular and have grown to become crucial components in the daily device...
Machine learning is used in myriad aspects, both in academic research and in everyday life, includin...