Add to ORKGDeep Neural Networks have been found vulnerable re-cently. A kind of well-designed inputs, which called adver-sarial examples, can lead the networks to make incorrectpredictions. Depending on the different scenarios, goalsand capabilities, the difficulties of the attacks are different.For example, a targeted attack is more difficult than a non-targeted attack, a universal attack is more difficult than anon-universal attack, a transferable attack is more difficultthan a nontransferable one. The question is: Is there existan attack that can meet all these requirements? In this pa-per, we answer this question by producing a kind of attacksunder these conditions. We learn a universal mapping tomap the sources to the adversarial examples. These ...
The previous study has shown that universal adversarial attacks can fool deep neural networks over a...
Existing transfer attack methods commonly assume that the attacker knows the training set (e.g., the...
With the rapidly increasing popularity of deep neural networks for image recognition tasks, a parall...
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention i...
Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applicat...
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neu...
Albeit displaying remarkable performance across a range of tasks, Deep Neural Networks (DNNs) are hi...
Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptib...
Deep learning has improved the performance of many computer vision tasks. However, the features that...
Deep learning models are known to be vulnerable not only to input-dependent adversarial attacks but ...
Transfer-based adversarial attacks can evaluate model robustness in the black-box setting. Several m...
Deep neural networks are vulnerable to attacks from adversarial inputs and, more recently, Trojans t...
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentiona...
Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networ...
Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networ...
The previous study has shown that universal adversarial attacks can fool deep neural networks over a...
Existing transfer attack methods commonly assume that the attacker knows the training set (e.g., the...
With the rapidly increasing popularity of deep neural networks for image recognition tasks, a parall...
Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention i...
Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applicat...
Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neu...
Albeit displaying remarkable performance across a range of tasks, Deep Neural Networks (DNNs) are hi...
Deep neural networks are vulnerable to adversarial examples that are crafted by imposing imperceptib...
Deep learning has improved the performance of many computer vision tasks. However, the features that...
Deep learning models are known to be vulnerable not only to input-dependent adversarial attacks but ...
Transfer-based adversarial attacks can evaluate model robustness in the black-box setting. Several m...
Deep neural networks are vulnerable to attacks from adversarial inputs and, more recently, Trojans t...
Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentiona...
Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networ...
Convolutional Neural Networks (CNNs) models are one of the most frequently used deep learning networ...
The previous study has shown that universal adversarial attacks can fool deep neural networks over a...
Existing transfer attack methods commonly assume that the attacker knows the training set (e.g., the...
With the rapidly increasing popularity of deep neural networks for image recognition tasks, a parall...