Deep learning is vulnerable to adversarial attacks, where carefully-crafted input
perturbations could mislead a well-trained Deep Neural Network (DNN) to produce incorrect …

Deep neural networks are susceptible to small-but-specific adversarial perturbations
capable of deceiving the network. This vulnerability can lead to potentially harmful …

Deep neural networks (DNNs) are increasingly powering high-stakes applications such as
autonomous cars and healthcare; however, DNNs are often treated as" black boxes" in such …

Recent studies show that Deep Neural Networks (DNN) are vulnerable to adversarial
samples that are generated by perturbing correctly classified inputs to cause the …

Deep neural networks are powerful and popular learning models that achieve state-of-the-
art pattern recognition performance on many computer vision, speech, and language …

With the advancement of accelerated hardware in recent years, there has been a surge in
the development and application of intelligent systems. Deep learning systems, in particular …

Deep neural networks (DNNs) are now commonly used in many domains. However, they
are vulnerable to adversarial attacks: carefully-crafted perturbations on data inputs that can …

Adversarial robustness of deep learning models has gained much traction in the last few
years. Various attacks and defenses are proposed to improve the adversarial robustness of …

In recent years Deep Neural Networks (DNNs) have achieved remarkable results and even
showed superhuman capabilities in a broad range of domains. This led people to trust in …

Deep neural networks (DNN) are known to be vulnerable to adversarial attacks. Numerous
efforts either try to patch weaknesses in trained models, or try to make it difficult or costly to …