Deep learning has shown impressive performance on hard perceptual problems. However,
researchers found deep learning systems to be vulnerable to small, specially crafted …

Deep neural networks have proven remarkably effective at solving many classification
problems, but have been criticized recently for two major weaknesses: the reasons behind …

Despite the impressive performances reported by deep neural networks in different
application domains, they remain largely vulnerable to adversarial examples, ie, input …

The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

With the remarkable success of deep learning, Deep Neural Networks (DNNs) have been
applied as dominant tools to various machine learning domains. Despite this success …

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 …

Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial
samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such …

The vulnerabilities of deep neural networks against adversarial examples have become a
significant concern for deploying these models in sensitive domains. Devising a definitive …

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 …

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples
resulting from adding small-magnitude perturbations to inputs. Such adversarial examples …