Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

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 …

Adversarial examples have attracted significant attention in machine learning, but the
reasons for their existence and pervasiveness remain unclear. We demonstrate that …

Deep neural networks have been demonstrated to be vulnerable to backdoor attacks.
Specifically, by injecting a small number of maliciously constructed inputs into the training …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …

Defenses against adversarial examples, such as adversarial training, are typically tailored to
a single perturbation type (eg, small $\ell_\infty $-noise). For other perturbations, these …

A wide range of defenses have been proposed to harden neural networks against
adversarial attacks. However, a pattern has emerged in which the majority of adversarial …

Deep Learning algorithms have achieved state-of-the-art performance for Image
Classification. For this reason, they have been used even in security-critical applications …

Many machine learning models are vulnerable to adversarial attacks; for example, adding
adversarial perturbations that are imperceptible to humans can often make machine …

Despite the empirical success of using adversarial training to defend deep learning models
against adversarial perturbations, so far, it still remains rather unclear what the principles are …