Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven
notoriously difficult to understand how they reach their decisions. We here introduce a high-…

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning
increasingly complex representations of object shapes. Some recent studies suggest a …

Adaptive attacks have (rightfully) become the de facto standard for evaluating defenses to
adversarial examples. We find, however, that typical adaptive evaluations are incomplete. We …

Self-supervised representation learning has shown remarkable success in a number of
domains. A common practice is to perform data augmentation via hand-crafted transformations …

Deep learning has triggered the current rise of artificial intelligence and is the workhorse of
today’s machine intelligence. Numerous success stories have rapidly spread all over science…

A few years ago, the first CNN surpassed human performance on ImageNet. However, it
soon became clear that machines lack robustness on more challenging test cases, a major …

The human visual system is remarkably robust against a wide range of naturally occurring
variations and corruptions like rain or snow. In contrast, the performance of modern image …

With the rise of machines to human-level performance in complex recognition tasks, a
growing amount of work is directed toward comparing information processing in humans and …

Many machine learning algorithms are vulnerable to almost imperceptible perturbations of
their inputs. So far it was unclear how much risk adversarial perturbations carry for the safety …

Correctly evaluating defenses against adversarial examples has proven to be extremely
difficult. Despite the significant amount of recent work attempting to design defenses that …