Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

Recent text-to-image diffusion models such as MidJourney and Stable Diffusion threaten to
displace many in the professional artist community. In particular, models can learn to mimic …

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

Deep neural networks (DNNs) are an indispensable machine learning tool despite the
difficulty of diagnosing what aspects of a model's input drive its decisions. In countless real …

With the success of deep learning algorithms in various domains, studying adversarial
attacks to secure deep models in real world applications has become an important research …

We present a novel method for scalable and precise certification of deep neural networks.
The key technical insight behind our approach is a new abstract domain which combines …

Current adversarial attack research reveals the vulnerability of learning-based classifiers
against carefully crafted perturbations. However, most existing attack methods have inherent …

With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …

We present AI 2, the first sound and scalable analyzer for deep neural networks. Based on
overapproximation, AI 2 can automatically prove safety properties (eg, robustness) of …