W Rawat, Z Wang - Neural computation, 2017 - ieeexplore.ieee.org
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 …
X Yuan, P He, Q Zhu, X Li - IEEE transactions on neural …, 2019 - ieeexplore.ieee.org
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 …