433 citations · 1.3k across the 96 of their papers we have counts for
8 papers · 1 filter
Resilience and Security of Deep Neural Networks Against Intentional and Unintentional Perturbations: Survey and Research Challenges
Sazzad Sayyed, Milin Zhang, Shahriar Rifat +3
In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative that DNNs provide inference robust to external perturbations - both intentional and uninte…
You Do (Not) Belong Here: Detecting DPI Evasion Attacks with Context Learning
Shitong Zhu, Shasha Li, Zhongjie Wang +5
As Deep Packet Inspection (DPI) middleboxes become increasingly popular, a spectrum of adversarial attacks have emerged with the goal of evading such middleboxes. Many of these att…
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Nicolas Papernot, Patrick McDaniel, Ananthram Swami +1
Machine learning models are frequently used to solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles or predicting…
Detection under Privileged Information
Z. Berkay Celik, Patrick McDaniel, Rauf Izmailov +4
For well over a quarter century, detection systems have been driven by models learned from input features collected from real or simulated environments. An artifact (e.g., network…
Practical Black-Box Attacks against Machine Learning
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow +3
Machine learning (ML) models, e.g., deep neural networks (DNNs), are vulnerable to adversarial examples: malicious inputs modified to yield erroneous model outputs, while appearing…
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha +3
Deep learning takes advantage of large datasets and computationally efficient training algorithms to outperform other approaches at various machine learning tasks. However, imperfe…
Six Potential Game-Changers in Cyber Security: Towards Priorities in Cyber Science and Engineering
Alexander Kott, Ananthram Swami, Patrick McDaniel
The fields of study encompassed by cyber science and engineering are broad and poorly defined at this time. As national governments and research communities increase their recognit…
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
Nicolas Papernot, Patrick McDaniel, Xi Wu +2
Deep learning algorithms have been shown to perform extremely well on many classical machine learning problems. However, recent studies have shown that deep learning, like other ma…