4 papers
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan +2
Deep neural networks, like many other machine learning models, have recently been shown to lack robustness against adversarially crafted inputs. These inputs are derived from regul…
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
Nicolas Papernot, Patrick McDaniel, Ian Goodfellow
Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output. Adversarial…
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…
Towards Least Privilege Containers with Cimplifier
Vaibhav Rastogi, Drew Davidson, Lorenzo De Carli +2
Application containers, such as Docker containers, have recently gained popularity as a solution for agile and seamless deployment of applications. These light-weight virtualizatio…