228 citations · 329 across the 13 of their papers we have counts for
5 papers · 1 filter
Reaching Data Confidentiality and Model Accountability on the CalTrain
Zhongshu Gu, Hani Jamjoom, Dong Su +5
Distributed collaborative learning (DCL) paradigms enable building joint machine learning models from distrusting multi-party participants. Data confidentiality is guaranteed by re…
Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering
Bryant Chen, Wilka Carvalho, Nathalie Baracaldo +5
While machine learning (ML) models are being increasingly trusted to make decisions in different and varying areas, the safety of systems using such models has become an increasing…
Adversarial Robustness Toolbox v1.0.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran +9
Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision…
Confidential Inference via Ternary Model Partitioning
Zhongshu Gu, Heqing Huang, Jialong Zhang +5
Today's cloud vendors are competing to provide various offerings to simplify and accelerate AI service deployment. However, cloud users always have concerns about the confidentiali…
Defending Against Machine Learning Model Stealing Attacks Using Deceptive Perturbations
Taesung Lee, Benjamin Edwards, Ian Molloy +1
Machine learning models are vulnerable to simple model stealing attacks if the adversary can obtain output labels for chosen inputs. To protect against these attacks, it has been p…