74 citations · 166 across the 8 of their papers we have counts for
8 papers
Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning
Ji Gao, Sanjam Garg, Mohammad Mahmoody +1
Privacy attacks on machine learning models aim to identify the data that is used to train such models. Such attacks, traditionally, are studied on static models that are trained on…
Adversarial Sample Detection for Speaker Verification by Neural Vocoders
Haibin Wu, Po-chun Hsu, Ji Gao +6
Automatic speaker verification (ASV), one of the most important technology for biometric identification, has been widely adopted in security-critical applications. However, ASV is…
Learning and Certification under Instance-targeted Poisoning
Ji Gao, Amin Karbasi, Mohammad Mahmoody
In this paper, we study PAC learnability and certification of predictions under instance-targeted poisoning attacks, where the adversary who knows the test instance may change a fr…
Exploring the Naturalness of Buggy Code with Recurrent Neural Networks
Jack Lanchantin, Ji Gao
Statistical language models are powerful tools which have been used for many tasks within natural language processing. Recently, they have been used for other sequential data such…
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Ji Gao, Jack Lanchantin, Mary Lou Soffa +1
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention has been paid to black-box attacks, which are more re…
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
Ji Gao, Beilun Wang, Zeming Lin +2
Recent studies have shown that deep neural networks (DNN) are vulnerable to adversarial samples: maliciously-perturbed samples crafted to yield incorrect model outputs. Such attack…
A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
Beilun Wang, Ji Gao, Yanjun Qi
Estimating multiple sparse Gaussian Graphical Models (sGGMs) jointly for many related tasks (large ) under a high-dimensional (large ) situation is an important task. Most pr…
A Theoretical Framework for Robustness of (Deep) Classifiers against Adversarial Examples
Beilun Wang, Ji Gao, Yanjun Qi
Most machine learning classifiers, including deep neural networks, are vulnerable to adversarial examples. Such inputs are typically generated by adding small but purposeful modifi…