217 citations · 1.8k across the 154 of their papers we have counts for
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Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing
Jinyuan Jia, Xiaoyu Cao, Binghui Wang +1
It is well-known that classifiers are vulnerable to adversarial perturbations. To defend against adversarial perturbations, various certified robustness results have been derived.…
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning
Minghong Fang, Xiaoyu Cao, Jinyuan Jia +1
In federated learning, multiple client devices jointly learn a machine learning model: each client device maintains a local model for its local training dataset, while a master dev…
Data Poisoning Attacks to Local Differential Privacy Protocols
Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
Local Differential Privacy (LDP) protocols enable an untrusted data collector to perform privacy-preserving data analytics. In particular, each user locally perturbs its data to pr…
IPGuard: Protecting Intellectual Property of Deep Neural Networks via Fingerprinting the Classification Boundary
Xiaoyu Cao, Jinyuan Jia, Neil Zhenqiang Gong
A deep neural network (DNN) classifier represents a model owner's intellectual property as training a DNN classifier often requires lots of resource. Watermarking was recently prop…
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges
Jinyuan Jia, Neil Zhenqiang Gong
As machine learning (ML) becomes more and more powerful and easily accessible, attackers increasingly leverage ML to perform automated large-scale inference attacks in various doma…
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Jinyuan Jia, Ahmed Salem, Michael Backes +2
In a membership inference attack, an attacker aims to infer whether a data sample is in a target classifier's training dataset or not. Specifically, given a black-box access to the…