5 citations · 11 across the 6 of their papers we have counts for
6 papers
Anonymity Unveiled: A Practical Framework for Auditing Data Use in Deep Learning Models
Zitao Chen, Karthik Pattabiraman
The rise of deep learning (DL) has led to a surging demand for training data, which incentivizes the creators of DL models to trawl through the Internet for training materials. Mea…
A Method to Facilitate Membership Inference Attacks in Deep Learning Models
Zitao Chen, Karthik Pattabiraman
Modern machine learning (ML) ecosystems offer a surging number of ML frameworks and code repositories that can greatly facilitate the development of ML models. Today, even ordinary…
Overconfidence is a Dangerous Thing: Mitigating Membership Inference Attacks by Enforcing Less Confident Prediction
Zitao Chen, Karthik Pattabiraman
Machine learning (ML) models are vulnerable to membership inference attacks (MIAs), which determine whether a given input is used for training the target model. While there have be…
Jujutsu: A Two-stage Defense against Adversarial Patch Attacks on Deep Neural Networks
Zitao Chen, Pritam Dash, Karthik Pattabiraman
Adversarial patch attacks create adversarial examples by injecting arbitrary distortions within a bounded region of the input to fool deep neural networks (DNNs). These attacks are…
TensorFI: A Flexible Fault Injection Framework for TensorFlow Applications
Zitao Chen, Niranjhana Narayanan, Bo Fang +3
As machine learning (ML) has seen increasing adoption in safety-critical domains (e.g., autonomous vehicles), the reliability of ML systems has also grown in importance. While prio…
A Low-cost Fault Corrector for Deep Neural Networks through Range Restriction
Zitao Chen, Guanpeng Li, Karthik Pattabiraman
The adoption of deep neural networks (DNNs) in safety-critical domains has engendered serious reliability concerns. A prominent example is hardware transient faults that are growin…