Search Results for author: Carl A. Gunter

Found 7 papers, 3 papers with code

Detecting AI Trojans Using Meta Neural Analysis

1 code implementation8 Oct 2019 Xiaojun Xu, Qi. Wang, Huichen Li, Nikita Borisov, Carl A. Gunter, Bo Li

To train the meta-model without knowledge of the attack strategy, we introduce a technique called jumbo learning that samples a set of Trojaned models following a general distribution.

Data Poisoning

G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators

2 code implementations NeurIPS 2021 Yunhui Long, Boxin Wang, Zhuolin Yang, Bhavya Kailkhura, Aston Zhang, Carl A. Gunter, Bo Li

In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator.

Understanding Membership Inferences on Well-Generalized Learning Models

1 code implementation13 Feb 2018 Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiao-Feng Wang, Haixu Tang, Carl A. Gunter, Kai Chen

Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model.

Inference Attack Membership Inference Attack

CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition

no code implementations24 Jan 2018 Xuejing Yuan, Yuxuan Chen, Yue Zhao, Yunhui Long, Xiaokang Liu, Kai Chen, Shengzhi Zhang, Heqing Huang, Xiao-Feng Wang, Carl A. Gunter

For this purpose, we developed novel techniques that address a key technical challenge: integrating the commands into a song in a way that can be effectively recognized by ASR through the air, in the presence of background noise, while not being detected by a human listener.

Automatic Speech Recognition

Plausible Deniability for Privacy-Preserving Data Synthesis

no code implementations26 Aug 2017 Vincent Bindschaedler, Reza Shokri, Carl A. Gunter

We demonstrate the efficiency of this generative technique on a large dataset; it is shown to preserve the utility of original data with respect to various statistical analysis and machine learning measures.

De-identification

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