1.7k citations · 3k across the 18 of their papers we have counts for
18 papers
Large-Scale Public Data Improves Differentially Private Image Generation Quality
Ruihan Wu, Chuan Guo, Kamalika Chaudhuri
Public data has been frequently used to improve the privacy-accuracy trade-off of differentially private machine learning, but prior work largely assumes that this data come from t…
ViP: A Differentially Private Foundation Model for Computer Vision
Yaodong Yu, Maziar Sanjabi, Yi Ma +2
Artificial intelligence (AI) has seen a tremendous surge in capabilities thanks to the use of foundation models trained on internet-scale data. On the flip side, the uncurated natu…
Learning to Invert: Simple Adaptive Attacks for Gradient Inversion in Federated Learning
Ruihan Wu, Xiangyu Chen, Chuan Guo +1
Gradient inversion attack enables recovery of training samples from model gradients in federated learning (FL), and constitutes a serious threat to data privacy. To mitigate this v…
Origins of Low-dimensional Adversarial Perturbations
Elvis Dohmatob, Chuan Guo, Morgane Goibert
In this paper, we initiate a rigorous study of the phenomenon of low-dimensional adversarial perturbations (LDAPs) in classification. Unlike the classical setting, these perturbati…
Does Label Differential Privacy Prevent Label Inference Attacks?
Ruihan Wu, Jin Peng Zhou, Kilian Q. Weinberger +1
Label differential privacy (label-DP) is a popular framework for training private ML models on datasets with public features and sensitive private labels. Despite its rigorous priv…
ReAct: Out-of-distribution Detection With Rectified Activations
Yiyou Sun, Chuan Guo, Yixuan Li
Out-of-distribution (OOD) detection has received much attention lately due to its practical importance in enhancing the safe deployment of neural networks. One of the primary chall…
On the Importance of Difficulty Calibration in Membership Inference Attacks
Lauren Watson, Chuan Guo, Graham Cormode +1
The vulnerability of machine learning models to membership inference attacks has received much attention in recent years. However, existing attacks mostly remain impractical due to…
Gradient-based Adversarial Attacks against Text Transformers
Chuan Guo, Alexandre Sablayrolles, Hervé Jégou +1
We propose the first general-purpose gradient-based attack against transformer models. Instead of searching for a single adversarial example, we search for a distribution of advers…
Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems
Ruihan Wu, Chuan Guo, Awni Hannun +1
Machine-learning systems such as self-driving cars or virtual assistants are composed of a large number of machine-learning models that recognize image content, transcribe speech,…
Simple Black-box Adversarial Attacks
Chuan Guo, Jacob R. Gardner, Yurong You +2
We propose an intriguingly simple method for the construction of adversarial images in the black-box setting. In constrast to the white-box scenario, constructing black-box adversa…
Mining Dual Emotion for Fake News Detection
Xueyao Zhang, Juan Cao, Xirong Li +3
Emotion plays an important role in detecting fake news online. When leveraging emotional signals, the existing methods focus on exploiting the emotions of news contents that convey…
Low Frequency Adversarial Perturbation
Chuan Guo, Jared S. Frank, Kilian Q. Weinberger
Adversarial images aim to change a target model's decision by minimally perturbing a target image. In the black-box setting, the absence of gradient information often renders this…
An empirical study on evaluation metrics of generative adversarial networks
Qiantong Xu, Gao Huang, Yang Yuan +4
Evaluating generative adversarial networks (GANs) is inherently challenging. In this paper, we revisit several representative sample-based evaluation metrics for GANs, and address…
Countering Adversarial Images using Input Transformations
Chuan Guo, Mayank Rana, Moustapha Cisse +1
This paper investigates strategies that defend against adversarial-example attacks on image-classification systems by transforming the inputs before feeding them to the system. Spe…
On Calibration of Modern Neural Networks
Chuan Guo, Geoff Pleiss, Yu Sun +1
Confidence calibration -- the problem of predicting probability estimates representative of the true correctness likelihood -- is important for classification models in many applic…
A tight bound on the size of certain separating hash families
Chuan Guo, Douglas R. Stinson
In this paper, we present a new lower bound on the size of separating hash families of type {w_1^{q-1},w_2} where w_1 < w_2. Our result extends the paper by Guo et al. on binary fr…
On the Combinatorics of Palindromes and Antipalindromes
Chuan Guo, Jeffrey Shallit, Arseny M. Shur
We prove a number of results on the structure and enumeration of palindromes and antipalindromes. In particular, we study conjugates of palindromes, palindromic pairs, rich words,…
On tight bounds for binary frameproof codes
Chuan Guo, Douglas R. Stinson, Tran van Trung
In this paper, we study -frameproof codes, which are equivalent to -separating hash families. Our main results concern binary codes, which are defined over an alphabet…