1.7k citations · 2.2k across the 2 of their papers we have counts for
8 papers
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…