1k citations · 2.9k across the 16 of their papers we have counts for
16 papers
Improving Model Generalization by On-manifold Adversarial Augmentation in the Frequency Domain
Chang Liu, Wenzhao Xiang, Yuan He +3
Deep neural networks (DNNs) may suffer from significantly degenerated performance when the training and test data are of different underlying distributions. Despite the importance…
Distilling Representations from GAN Generator via Squeeze and Span
Yu Yang, Xiaotian Cheng, Chang Liu +2
In recent years, generative adversarial networks (GANs) have been an actively studied topic and shown to successfully produce high-quality realistic images in various domains. The…
Unrestricted Adversarial Attacks on ImageNet Competition
Yuefeng Chen, Xiaofeng Mao, Yuan He +34
Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-wo…
You Cannot Easily Catch Me: A Low-Detectable Adversarial Patch for Object Detectors
Zijian Zhu, Hang Su, Chang Liu +2
Blind spots or outright deceit can bedevil and deceive machine learning models. Unidentified objects such as digital "stickers," also known as adversarial patches, can fool facial…
Improving the Robustness of Adversarial Attacks Using an Affine-Invariant Gradient Estimator
Wenzhao Xiang, Hang Su, Chang Liu +2
As designers of artificial intelligence try to outwit hackers, both sides continue to hone in on AI's inherent vulnerabilities. Designed and trained from certain statistical distri…
Improving Visual Quality of Unrestricted Adversarial Examples with Wavelet-VAE
Wenzhao Xiang, Chang Liu, Shibao Zheng
Traditional adversarial examples are typically generated by adding perturbation noise to the input image within a small matrix norm. In practice, un-restricted adversarial attack h…
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid Decoder
Guanlin Li, Shuya Ding, Jun Luo +1
Whereas adversarial training is employed as the main defence strategy against specific adversarial samples, it has limited generalization capability and incurs excessive time compl…
Curriculum Adversarial Training
Qi-Zhi Cai, Min Du, Chang Liu +1
Recently, deep learning has been applied to many security-sensitive applications, such as facial authentication. The existence of adversarial examples hinders such applications. Th…
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Nicholas Carlini, Chang Liu, Úlfar Erlingsson +2
This paper describes a testing methodology for quantitatively assessing the risk that rare or unique training-data sequences are unintentionally memorized by generative sequence mo…
Tree-to-tree Neural Networks for Program Translation
Xinyun Chen, Chang Liu, Dawn Song
Program translation is an important tool to migrate legacy code in one language into an ecosystem built in a different language. In this work, we are the first to employ deep neura…
Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
Xinyun Chen, Chang Liu, Bo Li +2
Deep learning models have achieved high performance on many tasks, and thus have been applied to many security-critical scenarios. For example, deep learning-based face recognition…
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning
Xiaojun Xu, Chang Liu, Dawn Song
Synthesizing SQL queries from natural language is a long-standing open problem and has been attracting considerable interest recently. Toward solving the problem, the de facto appr…
Fooling Vision and Language Models Despite Localization and Attention Mechanism
Xiaojun Xu, Xinyun Chen, Chang Liu +3
Adversarial attacks are known to succeed on classifiers, but it has been an open question whether more complex vision systems are vulnerable. In this paper, we study adversarial ex…
Towards Synthesizing Complex Programs from Input-Output Examples
Xinyun Chen, Chang Liu, Dawn Song
In recent years, deep learning techniques have been developed to improve the performance of program synthesis from input-output examples. Albeit its significant progress, the progr…
Latent Attention For If-Then Program Synthesis
Xinyun Chen, Chang Liu, Richard Shin +2
Automatic translation from natural language descriptions into programs is a longstanding challenging problem. In this work, we consider a simple yet important sub-problem: translat…
Delving into Transferable Adversarial Examples and Black-box Attacks
Yanpei Liu, Xinyun Chen, Chang Liu +1
An intriguing property of deep neural networks is the existence of adversarial examples, which can transfer among different architectures. These transferable adversarial examples m…