52 citations · 204 across the 12 of their papers we have counts for
12 papers
Detecting Backdoors in Pre-trained Encoders
Shiwei Feng, Guanhong Tao, Siyuan Cheng +6
Self-supervised learning in computer vision trains on unlabeled data, such as images or (image, text) pairs, to obtain an image encoder that learns high-quality embeddings for inpu…
BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense
Siyuan Cheng, Guanhong Tao, Yingqi Liu +8
Deep Learning backdoor attacks have a threat model similar to traditional cyber attacks. Attack forensics, a critical counter-measure for traditional cyber attacks, is hence of imp…
Backdoor Vulnerabilities in Normally Trained Deep Learning Models
Guanhong Tao, Zhenting Wang, Siyuan Cheng +7
We conduct a systematic study of backdoor vulnerabilities in normally trained Deep Learning models. They are as dangerous as backdoors injected by data poisoning because both can b…
FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
Kaiyuan Zhang, Guanhong Tao, Qiuling Xu +8
Federated Learning (FL) is a distributed learning paradigm that enables different parties to train a model together for high quality and strong privacy protection. In this scenario…
Constrained Optimization with Dynamic Bound-scaling for Effective NLPBackdoor Defense
Guangyu Shen, Yingqi Liu, Guanhong Tao +5
We develop a novel optimization method for NLPbackdoor inversion. We leverage a dynamically reducing temperature coefficient in the softmax function to provide changing loss landsc…
Backdoor Scanning for Deep Neural Networks through K-Arm Optimization
Guangyu Shen, Yingqi Liu, Guanhong Tao +5
Back-door attack poses a severe threat to deep learning systems. It injects hidden malicious behaviors to a model such that any input stamped with a special pattern can trigger suc…
Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification
Siyuan Cheng, Yingqi Liu, Shiqing Ma +1
Trojan (backdoor) attack is a form of adversarial attack on deep neural networks where the attacker provides victims with a model trained/retrained on malicious data. The backdoor…
UDC 2020 Challenge on Image Restoration of Under-Display Camera: Methods and Results
Yuqian Zhou, Michael Kwan, Kyle Tolentino +42
This paper is the report of the first Under-Display Camera (UDC) image restoration challenge in conjunction with the RLQ workshop at ECCV 2020. The challenge is based on a newly-co…
Deep Learning Backdoors
Shaofeng Li, Shiqing Ma, Minhui Xue +1
Intuitively, a backdoor attack against Deep Neural Networks (DNNs) is to inject hidden malicious behaviors into DNNs such that the backdoor model behaves legitimately for benign in…
Dynamic Backdoor Attacks Against Machine Learning Models
Ahmed Salem, Rui Wen, Michael Backes +2
Machine learning (ML) has made tremendous progress during the past decade and is being adopted in various critical real-world applications. However, recent research has shown that…
Testing Deep Learning Models for Image Analysis Using Object-Relevant Metamorphic Relations
Yongqiang Tian, Shiqing Ma, Ming Wen +3
Deep learning models are widely used for image analysis. While they offer high performance in terms of accuracy, people are concerned about if these models inappropriately make inf…
Attacks Meet Interpretability: Attribute-steered Detection of Adversarial Samples
Guanhong Tao, Shiqing Ma, Yingqi Liu +1
Adversarial sample attacks perturb benign inputs to induce DNN misbehaviors. Recent research has demonstrated the widespread presence and the devastating consequences of such attac…