80 citations · 244 across the 14 of their papers we have counts for
14 papers
PerD: Perturbation Sensitivity-based Neural Trojan Detection Framework on NLP Applications
Diego Garcia-soto, Huili Chen, Farinaz Koushanfar
Deep Neural Networks (DNNs) have been shown to be susceptible to Trojan attacks. Neural Trojan is a type of targeted poisoning attack that embeds the backdoor into the victim and i…
Self-Aware Personalized Federated Learning
Huili Chen, Jie Ding, Eric Tramel +4
In the context of personalized federated learning (FL), the critical challenge is to balance local model improvement and global model tuning when the personal and global objectives…
AdaTest:Reinforcement Learning and Adaptive Sampling for On-chip Hardware Trojan Detection
Huili Chen, Xinqiao Zhang, Ke Huang +1
This paper proposes AdaTest, a novel adaptive test pattern generation framework for efficient and reliable Hardware Trojan (HT) detection. HT is a backdoor attack that tampers with…
An Adaptive Black-box Backdoor Detection Method for Deep Neural Networks
Xinqiao Zhang, Huili Chen, Ke Huang +1
With the surge of Machine Learning (ML), An emerging amount of intelligent applications have been developed. Deep Neural Networks (DNNs) have demonstrated unprecedented performance…
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection
Yein Kim, Huili Chen, Farinaz Koushanfar
The goal of federated learning (FL) is to train one global model by aggregating model parameters updated independently on edge devices without accessing users' private data. Howeve…
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning
Oliver Lutz, Huili Chen, Hossein Fereidooni +4
Ethereum smart contracts are automated decentralized applications on the blockchain that describe the terms of the agreement between buyers and sellers, reducing the need for trust…
TAD: Trigger Approximation based Black-box Trojan Detection for AI
Xinqiao Zhang, Huili Chen, Farinaz Koushanfar
An emerging amount of intelligent applications have been developed with the surge of Machine Learning (ML). Deep Neural Networks (DNNs) have demonstrated unprecedented performance…
FLAME: Taming Backdoors in Federated Learning (Extended Version 1)
Thien Duc Nguyen, Phillip Rieger, Huili Chen +10
Federated Learning (FL) is a collaborative machine learning approach allowing participants to jointly train a model without having to share their private, potentially sensitive loc…
Trustworthy AI Inference Systems: An Industry Research View
Rosario Cammarota, Matthias Schunter, Anand Rajan +26
In this work, we provide an industry research view for approaching the design, deployment, and operation of trustworthy Artificial Intelligence (AI) inference systems. Such systems…
A Neural-based Program Decompiler
Cheng Fu, Huili Chen, Haolan Liu +4
Reverse engineering of binary executables is a critical problem in the computer security domain. On the one hand, malicious parties may recover interpretable source codes from the…
BlackMarks: Blackbox Multibit Watermarking for Deep Neural Networks
Huili Chen, Bita Darvish Rouhani, Farinaz Koushanfar
Deep Neural Networks have created a paradigm shift in our ability to comprehend raw data in various important fields ranging from computer vision and natural language processing to…
Performance Comparison of Contemporary DNN Watermarking Techniques
Huili Chen, Bita Darvish Rouhani, Xinwei Fan +2
DNNs shall be considered as the intellectual property (IP) of the model builder due to the impeding cost of designing/training a highly accurate model. Research attempts have been…
DeepSigns: A Generic Watermarking Framework for IP Protection of Deep Learning Models
Bita Darvish Rouhani, Huili Chen, Farinaz Koushanfar
Deep Learning (DL) models have caused a paradigm shift in our ability to comprehend raw data in various important fields, ranging from intelligence warfare and healthcare to autono…
DeepMarks: A Digital Fingerprinting Framework for Deep Neural Networks
Huili Chen, Bita Darvish Rohani, Farinaz Koushanfar
This paper proposes DeepMarks, a novel end-to-end framework for systematic fingerprinting in the context of Deep Learning (DL). Remarkable progress has been made in the area of dee…