133 citations · 153 across the 10 of their papers we have counts for
11 papers
A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learning
Phung Lai, Guanxiong Liu, NhatHai Phan +3
Federated learning (FL) enables collaborative model training using decentralized private data from multiple clients. While FL has shown robustness against poisoning attacks with ba…
An Adaptive Black-box Defense against Trojan Attacks (TrojDef)
Guanxiong Liu, Abdallah Khreishah, Fatima Sharadgah +1
Trojan backdoor is a poisoning attack against Neural Network (NN) classifiers in which adversaries try to exploit the (highly desirable) model reuse property to implant Trojans int…
Smart Traffic Monitoring System using Computer Vision and Edge Computing
Guanxiong Liu, Hang Shi, Abbas Kiani +5
Traffic management systems capture tremendous video data and leverage advances in video processing to detect and monitor traffic incidents. The collected data are traditionally for…
A Synergetic Attack against Neural Network Classifiers combining Backdoor and Adversarial Examples
Guanxiong Liu, Issa Khalil, Abdallah Khreishah +1
In this work, we show how to jointly exploit adversarial perturbation and model poisoning vulnerabilities to practically launch a new stealthy attack, dubbed AdvTrojan. AdvTrojan i…
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
Guanxiong Liu, Issa Khalil, Abdallah Khreishah +5
Machine learning models, especially neural network (NN) classifiers, have acceptable performance and accuracy that leads to their wide adoption in different aspects of our daily li…
CheXclusion: Fairness gaps in deep chest X-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott +2
Machine learning systems have received much attention recently for their ability to achieve expert-level performance on clinical tasks, particularly in medical imaging. Here, we ex…
Using Single-Step Adversarial Training to Defend Iterative Adversarial Examples
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Adversarial examples have become one of the largest challenges that machine learning models, especially neural network classifiers, face. These adversarial examples break the assum…
Using Intuition from Empirical Properties to Simplify Adversarial Training Defense
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Due to the surprisingly good representation power of complex distributions, neural network (NN) classifiers are widely used in many tasks which include natural language processing,…
ZK-GanDef: A GAN based Zero Knowledge Adversarial Training Defense for Neural Networks
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Neural Network classifiers have been used successfully in a wide range of applications. However, their underlying assumption of attack free environment has been defied by adversari…
Clinically Accurate Chest X-Ray Report Generation
Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott +4
The automatic generation of radiology reports given medical radiographs has significant potential to operationally and improve clinical patient care. A number of prior works have f…
GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier
Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Machine learning models, especially neural network (NN) classifiers, are widely used in many applications including natural language processing, computer vision and cybersecurity.…