1 citations · 1 across the 5 of their papers we have counts for
6 papers
RobustMQ: Benchmarking Robustness of Quantized Models
Yisong Xiao, Aishan Liu, Tianyuan Zhang +3
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities wh…
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
Jun Guo, Aishan Liu, Xingyu Zheng +4
Despite the broad application of Machine Learning models as a Service (MLaaS), they are vulnerable to model stealing attacks. These attacks can replicate the model functionality by…
Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing
Yisong Xiao, Aishan Liu, Tianlin Li +1
Machine learning (ML) systems have achieved remarkable performance across a wide area of applications. However, they frequently exhibit unfair behaviors in sensitive application do…
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations
Tony Ma, Songze Li, Yisong Xiao +1
The transferability of adversarial examples is a crucial aspect of evaluating the robustness of deep learning systems, particularly in black-box scenarios. Although several methods…
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection
Tianyuan Zhang, Yisong Xiao, Xiaoya Zhang +2
Adversarial attacks in the physical world can harm the robustness of detection models. Evaluating the robustness of detection models in the physical world can be challenging due to…
Benchmarking the Robustness of Quantized Models
Yisong Xiao, Tianyuan Zhang, Shunchang Liu +1
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities wh…