Search Results for author:
Found 7 papers, 1 papers with code
Pre-trained Trojan Attacks for Visual Recognition
This paper aims to raise awareness of the potential threats associated with applying PVMs in practical scenarios.
RobustMQ: Benchmarking Robustness of Quantized Models
Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources.
Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks
However, these defenses are now suffering problems of high inference computational overheads and unfavorable trade-offs between benign accuracy and stealing robustness, which challenges the feasibility of deployed models in practice.
Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing
Machine learning (ML) systems have achieved remarkable performance across a wide area of applications.
Benchmarking the Physical-world Adversarial Robustness of Vehicle Detection
Thus, virtual simulation experiments can provide a solution to this challenge.
Boosting Cross-task Transferability of Adversarial Patches with Visual Relations
The transferability of adversarial examples is a crucial aspect of evaluating the robustness of deep learning systems, particularly in black-box scenarios.
Benchmarking the Robustness of Quantized Models
To address this gap, we thoroughly evaluated the robustness of quantized models against various noises (adversarial attacks, natural corruptions, and systematic noises) on ImageNet.
