36 citations · 95 across the 9 of their papers we have counts for
9 papers
Range-Aware Attention Network for LiDAR-based 3D Object Detection with Auxiliary Point Density Level Estimation
Yantao Lu, Xuetao Hao, Yilan Li +3
3D object detection from LiDAR data for autonomous driving has been making remarkable strides in recent years. Among the state-of-the-art methodologies, encoding point clouds into…
Hermes Attack: Steal DNN Models with Lossless Inference Accuracy
Yuankun Zhu, Yueqiang Cheng, Husheng Zhou +1
Deep Neural Networks (DNNs) models become one of the most valuable enterprise assets due to their critical roles in all aspects of applications. With the trend of privatization dep…
Towards Practical Lottery Ticket Hypothesis for Adversarial Training
Bai Li, Shiqi Wang, Yunhan Jia +4
Recent research has proposed the lottery ticket hypothesis, suggesting that for a deep neural network, there exist trainable sub-networks performing equally or better than the orig…
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
Yantao Lu, Yunhan Jia, Jianyu Wang +4
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they remain adversarial even against other m…
Fooling Detection Alone is Not Enough: First Adversarial Attack against Multiple Object Tracking
Yunhan Jia, Yantao Lu, Junjie Shen +3
Recent work in adversarial machine learning started to focus on the visual perception in autonomous driving and studied Adversarial Examples (AEs) for object detection models. Howe…
Autonomous Human Activity Classification from Ego-vision Camera and Accelerometer Data
Yantao Lu, Senem Velipasalar
There has been significant amount of research work on human activity classification relying either on Inertial Measurement Unit (IMU) data or data from static cameras providing a t…
Enhancing Cross-task Transferability of Adversarial Examples with Dispersion Reduction
Yunhan Jia, Yantao Lu, Senem Velipasalar +2
Neural networks are known to be vulnerable to carefully crafted adversarial examples, and these malicious samples often transfer, i.e., they maintain their effectiveness even again…
Autonomously and Simultaneously Refining Deep Neural Network Parameters by a Bi-Generative Adversarial Network Aided Genetic Algorithm
Yantao Lu, Burak Kakillioglu, Senem Velipasalar
The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks. Genetic Algorithms (GA) have been used…
Autonomously and Simultaneously Refining Deep Neural Network Parameters by Generative Adversarial Networks
Burak Kakillioglu, Yantao Lu, Senem Velipasalar
The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks. However, there has not been much work…