30 citations · 31 across the 4 of their papers we have counts for
5 papers
A Unified Evaluation Framework of Physical Adversarial Robustness in the Edge VLM Era
Eric Liang, Alan Luo, Kaiwen Yuan
Recent advancements in Vision-Language Models (VLMs) enable end-to-end visual grounding via language prompts and generative decoding, demonstrating largely improved zero-shot and o…
Multi-modal Streaming 3D Object Detection
Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang +1
Modern autonomous vehicles rely heavily on mechanical LiDARs for perception. Current perception methods generally require 360° point clouds, collected sequentially as the LiDAR sca…
Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework
Li Ding, Yongwei Wang, Xin Ding +4
Deep learning based image classification models are shown vulnerable to adversarial attacks by injecting deliberately crafted noises to clean images. To defend against adversarial…
Adversarial Attacks on Camera-LiDAR Models for 3D Car Detection
Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang +1
Most autonomous vehicles (AVs) rely on LiDAR and RGB camera sensors for perception. Using these point cloud and image data, perception models based on deep neural nets (DNNs) have…
Towards Universal Physical Attacks On Cascaded Camera-Lidar 3D Object Detection Models
Mazen Abdelfattah, Kaiwen Yuan, Z. Jane Wang +1
We propose a universal and physically realizable adversarial attack on a cascaded multi-modal deep learning network (DNN), in the context of self-driving cars. DNNs have achieved h…