Full-Distance Evasion of Pedestrian Detectors in the Physical World
Authors: Zhi Cheng, Zhanhao Hu, Yuqiu Liu, Jianmin Li, Hang Su, Xiaolin Hu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our physical world experiments demonstrate the effectiveness of our FDA patterns across various detection models like YOLOv5, Deformable-DETR, and Mask RCNN. |
| Researcher Affiliation | Academia | Zhi Cheng1, Zhanhao Hu2, Yuqiu Liu3, Jianmin Li1, Hang Su1, Xiaolin Hu1* 1Department of Computer Science and Technology, Tsinghua University, Beijing, China 2Department of Electrical Engineering and Computer Sciences, UC Berkeley 3Department of Technology, Beijing Forestry University, Beijing, China |
| Pseudocode | No | The paper describes the methodology and various modules (DIC, MFO) but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Codes available at https://github.com/zhicheng2T0/Full-Distance Attack.git |
| Open Datasets | Yes | To optimize the FDA patterns in the digital world, we created a pedestrian dataset and a background dataset. 1100 pedestrian images were extracted from existing datasets (INRIA [3], Penn Fudan [36] and COCO [24]). |
| Dataset Splits | Yes | To form a distant image dataset to train the DIC (Figure 4 (a)), we printed 45 training images and 9 testing images onto papers, collected photos of all printed images at 7 distances (4m, 8m, 14m, 20m, 26m, 34m, 40m) in 5 days and removed ones with noises (e.g. reflections and shadows). |
| Hardware Specification | No | The paper mentions specific smartphone cameras used for capturing test images ('back camera of Xiaomi-CIVI smart phone', 'Huawei-Nova-11-SE and OPPO-A9 smart phones'), but it does not specify any hardware details (like GPU or CPU models, memory) used for running the computational experiments or training models. |
| Software Dependencies | No | The paper references various detection models like YOLOv5, Deformable-DETR, and Mask RCNN, but it does not provide specific version numbers for these or other software dependencies like programming languages or libraries. |
| Experiment Setup | No | The paper states 'For optimization details, we used configurations of Adv-Tshirt [42] and TCA [17] for patch and clothing experiments respectively,' which defers specific hyperparameters to external works rather than detailing them in the main text. |