Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Revisiting Adversarial Patches for Designing Camera-Agnostic Attacks against Person Detection
Authors: Hui Wei, Zhixiang Wang, Kewei Zhang, Jiaqi Hou, Yuanwei Liu, Hao Tang, Zheng Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our proposed Camera-Agnostic Patch (CAP) attack effectively conceals persons from detectors across various imaging hardware, including two distinct cameras and four smartphones. |
| Researcher Affiliation | Academia | 1National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University 2The University of Tokyo 3School of Computer Science, Peking University |
| Pseudocode | Yes | Algorithm 1 The proposed adversarial optimization ( Attacker and Defender) |
| Open Source Code | No | The source code will be made available upon acceptance of the paper. |
| Open Datasets | Yes | We use the INRIAPERSON dataset [4, 30] to evaluate digital-space attacks. ... YOLOv5 [17] model pre-trained on the COCO dataset [20] |
| Dataset Splits | No | The paper mentions 613 training images and 288 test images for the INRIAPerson dataset but does not explicitly specify a validation set split. |
| Hardware Specification | Yes | Our implementation utilizes PyTorch on a Linux server equipped with dual NVIDIA GeForce RTX 3090 GPUs. |
| Software Dependencies | No | Our implementation utilizes PyTorch. However, no specific version number for PyTorch or any other software dependency is provided. |
| Experiment Setup | Yes | The adversarial patches are configured with dimensions of 300 × 300, and we employ a YOLOv5 [17] model pre-trained on the COCO dataset [20] and subsequently fine-tuned on INRIAPerson [30] as our victim detector. The detector processes input images at a resolution of 640 × 640, and adversarial training proceeds for 100 epochs. |