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..
BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection
Authors: Saket S. Chaturvedi, Lan Zhang, Wenbin Zhang, Pan He, Xiaoyong Yuan
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Evaluation In this section, we first detail our experimental framework (dataset, implementation & training details, evaluation metrics) and then present the evaluation results of the proposed Bad Fusion. We further demonstrate the effectiveness of Bad Fusion against mainly Point-line Camera-to-Li DAR fusion methods in 3D object detection and also benchmark our approach against three state-of-the-art backdoor detection methods. Lastly, we conduct an ablation study to elucidate the internal mechanics of the Bad Fusion. |
| Researcher Affiliation | Academia | 1Clemson University 2Florida International University 3Auburn University |
| Pseudocode | Yes | Algorithm 1 Algorithm Procedure of Bad Fusion |
| Open Source Code | No | The paper states: 'We implement Untar OD based on their open-source code3' with a link to a third-party repository. There is no explicit statement or link indicating that the code for their proposed method (Bad Fusion) is open-source or publicly available. |
| Open Datasets | Yes | We use the KITTI dataset [Geiger et al., 2013] in the evaluation. |
| Dataset Splits | Yes | we split the training data into a train set and a validation set with 3, 712 and 3, 769 samples, respectively, following the train/valid split process in previous work [Chen et al., 2016]. |
| Hardware Specification | No | The paper does not explicitly specify the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions software components like 'Focal Loss', 'Smooth L1Loss', 'Adam W optimizer', and 'mmdetection3d' but does not provide specific version numbers for these or other key software dependencies required for replication. |
| Experiment Setup | Yes | The fusion models are trained using an Adam W optimizer with a learning rate of 0.002 and a weight decay parameter of 0.01 for 70 epochs. |