Multi-Target Invisibly Trojaned Networks for Visual Recognition and Detection
Authors: Xinzhe Zhou, Wenhao Jiang, Sheng Qi, Yadong Mu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments are conducted on the datasets PASCAL VOC and Microsoft COCO (for detection) and a subset of Image Net (for recognition). All results clearly demonstrate the effectiveness of our proposed visual trojan method. |
| Researcher Affiliation | Collaboration | Xinzhe Zhou1 , Wenhao Jiang2 , Sheng Qi1 and Yadong Mu1 1Wangxuan Institute of Computer Technology, Peking University 2Tencent AI Lab |
| Pseudocode | No | The paper describes the proposed method conceptually and through figures and equations, but it does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement about making the source code available or provide a link to a code repository for the described methodology. |
| Open Datasets | Yes | For image recognition, we conduct the experiments using a 100-class subset of the full large-scale Image Net benchmark [Russakovsky et al., 2015] (denoted as Image Net-100)... For visual object detection, two representative benchmarks (PASCAL VOC [Everingham et al., 2010] and MSCOCO [Lin et al., 2014]) are used. |
| Dataset Splits | No | The paper mentions datasets used for training and testing but does not explicitly state the training, validation, and test dataset splits needed for reproduction, nor does it cite predefined splits. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory) used to run the experiments. It does not mention any hardware specifications for training or evaluation. |
| Software Dependencies | No | The paper does not provide specific software dependencies or their version numbers (e.g., deep learning frameworks with versions, programming languages with versions, specific libraries) that would be needed to reproduce the experiment. |
| Experiment Setup | No | The paper mentions the use of Adam optimizer and certain loss functions but does not provide specific hyperparameter values such as learning rate, batch size, or number of epochs, which are essential for reproducing the experimental setup. |