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..
Global-Local Characteristic Excited Cross-Modal Attacks from Images to Videos
Authors: Ruikui Wang, Yuanfang Guo, Yunhong Wang
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on the UCF-101 and Kinetics-400 validate the proposed method significantly improves cross-modal transferability and even surpasses stronger baseline using video models as substitute model. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Beihang University, China 2Zhongguancun Laboratory, Beijing, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Global-Local Characteristic Excited Cross Modal Attack. |
| Open Source Code | Yes | Our source codes are available at https://github.com/lwmming/Cross-Modal-Attack. |
| Open Datasets | Yes | Two video recognition datasets, UCF-101 (Soomro, Zamir, and Shah 2012) and Kinetics-400 (Carreira and Zisserman 2017), are used for evaluations. Image Net-pretrained image models. |
| Dataset Splits | No | The paper does not explicitly provide details about train/validation/test dataset splits, or how validation was performed for model training. It mentions 'evaluations' and 'Attack Success Rate', but not specific data splits for validation during training or hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions models like Alex Net, Res Net, Squeeze Net, VGG, TPN, Slow Fast, but does not specify software versions (e.g., Python, PyTorch, CUDA versions) used for implementation. |
| Experiment Setup | Yes | For optimization strategy, we set the maximum perturbations ϵ as 16.0, step size α as 0.005, number of iterations I as 60, λ in Eq. 5 as 0.01. For the intermediate layer l in Eq. 3, we select feature.7 for Alex Net, layer2 for Res Net101, features.6.expand3 3activation for Squeeze Net and features.20 for VGG-16, which is consistent with I2V. In practice, we set n1 as 2 and n2 as 3. |