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..
Attacking Video Recognition Models with Bullet-Screen Comments
Authors: Kai Chen, Zhipeng Wei, Jingjing Chen, Zuxuan Wu, Yu-Gang Jiang312-320
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to verify the effectiveness of the proposed method. On both UCF-101 and HMDB-51 datasets, our BSC attack method can achieve about 90% fooling rate when attacking three mainstream video recognition models, while only occluding <8% areas in the video. |
| Researcher Affiliation | Academia | Kai Chen, Zhipeng Wei, Jingjing Chen Zuxuan Wu, Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Adversarial BSC attack |
| Open Source Code | Yes | Our code is available at https://github.com/kay-ck/BSC-attack. |
| Open Datasets | Yes | We consider two popular benchmark datasets for video recognition: UCF-101 (Su et al. 2009) and HMDB-51 (Kuehne et al. 2011). |
| Dataset Splits | Yes | Both datasets split 70% of the videos as training set and the remaining 30% as test set. |
| Hardware Specification | Yes | Our approach is implemented on a workstation with four GPUs of NVIDIA Ge Force RTX 2080 Ti. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as programming languages, libraries, or frameworks (e.g., PyTorch, TensorFlow, Python version). |
| Experiment Setup | Yes | To strike a balance between FR, AOA and AQN, we set m = 4 and h = 9 to conduct subsequent experiments. ... Therefore, we set λ = 1e 3 so that adversarial BSC attack can achieve the highest FR (%) and the least AQN. ... we set T = Deja V u Serif to achieve the best attack performance for the adversarial BSC attack. ... We optimize the parameters via Adam with a learning rate of 0.03. |