Attacking Video Recognition Models with Bullet-Screen Comments

Authors: Kai Chen, Zhipeng Wei, Jingjing Chen, Zuxuan Wu, Yu-Gang Jiang312-320

AAAI 2022 | Conference PDF | Archive PDF | Plain Text | 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 {kaichen20, chenjingjing, zxwu, ygj}@fudan.edu.cn, zpwei21@m.fudan.edu.cn
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.