•  
  •  
 

Bulletin of Chinese Academy of Sciences (Chinese Version)

Keywords

artificial intelligence generated content (AIGC);generated content detection;public security;whole-process governance

Document Type

Artificial Intelligence and Public Security

Abstract

The rapid development of artificial intelligence generated content (AIGC) technology has triggered new public security risks, posing a serious threat to national security and social stability. This study exhibits the recent advances of artificial intelligence (AI) content generation and detection techniques, points out the challenges of detection techniques in real-world scenarios, and advocates that it is necessary to develop AIGC detection technology for public security needs and build a whole-process detection technology system from generative models to online platforms, which supports AIGC to be labeled at the generation phase, identifiable during dissemination and source-traceable after the incident occurs.

First page

399

Last Page

407

Language

Chinese

Publisher

Bulletin of Chinese Academy of Sciences

References

1 王祺, 李冬露, 张云, 等. 2023年中国AIGC产业全景报告. 北京: 艾瑞咨询, 2023. Wang Q, Li D L, Zhang Y, et al. 2023 China AIGC Industry Panorama Report. Beijing: iResearch Center, 2023. (in Chinese)

2 Jakesch M, Hancock J T, Naaman M. Human heuristics for AI-generated language are flawed. PNAS, 2023, 120(11): e2208839120.

3 Pocol A, Istead L, Siu S, et al. Seeing is no longer believing: A survey on the state of deepfakes, ai-generated humans, and other nonveridical media// Computer Graphics International Conference. Shanghai: Springer, 2023: 427-440.

4 Mai K T, Bray S, Davies T, et al. Warning: Humans cannot reliably detect speech deepfakes. PLoS One, 2023, 18(8): e0285333.

5 Liang W X, Izzo Z, Zhang Y H, et al. Monitoring AI-modified content at scale: A case study on the impact of ChatGPT on AI conference peer reviews// Proceedings of the 41st International Conference on Machine Learning. Vienna: ML Research Press, 2024: 29575-29620.

6 Mitchell E, Lee Y, Khazatsky A, et al. DetectGPT: Zero-shot machine-generated text detection using probability curvature// Proceedings of the 40th International Conference on Machine Learning. Honolulu: ML Research Press, 2023: 24950-24962.

7 Yang T Y, Huang Z Y, Cao J, et al. Deepfake network architecture attribution. Proceedings of the AAAI Conference on Artificial Intelligence. 2022, 36(4): 4662-4670.

8 Sun Z H, Fang H P, Cao J, et al. Rethinking image editing detection in the era of generative AI revolution// Proceedings of the 32nd ACM International Conference on Multimedia. Melbourne: ACM, 2024: 3538-3547.

9 Shi Y H, Sheng Q, Cao J, et al. Ten words only still help: Improving black-box AI-generated text detection via proxy-guided efficient re-sampling// Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. Jeju: IJCAI, 2024: 494-502.

10 Yang T Y, Wang D D, Tang F, et al. Progressive open space expansion for open-set model attribution// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver: IEEE, 2023: 15856-15865.

11 Yang T Y, Cao J, Wang D D, et al. Model synthesis for zero-shot model attribution. arXiv preprint, 2024, doi: arxiv.org/abs/2307.15977.

12 Fang Q K, Guo S T, Zhou Y, et al. LLaMA-Omni: Seamless speech interaction with large language models. arXiv preprint, 2024, doi: org/10.48550/arXiv.2409.06666.

Share

COinS