人工智能生成内容技术在内容安全治理领域的风险和对策

doi:10.11959/j.issn.1000-0801.2023190

Abstract

Abstract:

Recently, artificial intelligence generated content (AIGC) technology has achieved various disruptive results and has become a new trend in AI research and application, driving AI into a new era.Firstly, the development status of AIGC technology was analyzed, focusing on generative models such as generative adversarial networks and diffusion models, as well as multimodal technologies, and surveying and elaborating on the existing technological capabilities for text, speech, image and video generation.Then, the risks brought by AIGC technology in the field of content security governance were focused and analyzed, including fake information, content infringement, network and software supply chain security, data leakage and other aspects.Finally, in view of the above security risks, counter strategies were proposed from the technical, application and regulatory levels, respectively.

Key words: AIGC, generative model, multimodal technology, content security governance

CLC Number:

TP399

Zhe QIAO. Risks and countermeasures of artificial intelligence generated content technology in content security governance[J]. Telecommunications Science, 2023, 39(10): 136-146.

Figures/Tables 2

References 38

[1]	李白杨, 白云, 詹希旎 ,等. 人工智能生成内容(AIGC)的技术特征与形态演进[J]. 图书情报知识, 2023(1): 66-74.
	LI B Y , BAI Y , ZHAN X N ,et al. The technical features and aromorphosis of artificial intelligence generated content (AIGC)[J]. Documentation,Information ＆ Knowledge, 2023(1): 66-74.
[2]	CAO Y , LI S , LIU Y ,et al. A comprehensive survey of AI-generated content (AIGC):a history of generative AI from GAN to ChatGPT[J]. arXiv preprint, 2023,arXiv:2303.04226.
[3]	WU J , GAN W , CHEN Z ,et al. AI-generated content (AIGC):a survey[J]. arXiv preprint, 2023,arXiv:2304.06632.
[4]	ADAMOPOULOU E , MOUSSIADES L . An overview of Chatbot technology[C]// IFIP International Conference on Artificial Intelligence Applications and Innovations. Cham:Springer, 2020: 373-383.
[5]	TOUSEEF I , QURESHI S . The survey:text generation models in deep learning[J]. Journal of King Saud University - Computer and Information Sciences, 2022,34(6): 2515-2528.
[6]	詹希旎, 李白杨, 孙建军 . 数智融合环境下 AIGC 的场景化应用与发展机遇[J]. 图书情报知识, 2023(1): 75-85,55.
	ZHAN X N , LI B Y , SUN J J . Application scenarios and development opportunities of AIGC in the digital intelligence integration environment[J]. Documentation,Information ＆ Knowledge, 2023(1): 75-85,55.
[7]	ZHOU X Y , ZAFARANI R . A survey of fake news[J]. ACM Computing Surveys, 2021,53(5): 1-40.
[8]	喻国明, 苏健威 . 生成式人工智能浪潮下的传播革命与媒介生态[J]. 新疆师范大学学报(哲学社会科学版), 2023,44(5): 65-73.
	YU G M , SU J W . Communication revolution and media ecology under the wave of generative artificial intelligence[J]. Journal of Xinjiang Normal University (Edition of Philosophy and Social Sciences), 2023,44(5): 65-73.
[9]	KATZ D M , BOMMARITO M J , GAO S ,et al. GPT-4 passes the bar exam[J]. SSRN Electronic Journal, 2023.
[10]	DONG Q , LI L , DAI D ,et al. A survey on in-context learning[J]. arXiv preprint, 2022,arXiv:2301.00234.
[11]	GOODFELLOW I , POUGET-ABADIE J , MIRZA M ,et al. Generative adversarial networks[J]. Communications of the ACM, 2020,63(11): 139-144.
[12]	KARRAS T , LAINE S , AITTALA M ,et al. Analyzing and improving the image quality of StyleGAN[C]// Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2020: 8107-8116.
[13]	CLARK A , DONAHUE J , SIMONYAN K . Adversarial video generation on complex datasets[J]. arXiv preprint, 2019,arXiv:1907.06571.
[14]	CROITORU F A , HONDRU V , IONESCU R T ,et al. Diffusion models in vision:a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023,45(9): 10850-10869.
[15]	马化腾 . 从专用人工智能迈向通用人工智能[J]. 中国科技产业, 2019(9): 9.
	MA H T . From special artificial intelligence to general artificial intelligence[J]. Science ＆ Technology Industry of China, 2019(9): 9.
[16]	LU J , BATRA D , PARIKH D ,et al. ViLBERT:pretraining task-agnostic visiolinguistic representations for vision-andlanguage tasks[J]. arXiv preprint, 2019,arXiv:1908.02265.
[17]	RADFORD A , KIM J W , HALLACY C ,et al. Learning transferable visual models from natural language supervision[J]. arXiv preprint, 2021,arXiv:2103.00020.
[18]	LI W , GAO C , NIU G C ,et al. UNIMO-2:end-to-end unified vision-language grounded learning[C]// Proceedings of Findings of the Association for Computational Linguistics:ACL 2022. Stroudsburg:Association for Computational Linguistics, 2022: 3187-3201.
[19]	SINGH A , HU R H , GOSWAMI V ,et al. FLAVA:a foundational language and vision alignment model[C]// Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2022: 15617-15629.
[20]	FENG Z D , ZHANG Z Y , YU X T ,et al. ERNIE-ViLG 2.0:improving text-to-image diffusion model with knowledge-enhanced mixture-of-denoising-experts[C]// Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2023: 10135-10145.
[21]	CHEN J , GUO H , YI K ,et al. VisualGPT:data-efficient adaptation of pretrained language models for image captioning[C]// Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway:IEEE Press, 2022: 18009-18019.
[22]	ZHANG T . Deepfake generation and detection,a survey[J]. Multimedia Tools and Applications, 2022,81(5): 6259-6276.
[23]	RAMESH A , DHARIWAL P , NICHOL A ,et al. Hierarchical text-conditional image generation with CLIP latents[J]. arXiv preprint, 2022,arXiv:2204.06125.
[24]	ZHANG L , RAO A , AGRAWALA M . Adding conditional control to text-to-image diffusion models[J]. arXiv preprint, 2023,arXiv:2302.05543.
[25]	ESSER P , CHIU J , ATIGHEHCHIAN P ,et al. Structure and content-guided video synthesis with diffusion models[J]. arXiv preprint, 2023,arXiv:2302.03011.
[26]	SINGER U , POLYAK A , HAYES T ,et al. Make-a-video:text-to-video generation without text-video data[J]. arXiv preprint, 2022,arXiv:2209.14792.
[27]	STUPP C . Fraudsters used AI to mimic CEO’s voice in unusual cybercrime case[J]. The Wall Street Journal, 2019,30(8).
[28]	孙山 . 人工智能生成内容著作权法保护的困境与出路[J]. 知识产权, 2018,28(11): 60-65.
	SUN S . Predicament and outlet of copyright law protection of artificial intelligence-generated content[J]. Intellectual Property, 2018,28(11): 60-65.
[29]	PEARCE H , AHMAD B , TAN B ,et al. Asleep at the keyboard? assessing the security of GitHub copilot’s code contributions[C]// Proceedings of 2022 IEEE Symposium on Security and Privacy (SP). Piscataway:IEEE Press, 2022: 754-768.
[30]	向德生, 杨格兰, 熊岳山 . 数字水印技术研究[J]. 计算机工程与设计, 2005,26(2): 326-328,334.
	XIANG D S , YANG G L , XIONG Y S . Survey of digital watermarking[J]. Computer Engineering and Design, 2005,26(2): 326-328,334.
[31]	KIRCHENBAUER J , GEIPING J , WEN Y ,et al. A watermark for large language models[J]. arXiv preprint, 2023,arXiv:2301.10226.
[32]	吴德阳, 张金羽, 容武艳 ,等. 数字图像水印技术综述[J]. 高技术通讯, 2021,31(2): 148-162.
	WU D Y , ZHANG J Y , RONG W Y ,et al. Survey of digital image watermarking technology[J]. Chinese High Technology Letters, 2021,31(2): 148-162.
[33]	XU C , GUO D , DUAN N ,et al. Baize:an open-source chat model with parameter-efficient tuning on self-chat data[J]. arXiv preprint, 2023,arXiv:2304.01196.
[34]	SHAO R , WU T , LIU Z . Detecting and grounding multi-modal media manipulation[J]. arXiv preprint, 2023,arXiv:2304.02556.
[35]	CHEN J J , BAO Q B , SUN C Z ,et al. LOREN:logic-regularized reasoning for interpretable fact verification[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2022,36(10): 10482-10491.
[36]	ZHOU X Y , WU J D , ZAFARANI R . SAFE:similarity-aware multi-modal fake news detection[C]// Pacific-Asia Conference on Knowledge Discovery and Data Mining. Cham:Springer, 2020: 354-367.
[37]	MRIDHA M F , KEYA A J , HAMID M A ,et al. A comprehensive review on fake news detection with deep learning[J]. IEEE Access, 2021(9): 156151-156170.
[38]	蔡鑫 . 基于 Bert 模型的互联网不良信息检测[J]. 电信科学, 2020,36(11): 121-126.
	CAI X . Internet bad information detection based on Bert model[J]. Telecommunications Science, 2020,36(11): 121-126.

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