信息资源管理学报 ›› 2025, Vol. 15 ›› Issue (2): 137-150.doi: 10.13365/j.jirm.2025.02.137

• 研究论文 • 上一篇    下一篇

AIGC中的深度伪造信息:生成机理与治理策略——基于行动者网络理论的分析框架

冉连 张薇   

  1. 西华大学法学与社会学学院,成都,610039
  • 出版日期:2025-03-26 发布日期:2025-04-11
  • 作者简介:冉连(通讯作者),博士,副教授,硕士生导师,研究方向为社会治理、公共政策,Email: ranlianbrad@163.com;张薇,硕士研究生,研究方向为社会治理。
  • 基金资助:
    本文系国家社会科学基金青年项目“数字政府建设背景下政府数据开放安全治理能力提升研究”(22CZZ035)的研究成果之一。

Deepfake Information in AIGC: Generation Mechanisms and Governance Strategies: An Analytical Framework Based on Actor-Network Theory

Ran Lian Zhang Wei   

  1. School of Law and Sociology,Xihua University,Chengdu,610039
  • Online:2025-03-26 Published:2025-04-11
  • About author:Ran Lian(corresponding author), Ph.D., associate professor, master supervisor, research direction: social governance, public policy, Email: ranlianbrad@163.com; Zhang Wei, master candidate, research direction: social governance.
  • Supported by:
    This article is one of the research results of the youth project "Research on the Improvement of Government Data Openness, Security and Governance Capability in the Context of Digital Government Construction" (22CZZ035), funded by the National Social Science Foundation of China.

摘要: 探讨AIGC中深度伪造信息生成背后的复杂逻辑机理,对于构建深度伪造信息认知框架和制定网络空间靶向治理策略等方面具有重要的现实价值。借鉴行动者网络理论要素,从问题呈现、利益赋予、征召动员、异议排除四方面构建AIGC深度伪造信息生成的理论分析框架,并在此基础上从深度伪造信息行动网络形成、网络结盟、网络稳定三方面诠释了AIGC深度伪造信息生成的动态过程。研究发现,AIGC深度伪造信息的持续输出极可能加剧技术宰制、事实衰落、道德消解等不良社会效应,其生产传播过程是AIGC深度伪造技术通过利益策略转译异质行动者,推动深度伪造利益网络由形成、结盟到稳定的联盟化过程,也是深度伪造利益联盟与其对抗组织的博弈过程,在此基础上从“德治-法治-技治-众治”层面提出了AIGC深度伪造信息的靶向治理策略。

关键词: AIGC, 深度伪造信息, 生成机理, 行动者网络理论, 人技合作

Abstract: Exploring the complex logical mechanisms behind AIGC-driven deepfake information generation has significant practical value for constructing a cognitive framework for understanding deepfake information and formulating targeted governance strategies in cyberspace. Drawing on the actor-network theory, this study constructs a theoretical framework for analyzing AIGC deepfake information generation, focusing on four aspects: problem presentation, allocation of benefits, mobilization, and exclusion of dissent. It further interprets the dynamic process of deepfake information generation in terms of network formation, alliance-building, and stabilization. The findings indicate that the continuous output of AIGC-generated deepfake information is likely to intensify adverse social effects, such as technological domination, truth decay, and moral dissolution. The production and dissemination of deepfake information involve AIGC technologies translating heterogeneous actors through interest-driven strategies, driving the deepfake interest network from formation to stabilization while engaging in a competitive dynamic with opposing organizations. Based on these findings, this study proposes targeted governance strategies for AIGC deepfake information across four dimensions: moral governance, rule of law, technological governance, and crowd-based governance.

Key words: AIGC, Deepfake information, Generative mechanisms, Actor-network theory, Human-technology collaboration

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