Generative Propaganda
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Abstract (arXiv)
Generative propaganda is the use of generative artificial intelligence (AI) to shape public opinion. To characterize its use in real-world settings, we conducted interviews with defenders (e.g., factcheckers, journalists, officials) in Taiwan and creators (e.g., influencers, political consultants, advertisers) as well as defenders in India, centering two places characterized by high levels of online propaganda. The term "deepfakes", we find, exerts outsized discursive power in shaping defenders' expectations of misuse and, in turn, the interventions that are prioritized. To better characterize the space of generative propaganda, we develop a taxonomy that distinguishes between obvious versus hidden and promotional versus derogatory use. Deception was neither the main driver nor the main impact vector of AI's use; instead, Indian creators sought to persuade rather than to deceive, often making AI's use obvious in order to reduce legal and reputational risks, while Taiwan's defenders saw deception as a subset of broader efforts to distort the prevalence of strategic narratives online. AI was useful and used, however, in producing efficiency gains in communicating across languages and modes, and in evading human and algorithmic detection. Security researchers should reconsider threat models to clearly differentiate deepfakes from promotional and obvious uses, to complement and bolster the social factors that constrain misuse by internal actors, and to counter efficiency gains globally.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 111 references that are in the map. First posted on arXiv 23 Sep 2025.
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17 of 111 references.
References · 17
- 2023Multilingual Jailbreak Challenges in Large Language Modelscited by 240
- 2023Invisible Image Watermarks Are Provably Removable Using Generative AIcited by 134
- 2023Watermarks in the Sand: Impossibility of Strong Watermarking for Generative Modelscited by 87
- 2023A Representative Study on Human Detection of Artificially Generated Media Across Countriescited by 22
- 2024An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscapecited by 16
- 2022Are Deepfakes Concerning? Analyzing Conversations of Deepfakes on Reddit and Exploring Societal Implicationscited by 14
- 2024SoK: Systematization and Benchmarking of Deepfake Detectors in a Unified Frameworkcited by 14
- 2022On the Horizon: Interactive and Compositional Deepfakescited by 13
- 2023AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detectioncited by 12
- 2022YouTube, The Great Radicalizer? Auditing and Mitigating Ideological Biases in YouTube Recommendationscited by 10
- 2024Analyzing the AI Nudification Application Ecosystemcited by 10
- 2023Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donationscited by 8
5 more
- 2024Characterizing the MrDeepFakes Sexual Deepfake Marketplacecited by 6
- 2024Exploring Parent-Child Perceptions on Safety in Generative AI: Concerns, Mitigation Strategies, and Design Implicationscited by 5
- 2020Why Older Adults (Don't) Use Password Managerscited by 2
- 2024Understanding Parents' Perceptions and Practices Toward Children's Security and Privacy in Virtual Realitycited by 2
- 2025Journalists' Perceptions of Artificial Intelligence and Disinformation Riskscited by 1
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