# Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models

arXiv: https://arxiv.org/abs/2311.17394 · HTML: https://mlsanity.com/paper/2311.17394/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2023.

## Built on by (most cited first, 21 of 21)

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- [From Evidence to Verdict: An Agent-Based Forensic Framework for AI-Generated Image Detection](https://mlsanity.com/paper/2511.00181/) (2025)
- [The World of Generative AI: Deepfakes and Large Language Models](https://mlsanity.com/paper/2402.04373/) (2024)
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- [The Ethical Implications of AI in Creative Industries: A Focus on AI-Generated Art](https://mlsanity.com/paper/2507.05549/) (2025)
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- [Toward Accountable AI-Generated Content on Social Platforms: Steganographic Attribution and Multimodal Harm Detection](https://mlsanity.com/paper/2604.10460/) (2026)
- [Challenges in GenAI and Authentication: a scoping review](https://mlsanity.com/paper/2507.11775/) (2025)
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- [MG-RWKV: Multi-Grained Context-Aware RWKV for Temporal Forgery Localization](https://mlsanity.com/paper/2607.00902/) (2026)
- [COMPASS: Contextual Moment Prior Alignment for Generalizable AIGI Detection](https://mlsanity.com/paper/2610.18034/) (2026)

## Abstract (arXiv)

With the advent of sophisticated artificial intelligence (AI) technologies, the proliferation of deepfakes and the spread of m/disinformation have emerged as formidable threats to the integrity of information ecosystems worldwide. This paper provides an overview of the current literature. Within the frontier AI's crucial application in developing defense mechanisms for detecting deepfakes, we highlight the mechanisms through which generative AI based on large models (LM-based GenAI) craft seemingly convincing yet fabricated contents. We explore the multifaceted implications of LM-based GenAI on society, politics, and individual privacy violations, underscoring the urgent need for robust defense strategies. To address these challenges, in this study, we introduce an integrated framework that combines advanced detection algorithms, cross-platform collaboration, and policy-driven initiatives to mitigate the risks associated with AI-Generated Content (AIGC). By leveraging multi-modal analysis, digital watermarking, and machine learning-based authentication techniques, we propose a defense mechanism adaptable to AI capabilities of ever-evolving nature. Furthermore, the paper advocates for a global consensus on the ethical usage of GenAI and implementing cyber-wellness educational programs to enhance public awareness and resilience against m/disinformation. Our findings suggest that a proactive and collaborative approach involving technological innovation and regulatory oversight is essential for safeguarding netizens while interacting with cyberspace against the insidious effects of deepfakes and GenAI-enabled m/disinformation campaigns.