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

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Authors: Mohamed R. Shoaib, Zefan Wang, Milad Taleby Ahvanooey, Jun Zhao

Published: 2023-11-29 06:47:58+00:00

AI Summary

This paper reviews the current literature on deepfakes and misinformation, highlighting the threats posed by generative AI. It proposes an integrated framework combining advanced detection algorithms, cross-platform collaboration, and policy initiatives to mitigate the risks of AI-generated content.

Abstract

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.

Key findings

The study suggests that a proactive and collaborative approach is essential for combating deepfakes and AI-generated misinformation. This includes technological innovation, regulatory oversight, and public awareness campaigns. The effectiveness of the proposed integrated framework depends on the synergy between technological solutions, strategic initiatives, policy and regulation, and public education.

Approach

The paper proposes an integrated framework to combat deepfakes and misinformation. This framework combines advanced detection algorithms (including audio and video analysis), cross-platform collaboration among stakeholders, and policy-driven initiatives to address the challenges posed by AI-generated content.

Datasets

UNKNOWN

Model(s)

The paper mentions the use of machine learning models for deepfake detection, but doesn't specify particular models/architectures.

Author countries

Singapore

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