[논문 리뷰] Deepfakes, Misinformation, and Disinformation in the Era of Frontier AI, Generative AI, and Large AI Models

Mohamed R. Shoaib, Zefan Wang외 2명|arXiv (Cornell University)|2023. 11. 29.

Misinformation and Its Impacts인용 수 15

한 줄 요약

이 논문은 프런티어 AI와 대형 생성 모델 시대의 딥페이크, 잘못된 정보 및 허위정보를 조사하고, 탐지, 인증, 플랫폼 간 협력, 정책 조치를 결합한 통합 방어 프레임워크를 제시합니다.

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.

연구 동기 및 목표

• Explain how frontier AI and LM-based GenAI amplify deepfakes and mis/disinformation.
• Review existing detection, authentication, and policy approaches to counter AI-generated content (AIGC).
• Propose an integrated defense framework combining technology, collaboration, policy, and education.
• Highlight ethical, societal, and governance implications and call for multi-stakeholder action.

제안 방법

• Synthesis of current literature on deepfakes, mis/disinformation, and frontier AI.
• Discussion of LM-based GenAI models, training, and content generation capabilities.
• Proposal of an integrated defense framework with technological, strategic, regulatory, and educational components.

실험 결과

연구 질문

• RQ1 How do frontier AI and LM-based GenAI affect the creation and spread of deepfakes and mis/disinformation?
• RQ2 What defense mechanisms—technological, cross-platform, and policy-driven—are required to mitigate AI-generated content threats?
• RQ3 What ethical and societal considerations should guide the development and deployment of GenAI and AIGC?
• RQ4 How can multi-stakeholder collaboration and education enhance resilience against deepfakes and misinformation?

주요 결과

• Detection algorithms and AI-driven authentication methods are essential but face an evolving arms race with deepfake generation.
• Cross-platform collaboration, transparent reporting, and user education are critical for mitigating spread and impact of AIGC.
• An integrated defense framework combining technology, policy, and education can improve resilience against deepfakes and mis/disinformation.
• Open collaboration among academia, industry, and government is necessary to share data, standards, and best practices.