ACIS 2024 Proceedings

Abstract

The proliferation of deepfakes poses significant challenges to information integrity and societal trust. At the same time, advancements in Generative Artificial Intelligence (GenAI) lower the technological barriers to create deepfake misinformation and complicate its detection. This study presents a scoping review of prevailing solutions and mitigation strategies to combat deepfake issues across three dimensions of misinformation combat: (1) prevention, (2) detection, and (3) debunking. After reviewing 58 papers from both Information Systems (IS) and Computer Science (CS) literature, we mapped the current state of research on deepfake misinformation combat and found that the literature is primarily concerned with technical detection techniques, particularly those leveraging advanced Artificial Intelligence (AI) and machine learning (ML) models. At the same time, social and socio-technical prevention and debunking strategies remain underexplored. We identify critical research gaps, including the need for integrated multi-phase approaches, non-technical prevention strategies, and cross-cultural considerations in deepfake misinformation perception and mitigation. Our work offers valuable insights for researchers, policymakers, and practitioners seeking to develop more robust, adaptive, and contextually aware strategies to conquer the evolving threats posed by deepfakes.

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