State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects

• URL: http://arxiv.org/abs/2501.01029v1
• Date: Thu, 02 Jan 2025 03:19:21 GMT
• Title: State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects
• Authors: Harshika Goyal, Mohammad Saif Wajid, Mohd Anas Wajid, Akib Mohi Ud Din Khanday, Mehdi Neshat, Amir Gandomi,
• Abstract summary: Deepfake technologies are designed to create incredibly lifelike facial imagery and video content. This review paper meticulously investigates the most recent developments in deepfake generation and detection, including around 400 publications.
• Score: 0.0
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• Abstract: The rapid advancement of deepfake technologies, specifically designed to create incredibly lifelike facial imagery and video content, has ignited a remarkable level of interest and curiosity across many fields, including forensic analysis, cybersecurity and the innovative creation of digital characters. By harnessing the latest breakthroughs in deep learning methods, such as Generative Adversarial Networks, Variational Autoencoders, Few-Shot Learning Strategies, and Transformers, the outcomes achieved in generating deepfakes have been nothing short of astounding and transformative. Also, the ongoing evolution of detection technologies is being developed to counteract the potential for misuse associated with deepfakes, effectively addressing critical concerns that range from political manipulation to the dissemination of fake news and the ever-growing issue of cyberbullying. This comprehensive review paper meticulously investigates the most recent developments in deepfake generation and detection, including around 400 publications, providing an in-depth analysis of the cutting-edge innovations shaping this rapidly evolving landscape. Starting with a thorough examination of systematic literature review methodologies, we embark on a journey that delves into the complex technical intricacies inherent in the various techniques used for deepfake generation, comprehensively addressing the challenges faced, potential solutions available, and the nuanced details surrounding manipulation formulations. Subsequently, the paper is dedicated to accurately benchmarking leading approaches against prominent datasets, offering thorough assessments of the contributions that have significantly impacted these vital domains. Ultimately, we engage in a thoughtful discussion of the existing challenges, paving the way for continuous advancements in this critical and ever-dynamic study area.

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