综述深度伪造生成与检测的前沿技术，分析其优劣与未来方向。
State-of-the-art AI-based Learning Approaches for Deepfake Generation and Detection, Analyzing Opportunities, Threading through Pros, Cons, and Future Prospects
- 系统梳理400篇文献，涵盖GAN、VAE、Transformer等生成方法。
- 在主流数据集上对比评估主流检测方法性能，揭示技术瓶颈。
- 适合关注AI安全、数字内容可信性的研究人员和从业者。
深度伪造技术的迅猛发展，尤其是用于生成高度逼真面部图像与视频内容，已在法医学、网络安全及数字角色创新等多个领域引发广泛关注。通过利用生成对抗网络（GAN）、变分自编码器（VAE）、少样本学习策略和Transformer等深度学习最新突破，生成结果已达到令人惊叹的逼真程度。与此同时，检测技术持续演进，以应对深度伪造可能被滥用带来的风险，包括政治操纵、虚假新闻传播和网络欺凌等问题。本文综述了近400篇相关文献，深入分析深度伪造生成与检测领域的前沿进展。从系统性文献综述方法出发，全面探讨生成技术的技术细节、挑战与解决方案，以及操控形式的复杂性。随后，对主流方法在典型数据集上的表现进行准确基准测试，评估其对相关领域的重要贡献。最后，讨论当前面临的挑战，为该关键且动态的研究领域提供持续发展的路径。
原文摘要 · Abstract (English)
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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