Deepfake Generation and Detection: A Benchmark and Survey

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As the author of this survey on Deepfake Generation and Detection: A Benchmark and Survey, I have systematically organized, categorized, and synthesized relevant research work in the deepfake field. First and foremost, this survey is a comprehensive sorting and integration of the rapidly evolving deepfake domain. When I started the research, I found that existing reviews only covered partial tasks or outdated technologies, while diffusion models, NeRF, and multimodal frameworks were rapidly reshaping the field. Therefore, I aimed to build a unified framework: from task definition, technical evolution, dataset and metric comparison, to method benchmarking and future prospects, so that readers can clearly grasp the full landscape of deepfake generation and detection. From a research perspective, Deepfake technology is a double-edged sword. On the one hand, driven by GANs, diffusion models, and 3D representations like NeRF, face swapping, reenactment, talking-face generation, and facial attribute editing have achieved unprecedented realism, bringing huge value to entertainment, film, digital humans, and content creation. On the other hand, the risk of malicious abuse is increasingly prominent—privacy invasion, identity impersonation, misinformation dissemination, and ethical crises have become urgent issues that cannot be ignored. This contradiction also makes detection technology equally critical and even more challenging. In terms of innovation, this survey has three core values that I highly recognize: It covers the latest technologies for the first time, especially diffusion-based and 3D-aware generation methods, filling the gap in previous reviews; It provides standardized benchmark comparisons for representative methods, using unified datasets and metrics to help researchers objectively evaluate model performance; It systematically summarizes challenges and future directions, pointing out clear paths for generalization, controllability, lightweight deployment, and cross-domain robustness. As a researcher, writing this survey also deepened my thinking about the field, the future of deepfake should be responsible innovation. Generation technology needs to pursue higher fidelity and controllability, while detection technology must keep pace with evolution, achieving stronger generalization and anti-interference ability. At the same time, technological progress must be combined with ethical norms and policy supervision to ensure that AIGC benefits society rather than harm it. In short, this survey is not only a review of the state-of-the-art, but also a reflection on the past, present, and future of the deepfake field. I hope it can become a useful reference for peers, promote more rigorous and innovative research, and boost the healthy and sustainable development of synthetic media technology.

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