Multimodal generative models are rapidly evolving, leading to a surge in the generation of realistic video and audio that offers exciting possibilities but also serious risks. Deepfake videos, which can convincingly impersonate individuals, have particularly garnered attention due to their potential misuse in spreading misinformation and creating fraudulent content. This survey paper examines the dual landscape of deepfake video generation and detection, emphasizing the need for effective countermeasures against potential abuses. We provide a comprehensive overview of current deepfake generation techniques, including face swapping, reenactment, and audio-driven animation, which leverage cutting-edge technologies like generative adversarial networks and diffusion models to produce highly realistic fake videos. Additionally, we analyze various detection approaches designed to differentiate authentic from altered videos, from detecting visual artifacts to deploying advanced algorithms that pinpoint inconsistencies across video and audio signals. The effectiveness of these detection methods heavily relies on the diversity and quality of datasets used for training and evaluation. We discuss the evolution of deepfake datasets, highlighting the importance of robust, diverse, and frequently updated collections to enhance the detection accuracy and generalizability. As deepfakes become increasingly indistinguishable from authentic content, developing advanced detection techniques that can keep pace with generation technologies is crucial. We advocate for a proactive approach in the "tug-of-war" between deepfake creators and detectors, emphasizing the need for continuous research collaboration, standardization of evaluation metrics, and the creation of comprehensive benchmarks.

翻译：多模态生成模型正在快速发展，催生了逼真视频与音频的生成浪潮，这既带来了令人兴奋的可能性，也伴随着严重的风险。深度伪造视频能够令人信服地模仿特定个人，因其在传播虚假信息和制作欺诈内容方面的潜在滥用而受到特别关注。本综述论文审视了深度伪造视频生成与检测的双重格局，强调了对潜在滥用行为采取有效对策的必要性。我们对当前的深度伪造生成技术进行了全面概述，包括人脸交换、面部重演和音频驱动动画，这些技术利用生成对抗网络和扩散模型等前沿技术来制作高度逼真的伪造视频。此外，我们分析了旨在区分真实视频与篡改视频的各种检测方法，从检测视觉伪影到部署能够精确定位视频与音频信号间不一致性的高级算法。这些检测方法的有效性在很大程度上依赖于用于训练和评估的数据集的多样性与质量。我们讨论了深度伪造数据集的演变，强调了构建稳健、多样且频繁更新的数据集对于提高检测准确性和泛化能力的重要性。随着深度伪造内容与真实内容越来越难以区分，开发能够跟上生成技术发展步伐的先进检测技术至关重要。我们主张在深度伪造创建者与检测者之间的"博弈"中采取积极主动的策略，强调持续的研究合作、评估指标的标准化以及创建全面基准的必要性。