深度伪造生成与检测之间的拉锯战
The Tug-of-War Between Deepfake Generation and Detection
AI总结：
本文综述了深度伪造视频生成与检测的对抗现状，分析了生成技术及检测方法，并强调通过多样化数据集、标准化评估和主动合作来应对日益逼真的伪造内容。
摘要：
多模态生成模型正在快速发展，导致逼真视频和音频的生成激增，这带来了令人兴奋的可能性，但也带来了严重风险。深度伪造视频能够令人信服地冒充个人，因其在传播错误信息和制造欺诈内容方面的潜在滥用而尤其受到关注。本综述论文审视了深度伪造视频生成与检测的双重格局，强调了对潜在滥用采取有效对策的必要性。我们全面概述了当前的深度伪造生成技术，包括换脸、表情重演和音频驱动动画，这些技术利用GANs和扩散模型等前沿技术来生成高度逼真的伪造视频。此外，我们分析了各种旨在区分真实视频与篡改视频的检测方法，从检测视觉伪影到部署先进算法以定位视频和音频信号中的不一致之处。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和质量。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。随着深度伪造变得越来越难以与真实内容区分，开发能够跟上生成技术发展的先进检测技术至关重要。我们主张在深度伪造制造者与检测者之间的“拉锯战”中采取主动策略，强调持续的研究合作、评估指标的标准化以及全面基准的创建的必要性。
英文摘要：
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 GANs 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
