卧底深度伪造：检测视频中的伪造片段
Undercover Deepfakes: Detecting Fake Segments in Videos
AI总结：
针对现有检测方法对“仅轻微篡改片段的部分伪造视频”研究不足的问题，本文提出结合空间与时序Transformer的帧级+视频级检测方法，构建含细微过渡的新基准数据集，实验表现优异，可助力深度伪造精准审核。
摘要：
生成模型近期的复兴主要由扩散模型的出现与GAN方法的迭代改进推动，催生了众多创意应用。然而，每一项技术进步也伴随着滥用潜力的上升。在深度伪造生成领域，这是一个关键的社会问题。特别是，利用此类生成技术修改视频片段的能力催生了一种新的深度伪造范式：这类伪造视频大多为真实视频，仅经过轻微篡改以歪曲事实。当前学术文献中的深度伪造检测方法对这一范式的研究仍显不足。\n本文提出了一种深度伪造检测方法，可通过在帧级别和视频级别执行深度伪造预测来解决这一问题。为便于测试我们的方法，我们构建了一个新的基准数据集，其中的视频同时包含真实帧序列和伪造帧序列，且二者之间的过渡极其细微。我们在该数据集上提供了基准测试结果：我们的检测方法采用基于缩放与移位的Vision Transformer（视觉Transformer）学习空间特征，并使用时序Transformer学习视频的时间特征，以辅助解析可能存在的深度伪造内容。在多种深度伪造生成方法上开展的大量实验表明，所提方法在时序分割以及经典的视频级别预测任务上均取得了优异结果。\n尤其值得一提的是，我们所研究的这一范式将为深度伪造内容审核提供有力工具，帮助人工审核更精准地聚焦于视频中疑似深度伪造的部分。所有实验均可在以下地址复现：github.com/rgb91/temporal-deepfake-segmentation。
英文摘要：
The recent renaissance in generative models, driven primarily by the advent of diffusion models and iterative improvement in GAN methods, has enabled many creative applications. However, each advancement is also accompanied by a rise in the potential for misuse. In the arena of the deepfake generation, this is a key societal issue. In particular, the ability to modify segments of videos using such generative techniques creates a new paradigm of deepfakes which are mostly real videos altered slightly to distort the truth. This paradigm has been under-explored by the current deepfake detection methods in the academic literature. In this paper, we present a deepfake detection method that can address this issue by performing deepfake prediction at the frame and video levels. To facilitate testing our method, we prepared a new benchmark dataset where videos have both real and fake frame sequences with very subtle transitions. We provide a benchmark on the proposed dataset with our detection method which utilizes the Vision Transformer based on Scaling and Shifting to learn spatial features, and a Timeseries Transformer to learn temporal features of the videos to help facilitate the interpretation of possible deepfakes. Extensive experiments on a variety of deepfake generation methods show excellent results by the proposed method on temporal segmentation and classical video-level predictions as well. In particular, the paradigm we address will form a powerful tool for the moderation of deepfakes, where human oversight can be better targeted to the parts of videos suspected of being deepfakes. All experiments can be reproduced at: github.com/rgb91/temporal-deepfake-segmentation.
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。
