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网络出版日期： 2024-12-23 ，
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姚文达,李盼池,赵娅等.人脸深度伪造检测方法研究综述[J].中国图象图形学报,
Yao Wenda,Li Panchi,Zhao Ya,et al.A review of research on face Deepfake detection methods[J].Journal of Image and Graphics,
深度伪造技术是一种基于深度学习的合成技术，旨在生成高度逼真的合成图像、音频或视频，包括人脸伪造、声音模仿和人体姿态合成等内容。其中，人脸深度伪造可以实现非常逼真的换脸效果，广泛应用于电影、动画制作等领域。然而，该技术的滥用同时也导致不雅视频与虚假新闻的传播，带来了恶劣的社会影响。为应对这些负面影响，众多学者提出了一系列检测方法，以有效识别伪造人脸图像或视频。然而，当前的检测方法类型多样、优缺点各异，且应用场景各不相同，鉴于此，对相关研究进行了系统的归纳与整理。首先，整理了人脸深度伪造检测常用数据集与评价指标；其次，从图像级伪造人脸检测与视频级伪造人脸检测两个领域出发，根据特征选择的不同，将前者划分为基于空间域和基于频率域的检测方法，将后者划分为基于时空不一致、基于生物特征和基于多模态的检测方法，并详细总结了各类方法的原理、优缺点及发展趋势，特别地，考虑到最近两年文本生成图像/视频的流行以及生成式人工智能在多模态创作上的显著进步，综述了针对文本生成图像/视频的检测方法和基于多模态的检测方法；最后，梳理了人脸深度伪造检测领域的研究现状及瓶颈，并对未来的研究与发展方向进行了探讨。
Deepfake technology refers to the synthesis of images， audio， and videos using deep learning algorithms. This technology enables the precise mapping of facial features or other physical characteristics from one person onto a subject in another video， achieving highly realistic face-swapping effects. With advancements in algorithms and the increased accessibility of computational resources， the threshold for utilizing Deepfake technology has gradually lowered， which brings both convenience and numerous social and legal challenges. For example， Deepfake technology is used to bring deceased actors back to the screen， providing a novel experience for audiences. On the other hand， it is often exploited to impersonate citizens or leaders for fraudulent activities， produce pornographic content， or create fake news to influence public opinion. Consequently， the importance of Deepfake detection technology is growing， making it a significant focus of current research. To detect images and videos synthesized by Deepfake technology， researchers need to design models that can uncover subtle traces of manipulation within these media. However， accurately identifying these traces remains challenging due to several factors that complicate the detection process. Firstly， the rapid advancements in Deepfake technology have made it increasingly difficult to differentiate fake images and videos from authentic content. As techniques like generative adversarial networks （GANs） and diffusion models continue to evolve and improve， the texture， lighting， and motion within synthesized media become more seamlessly realistic， which imposes significant challenges on detection models that seek to recognize subtle cues of manipulation. Secondly， forgers can employ a variety of countermeasures to obscure these traces of manipulation， such as applying compression， cropping， or noise addition. Additionally， forgers may create adversarial samples specifically crafted to exploit and bypass detection models’ vulnerabilities， making the identification of Deepfakes even more complex. Thirdly， the generalizability of Deepfake detection methods remains a significant hurdle， as different generative techniques leave behind distinct forensic traces. For instance， GAN-generated images often exhibit prominent grid-like artifacts in the frequency domain， while images produced through diffusion models typically leave only subtle， less detectable traces in this domain. Therefore， it is crucial that detection models do not exclusively rely on low-level， technique-specific features but instead focus on capturing deep， generalized features that ensure robustness and applicability across diverse forgery types and detection scenarios. To address these multifaceted challenges， numerous scholars have proposed a variety of detection methods designed to capture the nuanced traces left by Deepfake manipulations. For instance， certain approaches concentrate on identifying subtle forgery artifacts within the frequency domain of images， capitalizing on the distinct spectral anomalies that forgeries often introduce. Other methods prioritize assessing the temporal consistency across video frames， as unnatural transitions or frame-level inconsistencies can indicate synthesized content. Additionally， some detection strategies focus on evaluating the synchronization between different modalities within videos—such as audio and visual elements—to detect inconsistencies that may reveal forgery. Currently， several review papers in academia have summarized key research and developments within this domain. However， due to the rapid advancements in generative artificial intelligence， fake faces created with diffusion models have recently gained popularity， yet there is scarcely any review that addresses the detection of such forgeries. Furthermore， as generative AI progresses continues advancing toward multimodal integration， Deepfake detection methods are similarly evolving to incorporate features from multiple modalities. Nonetheless， the majority of existing reviews lack sufficient focus on multimodal detection approaches， underscoring a gap in the literature that this review seeks to address. To provide an up-to-date overview of face Deepfake detection， this review first organizes commonly used datasets and evaluation metrics in the field. Then， it divides the detection methods into image-level and video-level face Deepfake detection. Based on feature selection approaches， image-level methods are categorized into spatial-domain and frequency-domain methods， while video-level methods are categorized into approaches based on spatiotemporal inconsistencies， biological features， and multimodal features. Each category is thoroughly analyzed regarding its principles， strengths， weaknesses， and developmental trends. Finally， the current research status and challenges in face Deepfake detection are summarized， and future research directions are discussed. Compared with other related reviews， the novelty of this review lies in its summary of detection methods specifically targeting text-to-image/video generation and multimodal detection methods. This review aligns with the latest trends in generative artificial intelligence， offering a thorough and up-to-date summary of recent advancements in face Deepfake detection. By examining the latest methodologies， including those developed to address forgeries created through advanced techniques like diffusion models and multimodal integration， this review reflects the ongoing evolution of detection technology. It highlights both the progress made and the challenges that remain， positioning itself as a valuable resource for researchers aiming to navigate and contribute to the cutting-edge developments in this rapidly advancing field. A comprehensive analysis of face Deepfake detection methods reveals that current techniques achieve nearly 100% accuracy within the training datasets， particularly those leveraging advanced models like Transformers. However， their performance often declines significantly in cross-dataset testing， especially for spatial-domain and frequency-domain detection methods. This decline suggests that these approaches may fail to capture essential， generalizable features that are robust across varying datasets. By contrast， biological feature-based methods demonstrate superior generalization capabilities， successfully adapting to different contexts， yet they require carefully tailored training data and specific application conditions to reach optimal performance. Meanwhile， multimodal detection methods， which integrate features across multiple modalities， offer enhanced robustness and adaptability due to their layered approach； however， this added complexity often results in higher computational costs and increased model intricacy. Given the diversity in feature selection， alongside the unique advantages and limitations inherent to each detection approach， no single method yet provides a fully comprehensive solution to the Deepfake detection challenge. This reality underscores the critical need for continued research in this evolving field and highlights the importance of this review in mapping current advancements and identifying future research directions.
深度伪造检测人脸伪造检测人脸图像人脸视频空间域特征频率域特征时序特征多模态特征
Deepfake detectionface forgery detectionface imageface videospatial domain featurefrequency domain featuretemporal featuresmultimodal features
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