A Survey on Deepfake Video Detection

IET BiometricsVolume 10, Issue 6 p. 607-624 REVIEWOpen Access A Survey on Deepfake Video Detection Peipeng Yu, orcid.org/0000-0003-0056-4300 Engineering Research Centre of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Jiangsu Province, Nanjing, ChinaSearch for more papers by this authorZhihua Xia, Corresponding Author xia_zhihua@163.com College of Cyber Security, Jinan University, Guangzhou, China Engineering Research Center of Digital Forensics, Nanjing University of Information Science & Technology, Nanjing, China Correspondence Zhihua Xia, College of Cyber Security, Jinan University, Guangzhou, Guangdong Province, China Province, 510632, China. Email: xia_zhihua@163.comSearch for more papers by this authorJianwei Fei, Engineering Research Centre of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Jiangsu Province, Nanjing, ChinaSearch for more papers by this authorYujiang Lu, Engineering Research Centre of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Jiangsu Province, Nanjing, ChinaSearch for more papers by this author Peipeng Yu, orcid.org/0000-0003-0056-4300 Engineering Research Centre of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Jiangsu Province, Nanjing, ChinaSearch for more papers by this authorZhihua Xia, Corresponding Author xia_zhihua@163.com College of Cyber Security, Jinan University, Guangzhou, China Engineering Research Center of Digital Forensics, Nanjing University of Information Science & Technology, Nanjing, China Correspondence Zhihua Xia, College of Cyber Security, Jinan University, Guangzhou, Guangdong Province, China Province, 510632, China. Email: xia_zhihua@163.comSearch for more papers by this authorJianwei Fei, Engineering Research Centre of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Jiangsu Province, Nanjing, ChinaSearch for more papers by this authorYujiang Lu, Engineering Research Centre of Digital Forensics, Ministry of Education, School of Computer and Software, Jiangsu Engineering Centre of Network Monitoring, Jiangsu Collaborative Innovation Centre on Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Jiangsu Province, Nanjing, ChinaSearch for more papers by this author First published: 09 April 2021 https://doi.org/10.1049/bme2.12031Citations: 2AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Recently, deepfake videos, generated by deep learning algorithms, have attracted widespread attention. Deepfake technology can be used to perform face manipulation with high realism. So far, there have been a large amount of deepfake videos circulating on the Internet, most of which target at celebrities or politicians. These videos are often used to damage the reputation of celebrities and guide public opinion, greatly threatening social stability. Although the deepfake algorithm itself has no attributes of good or evil, this technology has been widely used for negative purposes. To prevent it from threatening human society, a series of research have been launched, including developing detection methods and building large-scale benchmarks. This review aims to demonstrate the current research status of deepfake video detection, especially, generation process, several detection methods and existing benchmarks. It has been revealed that current detection methods are still insufficient to be applied in real scenes, and further research should pay more attention to the generalization and robustness. 1 INTRODUCTION The problem of face-manipulated videos has received widespread attention in the past two years, especially after the advent of deepfake technology that manipulates images and videos with deep learning tools. Deepfake algorithm can replace faces in the target video with faces in the source video using autoencoders or generative adversarial networks. With this technology, face-manipulated videos are exceedingly simple to generate on condition that one can access large amounts of data. While deepfake technology could be used for positive purposes, such as film-making and virtual reality, it is still heavily applied for malicious uses [1-4]. As shown in Figure 1, a huge number of fake videos have been distributed on the Internet, most of which target at politicians and celebrities. The first deepfake content was a celebrity pornographic video created by a Reddit user named deepfakes in 2017, which means that it is unavoidable for deepfake technology to be used for malicious uses since created. Soon after, FakeApp, FaceSwap and other deepfake-based applications appeared continuously. In June 2019, there is even a smart undressing app named Deepnude, resulting in a huge panic around the world. Except for damaging personal privacy, videos generated by these apps are increasingly applied to interfere in political campaigns and public opinion. The detection of deepfake content has become one of the hot issues for individuals, businesses and governments around the world. FIGURE 1Open in figure viewerPowerPoint Video frames generated by deepfake algorithms. The first line shows the original video frames and the second line shows the corresponding video frames generated by deepfake methods With the increasing interest in deepfake technology, more and more related researches have been underway. The past two years have witnessed significant progress in developing new detection methods. To begin with, the number of video datasets built for deepfake detection tasks is growing. From small datasets (such as DeepFake-TIMIT [5] and UADFV [6]) in an early stage, to large-scale datasets (such as FaceForensic++ [7], Celeb-DF [8], DFDC [9] and DeeperForensic [10]), the number of datasets that can be used for training has increased. Furthermore, several research institutions are becoming aware of the dangers of deepfake videos and trying to promote related research. Recently, Amazon, Facebook and Microsoft have joined forces to host the deepfake detection challenge (DFDC) to build innovative technologies beneficial to detect deepfake videos. Also, SenseTime holds the deeperforensics challenge 2020 to solicite new ideas to advance the state-of-the-art in real-world face forgery detection. Owing to these reasons, several effective detection approaches have been proposed, which demonstrated excellent performance in forgery detection tasks. Though several advances have been achieved, many critical issues for existing deepfake detection methods still need to be solved. With the continuous evolution of deepfake methods, generated videos become more and more realistic. In this case, traditional methods are probably not suitable for detecting manipulated videos generated by new deepfake algorithms [11]. It is significant to analyse and forecast the advanced development of deepfake-related research and improve corresponding detection approaches. In this review, we will focus on the existing detection scheme designed for deepfake videos, attempting to promote the development of deepfake video detection. This article is organized as follows. In Section 2, we first introduce deepfake video generation algorithms proposed in recent years. Then, different types of detection approaches are described in Section 3. A list of datasets used in recent study is presented in Section 4. After that, a discussion of the state of deepfake video detection and its perspectives is carried out in Section 5. Finally, we conclude in Section 6. 2 DEEPFAKE VIDEO GENERATION Since the first release of deepfake videos, new manipulation algorithms are proposed soon, most of which are based on generative networks. During these methods, deepfake algorithms can be used to create fake content to infringe on personal privacy, showing huge destructive effect on society. This section will review the development of deepfake algorithms and then describe two types of deepfake algorithms. 2.1 Development of deepfake technologies Facemanipulation is not a new technology that appeared recently. The earliest attempt with facemanipulation in the literature can be found in the iconic 1865 portrait of the US President Abraham Lincoln. With the development of computer graphics technology, facemanipulation in digital images has become easily achievable [12-14]. Recent progress in the field of deep learning has fundamentally advanced the development of facemanipulation technology. According to different goals of face manipulation algorithms, existing deepfake algorithms could be divided into two categories: face swapping and face reenactment. 2.1.1 Face swapping Face swapping videos, swapping person identities in two videos, have attracted people's attention in recent years. Related researches have been established since 2017. In the study of Korshunova et al. [15], convolutional neural networks (CNNs) were trained to capture the appearance of target identity from an unstructured photo collection, which enables generating high-quality face-swapping images. However, time continuity is not considered, thus this approach cannot be applied for high-quality video generation. In the same year, Olszewski et al. [16] proposed a novel approach to generate videos with a single RGB image and a source video sequence. A deep generative network was used to infer perframe texture deformations of the target identity using source textures and the single target texture. Based on this method, the newly rendered face could be composited onto the source video, replacing the original face using the schema of [17]. In December 2017, the first face-swapping video generated by deepfake approach was posted by a Reddit user, bringing marvellous shock to the world. It is generally acknowledged that the inspiration of deepfake algorithms comes from [15], where CNNs were used to generate face-swapping images. After that, a wave of creating face-swapping videos was set off over the world, regardless of positive or negative purposes. Faceswap-GAN, an improved version of the original deepfake algorithms, was proposed in [18]. To generate more realistic faces, adversarial loss and perceptual loss were added to improve the performance of the autoencoder implemented by VGGFace [19]. Similarly, DeepFaceLab [20], an open-source deepfake generation framework, was designed for providing an imperative and easy-to-use pipeline for people without professional knowledge. In recent works, FaceShifter was proposed for occlusion aware face swapping with high fidelity [21]. Unlike previous face-swapping studies only using limited information from target images to synthesise faces, FaceShifter generates high-fidelity swapped faces by performing comprehensive integration of face attributes. Specifically, AEI-Net and HEAR-Net were leveraged to integrate face information and recover an anomaly region, respectively. Experiments show its superior performance compared with existing face-swapping algorithms. Videos generated by recent deepfake approaches have been extremely realistic, hardly distinguished by human eyes. 2.1.2 Face reenactment Different from face-swapping technologies, face reenactment algorithms attempt to control people's expressions in videos, whichmeans that attackers can generate videos manipulating someone to do something that does not exist. The first face reenactment algorithm could date back to 2006. Vlasic et al.[22] proposed to perform facial reenactment based on the face template, which was modified under different expression parameters. Most of the subsequent work is based on such schemes, where a parametric model is leveraged to adjust facial images. These methods could generate face images with high realism, but the obtained results often lack temporal coherence. In recent years, research on face reenactment has been further developed as computing ability increased. To perform monocular facial reenactment in real-time, Thies et al. [23] proposed Face2Face. In this study, a new global nonrigid model-based bundling approach was applied to reconstruct the facial features of target and source actors. At the same time, a subspace deformation transfer technique is designed to perform expression transfer between source and target actors. In addition to these contributions, this study also proposed a novel method in the synthesis of mouth regions, where the best matching image is retrieved from the target sequence. Compared to previous studies, Face2Face has already achieved quite remarkable performance. However, it cannot guarantee consistent head movements as only the migration of expressions is taken into account. Also, the synthesis of the mouth region is not satisfying, with coarse details of the mouth that are easily noticed by human eyes. With the development of deep learning techniques, these issues are gradually being noticed and addressed. It can be noticed that face videos synthesised by previous face reenactment algorithms have defects that they are inconsistent with voices. The work of Suwajanakorn et al. [24] supplemented this defect to a certain extent. They aimed to learn a sequence mapping from audio to video to manipulate actors to speak the same sentences as voice content. Features were extracted from voice sequence as the input of recurrent neural network (RNN), which outputs a sparse mouth shape corresponding to each frame of video output. The textures of mouth are further synthesized and merged into the original video. A better improvement is achieved by Fried et al. [25]. They performed talking-head video editing and changing speech words by using designed neural face rendering method. To perform face reenactment with better performance, Kim et al. [26] proposed a new method for photorealistic reanimation of portrait videos. The proposed generative neural network with a novel space-time architecture is used to transform coarse face model renderings into full photorealistic portrait video output. The major contribution of this study is designing a new spatiotemporal encoding as conditional input for video synthesis, resulting in synthesised videos with a high degree of spatiotemporal continuity. Compared to Face2Face, this work can not only migrate facial expressions, but also head pose, gaze direction and blinking movements, compensating for the inaccurate head pose in the Face2Face algorithm. Except for this study, Thies et al. [27] has also made further optimisations to address problems existing in Face2Face. Neuraltexture, incorporating Face2Face and neural networks for texture extraction based on Face2Face, compensating for Face2Face's blurred texture in the mouth region. 2.2 General process of deepfake video generation In this part, we will briefly describe the generation process of two types of deepfake videos. 2.2.1 Face swapping To generate a face-swapping video, all frames of the target video have to be processed using generative method. Figure 2 shows the general generation process of face-swapping videos. Obviously, the deepfake algorithm, which implements faceswapping while preserves the source expressions, is the core part of video generation. The deepfake algorithms used in faceswapping are mostly developed based on autoencoder, which is widely used for data reconstruction tasks. Autoencoder is composed of two components: an encoder and a decoder. Latent features are first extracted from the image by the encoder, and then inputted to the decoder to reconstruct the original image. In the deepfake algorithm, two autoencoders are trained to swap faces between source video frames and target video frames. As shown in Figure 3, during the training process, two encoders with the same weights are trained to extract common features in source and target faces. Then, features extracted are inputted to two decoders to reconstruct faces, respectively. It is worth noting that decoder A is only trained with faces of A while decoder B is only trained with faces ofB. When the training process is complete, a latent face generated from face A will be passed to the decoder B. Decoder B would try to reconstruct face B from feature relative to faceA. If the autoencoder is trained well, the latent space will represent facial expressions. In other words, the face generated by decoder B will have the same expression as faceA. FIGURE 2Open in figure viewerPowerPoint The generating process of face-swapping video frames. The face area is first detected in each video frame. Then, facial landmarks are extracted to perform face alignment. After that, the deepfake algorithm (autoencoder or GAN) is applied to generate a synthetic face by inputting the face-aligned image. To reduce artefacts caused by blending, the landmarks of the left and right eyebrows and the bottom mouth are used to generate a specific mask. In this way, after blending the synthetic face to the original image, only the content inside the mask is retained. Finally, to further make the generated image realistic, a postprocessing operation is supplemented to process the generated image. Specifically, Gaussian blur is applied to the boundary of the mask while the colour correction algorithm is applied to ensure the consistency of the synthetic face and background image FIGURE 3Open in figure viewerPowerPoint Generating process of face reenactment videos [26]. First, monocular face reconstruction is performed on the source face and the target face to obtain their respective face parameters. After that, parameters are modified by preserving parameters of illumination and identify while changing parameters of pose, expression and eye gaze. Synthetic images are then generated using modified parameters. Finally, rendering-to-video translation network is applied to generate face reenactment videos FIGURE 4Open in figure viewerPowerPoint Autoencoders used for faceswapping. Figure top shows the training process of the two autoencoders. When generating deepfake faces, decoders are swapped as the figure below shows 2.2.2 Face reenactment The face reenactment task aims to perform the migration of facial expressions. In order to better demonstrate this kind of scheme, we directly use the scheme in [26] as an example to introduce. Figure 4 shows the general process of performing face reenactment. First, the low-dimensional parameter representation of the source and target videos is obtained using a monocular face reconstruction method. Furthermore, head pose and expression could be transferred to the parameter space. To perform face reenactment, scene illumination and identity parameters are preserved while head pose, expression and eye gaze parameters are changed. After that, synthetic images of the target actor are regenerated based on the modified parameters. These images are then served as the conditional input of our new renderingvideo conversion network, which is then trained to convert the synthesized input into a realistic output. To obtain a complete video with better time consistency, the conditioning space-time volumes are fed into the network in a sliding window fashion. In this way, face reenactment video can be obtained. 3 DEEPFAKE VIDEO DETECTION Deepfake videos are increasingly harmful to personal privacy and social security. Various methods have been proposed to detect manipulated videos. Early attempts mainly focused on inconsistent features caused by the face synthesis process while current detection methods mostly target at fundamental features. As shown in Table 1, these methods fall into five categories based on the features they use. To begin with, detection based on general neural networks is commonly used in literature, where deepfake detection task is considered as regular classification tasks. Temporal consistency features are also exploited to detect discontinuities between adjacent frames of fake video. To find distinguishable features, visual artefacts generated in blending process are exploited in detection tasks. Recently proposed approaches focus on more fundamental features, where camera fingerprint and biological signal-based schemes show great potential in detection tasks. In the following sections, we will review detection methods mentioned above. TABLE 1. Classification for existing detection methods Methods Description Generalnetwork-based methods In this method, detection is regarded as a frame-level classification task which is finished by CNNs Temporalconsistency-based methods Deepfake videos are found to exist inconsistencies between adjacent frames due to the defects of the forgery algorithm. Thus RNN is applied to detect such inconsistencies Visualartefacts-based methods The blending operation in generation process would cause intrinsic image discrepancies in the blending boundaries. CNN-based methods are used to identify these artefacts Camerafingerprints-based methods Due to specific generation process, devices leave different traces in the captured images. At the same time, faces and background images are acknowledged to come from different devices. Thus, detection task can be completed by using these traces Biologicalsignals-based methods GAN is hard to understand hidden biological signals of faces, making it difficult to synthesize human faces with reasonable behaviour. Based on this observation, biological signals are extracted to detect deepfake videos 3.1 General-network-based methods Recent advances in image classification have been applied to improve the detection of deepfake videos. In this method, face images extracted from the detected video are used to train the detection network. Then, the trained network is applied to make predictions for all frames of this video. The predictions are finally calculated by averaging or voting strategy. Consequently, the detection accuracy is highly dependent on the neural networks, without the need to exploit specific distinguishable features. In this section, we divide existing network-based methods into two types: transfer learning-based methods and detection approaches based on specially designed networks. 3.1.1 Transfer learning Network-based detection methods should be the earliest method introduced for detection tasks. Shortly after the appearance of the first deepfake video, some early detection algorithms were proposed, mainly based on existing networks that performed well in image classification tasks. Transfer learning strategy could be easily found in the early studies. Combining steganalysis features and deep learning features, Zhou et al. [28] put forward a two-stream network for face tampering detection. Likewise, in [7], Rossler et al. evaluated XceptionNet [29] on the FaceForensic++ dataset, outperforming all other networks in detecting fakes. During DFDC, similar detection methods were used. In [30], two existing models were tested to provide a performance baseline: A small DNN(composed of six convolutional layers and a fully connected layer) and an existing XceptionNet. Early results showed that the best method (XceptionNet) provides 93.0% precision. Bonettini et al. [31] studied the ensemble of different trained CNN models, showing that the ensemble of CNNs can achieve promising results in deepfake detections. However, such network-based algorithms are prone to overfitting [32], so researchers attempted to exploit intrinsic differences between real and fake videos through preprocessing. Some preprocessing methods, such as optical flow calculation [33], had been proved to be useful to exploit interframe dissimilarities in network-based methods. 3.1.2 Specially designed networks With the advent of large-scale datasets and the development of detection algorithms, more attention is attracted to improve the generalization of detection algorithms. Nguyen et al. [34] introduced a capsule network to improve the performance of detection networks. As illustrated in Figure 5, face images are first fed into the pretrained VGG-19 network [35]. Extracted features are then inputted into the proposed capsule network, which includes several primary capsules and two output capsules. Agreement between the features extracted by the primary capsules is dynamically calculated by a dynamic routing algorithm and the results are finally routed to the appropriate output capsule. Visualization of latent features extracted indicated that the combination of capsule networks and dynamic routing algorithm is effective for detecting manipulations. However, the capsule network performed terribly when encountering unknown deepfake videos [8], proving that capsule networks still need further improvement to detect high-fidelity videos. To explore the mesoscopic properties of images, Afchar et al. [36] also proposed a CNN, namely MesoInception-4, consisted of a variant of inception modules introduced in [37]. Their proposed approach achieved 98.4% accuracy using a private database. Moreover, this approach is also tested using unseen datasets in recent study [7, 8, 30, 1], proving to be a robust approach in deepfake detection tasks. Although these methods achieved excellent results on various datasets, the reasons behind good performance are still unknown. In fact, deeper networks tend to achieve better results than shallower network in various areas. The reason for the good performance may simply be that designed networks are deep enough. Compared with traditional learning-based methods, Wang et al. [39] pay more attention to neuron coverage and interactions rather than the design of specific network structures. The FakeSpotter they proposed uses hierarchical neuron behaviour as a feature, showing high robustness against four common perturbation attacks. This research provided a new insight for detecting fakes. FIGURE 5Open in figure viewerPowerPoint Capsule-forensics architecture. Pretrained VGG-19 is first used to extract features from face images.Features are further input into proposed capsules, which include several primary capsules and two output capsules. Agreement between primary capsules and output capsules is calculated by a dynamic routing algorithm. Finally, output of capsules is mapped to probabilistic values 3.1.3 Summary The disadvantage of network-based methods is that such methods tend to overfit on specific datasets. In this type of method, although adjustment and optimization of model structure often affect the abstraction degree of features, it still lacks sufficient relevance for the task of deepfake detection. Therefore, the current direction of such work is gradually changing. On the one hand, by adding additional components to the model, the model can be constrained to learn heuristic features [40]. In this case, importance of model architecture is greatly reduced while additional components play a greater role. This is exactly the difference between deepfake detection tasks and general computer vision tasks. On the other hand, more and more network-based methods have begun to introduce multitask learning, that is, not only to classify real and fake faces, but also to generate pixel-level tampering masks. In [41], using a semisupervised learning strategy, Nguyen et al. designed a multitask learning framework to simultaneously detect manipulated content and locate the manipulated regions. In such schemes, however, supervised multitask learning is only a complementary implementation, which does not necessarily improve the final detection performance. Further improvement was achieved by using attention mechanisms. Dang et al. [42] utilized an attention mechanism to process feature maps for the classification. The proposed approach showed excellent performance both in deepfake detection and forgery location, achieving state-of-the-art performance compared to previous solutions. Their approach demonstrates the importance of attention mechanisms. Likewise, in [43], Tarasiou et al. designed a lightweight architecture for extracting local image features and a multitask training scheme for forgery localization. In this way, the forgery location process provides evidence for judgement while ensuring detection accuracy, promoting the practical use of detection algorithm. It is worth mentioning that some basic directions in computer vision, such as anomaly detection, semantic segmentation and metric learning, are making more and more important contributions in this field.