浏览全部资源
扫码关注微信
Published： 16 February 2024 ，
移动端阅览
丁峰， 匡仁盛， 周越， 孙珑， 朱小刚， 朱国普. 2024. 深度伪造及其取证技术综述. 中国图象图形学报， 29(02):0295-0317
Ding Feng， Kuang Rensheng， Zhou Yue， Sun Long， Zhu Xiaogang， Zhu Guopu. 2024. A survey of Deepfake and related digital forensics. Journal of Image and Graphics， 29(02):0295-0317
深度学习作为机器学习的一个具有前景的重要分支，在计算机视觉方面取得了重大突破。深度伪造（Deepfake）通常指的是使用深度学习（deep learning）进行涉及人脸和人声的多媒体伪造技术，如果被恶意滥用会给社会带来灾难。深度伪造不仅限于面部的替换，还有修改面部特征、修改表情、唇形同步、姿势变换、完整脸生成、篡改音频到视频以及文本到视频等方式。人类面部在社会、政治、经济等方面的敏感性，使得深度伪造技术威胁着社会和个人的安全。对深度伪造产物进行检测也成为数字取证领域的一个重要研究课题。为了提供对Deepfake检测研究工作的最新概述，本文描述了各种针对解决Deepfake相关问题的处理方法。本文主要参考了谷歌学术检索2018—2022共5年的深度伪造论文，分为不同类别进行分析比较，并且详细介绍了深度伪造数据集的特点以及伪造方法，简述了深度伪造技术及其基本原理，介绍了检测器在深度伪造技术数据集上的性能效果，分别从输入维度、浅层特征和深层特针对深度伪造检测技术进行分类，并对未来发展前景进行展望。
Deep learning， a promising branch of machine learning， has made significant breakthroughs in computer vision. However， Deepfake， which refers to the set of techniques for forging human-related multimedia data using deep learning， can bring disasters to society if used maliciously. It is not only limited to facial replacement， but also other manipulations， such as fabricating facial features， manipulating expressions， synchronizing lips， modifying head gestures， entire face synthesis， and tampering related audios to videos and related texts to videos. Moreover， it can be used to generate faked pornographic videos or even faked speeches to subvert state power. Thus， deep forgery technology can greatly threaten society and individuals， thereyby detecting Deepfake has also become an important research topic in digital forensics. We conducted a systematic and critical survey to provide an overview of the latest research on Deepfake detection by exploring the recent developments in Deepfake and related forensic techniques. This survey mainly referred to papers on Deepfake in Google Scholar during 2018—2022. This survey divided the Deepfake detection techniques into two categories for analysis and comparison： input dimensions and forensic features. First， a comprehensive and systematic introduction of digital forensics is presented from the following aspects： 1） the development and security of deep forgery detection technology， 2） Deepfake technology architecture， and 3） the prevailing datasets and evaluation metrics. Then， this survey presents Deepfake techniques in several categories. Finally， future challenges and development prospects are discussed. In terms of image and video effects， Deepfake techniques are usually divided into four categories： face replacement， lip synchronization， head puppets， and attribute modification. The most commonly used Deepfake algorithms are based on self-encoders， generative adversarial networks， and diffusion models. An typical autoencoder consists of two convolutional neural networks acting as an encoder and a decoder. The encoder reduces the dimensions of the input targets’ facial image and encodes it into a vector corresponding to facial features. We share the parameters of the encoder； that is， we use the same encoder to learn only the common feature information for the encoder network. The structure of a generative adversarial network is based on a generator and a discriminator. The generator is similar to the decoder in an autoencoder， which converts the input noise into a picture and sends it to the discriminator for discrimination along with the real existing picture. The discriminator and the generator use back-propagation to optimize the parameters. Moreover， diffusion model is a parameterized Markov chain trained using variational inference to produce samples that match the data after a finite time. There are always two processes to train a diffusion model. One is the forward process， also called the diffusion process. The other process is reverse diffusion， also known as the reverse process， which slowly restores the original image from noise through continuous sampling. In the Deepfake detection task， the datasets have also evolved to fill past gaps. In general， this survey divides the Deepfake datasets into two generations. The first-generation datasets are often not large enough， and the quality of the content is not satisfying because of the low degree of research fervor. These source videos are usually from video sites or existing face datasets， which can lead to copyright and privacy concerns. The main first-generation datasets are UADFV， DF-TIMIT， FaceForensics， and diverse fake face dataset（DFFD）. The second generation of face forgery datasets has improved forgery effects and image clarity. The main second-generation datasets are Celeb-DF， Deepfake detection challenge dataset（DFDC） preview， DeeperForensic-1.0， DFDC， Korean Deepfake detection dataset（KoDF）， etc. In terms of input dimension， detecting Deepfake can be roughly divided into three categories： 1） the first category is inputting the image or key frame from the video， namely， inputting the image or key frame extracted from the video and judging the input data from the visual performance. This category is commonly used because it can be promoted easily to other computer vision classification models， and most Deepfake videos are conducted by frame-by-frame images. 2） The second is inputting continuous frames from video. In particular， multiple consecutive frames are inputted to allow the model to perceive the difference in the relationship between the frames from real and fake videos. 3） The third is inputting multiple frames and audio simultaneously from the video； that is， the video’s authenticity is detected by examining its video frames and audio together. The features focused on by Deepfake detection techniques also vary. This survey divides them into four categories： 1） the frequency domain-based approach looks for anomalies in the video at the signal level， treating the video as a sequence of frames and a synchronized audio signal. Such anomalies， including image mismatches and mismatches in audio-video synchronization， are usually generated from the mismatches at the signal level during Deepfake video generation. 2） The texture and spatio-temporal approaches tend to focus only on face position and feature matching in the forged video generation process， where breakdowns that violate the laws of physics and human physiology may occur. 3） The reconstruction–classification learning methods emphasize the common compact representations of genuine faces and enhance the learned representations to be aware of unknown forgery patterns. Classification learning involves mining the essential discrepancy between real and fake images， facilitating the understanding of forgeries. 4） Data-driven methods are detection methods that do not target specific features. However， they use supervised learning to feed real and fake videos into the model for training. The road to the research on deep forgery techniques and deep forgery detection is still long. We must overcome the existing shortcomings and face the challenges of future technological advances.
深度造假机器学习人工智能深度学习数字取证数字反取证
Deepfakemachine learningartificial intelligencedeep learningdigital forensicsdigital anti-forensics
Akhtar Z and Dasgupta D. 2019. A comparative evaluation of local feature descriptors for DeepFakes detection//2019 IEEE International Symposium on Technologies for Homeland Security （HST）. Woburn， USA： IEEE： 1-5 ［DOI： 10.1109/HST47167.2019.9033005http://dx.doi.org/10.1109/HST47167.2019.9033005］
Allcott H and Gentzkow M. 2017. Social media and fake news in the 2016 election. Journal of Economic Perspectives， 31（2）： 211-236 ［DOI： 10.1257/jep.31.2.211http://dx.doi.org/10.1257/jep.31.2.211］
Amerini I， Galteri L， Caldelli R and Del Bimbo A. 2019. Deepfake video detection through optical flow based CNN//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshops. Seoul， Korea （South）： IEEE： 1205-1207 ［DOI： 10.1109/ICCVW.2019.00152http://dx.doi.org/10.1109/ICCVW.2019.00152］
Arjovsky M， Chintala S and Bottou L. 2017. Wasserstein generative adversarial networks//Proceedings of the 34th International Conference on Machine Learning. Sydney， Australia： PMLR： 214-223
Bradley A P. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition， 30（7）： 1145-1159 ［DOI： 10.1016/S0031-3203（96）00142-2http://dx.doi.org/10.1016/S0031-3203（96）00142-2］
Brock A， Donahue J and Simonyan K. 2019. Large scale GAN training for high fidelity natural image synthesis//Proceedings of the 7th International Conference on Learning Representations. New Orleans， USA： OpenReview.net
Cai Z X， Stefanov K， Dhall A and Hayat M. 2022. Do you really mean that？ Content driven audio-visual deepfake dataset and multimodal method for temporal forgery localization//Proceedings of 2022 International Conference on Digital Image Computing： Techniques and Applications. Sydney， Australia： IEEE： #10034605 ［DOI： 10.1109/DICTA56598.2022.10034605http://dx.doi.org/10.1109/DICTA56598.2022.10034605］
Cao J Y， Ma C， Yao T P， Chen S， Ding S H and Yang X K. 2022. End-to-end reconstruction-classification learning for face forgery detection//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 4103-4112 ［DOI： 10.1109/CVPR52688.2022.00408http://dx.doi.org/10.1109/CVPR52688.2022.00408］
Cao S H， Liu X H， Mao X Q and Zou Q. 2022. A review of human face forgery and forgery-detection technologies. Journal of Image and Graphics， 27（4）： 1023-1038
曹申豪， 刘晓辉， 毛秀青， 邹勤. 2022. 人脸伪造及检测技术综述. 中国图象图形学报， 27（4）： 1023-1038 ［DOI： 10.11834/jig.200466http://dx.doi.org/10.11834/jig.200466］
Chan C， Ginosar S， Zhou T H and Efros A. 2019. Everybody dance now//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 5932-5941 ［DOI： 10.1109/ICCV.2019.00603http://dx.doi.org/10.1109/ICCV.2019.00603］
Chen B J， Li T M and Ding W P. 2022a. Detecting deepfake videos based on spatiotemporal attention and convolutional LSTM. Information Sciences， 601： 58-70 ［DOI： 10.1016/j.ins.2022.04.014http://dx.doi.org/10.1016/j.ins.2022.04.014］
Chen L， Zhang Y， Song Y B， Liu L Q and Wang J. 2022b. Self-supervised learning of adversarial example： towards good generalizations for deepfake detection//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 18689-18698 ［DOI： 10.1109/CVPR52688.2022.01815http://dx.doi.org/10.1109/CVPR52688.2022.01815］
Chen S D， Tan S Q， Li B and Huang J W. 2016. Automatic detection of object-based forgery in advanced video. IEEE Transactions on Circuits and Systems for Video Technology， 26（11）： 2138-2151 ［DOI： 10.1109/TCSVT.2015.2473436http://dx.doi.org/10.1109/TCSVT.2015.2473436］
Cheng H， Guo Y Y， Wang T Y， Li Q， Chang X J and Nie L Q. 2022. Voice-Face homogeneity tells deepfake ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2203.02195.pdfhttps://arxiv.org/pdf/2203.02195.pdf
Cheng H， Guo Y Y， Wang T Y， Nie L Q and Kankanhalli M. 2023. Towards generalizable deepfake detection by primary region regularization ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2307.12534.pdfhttps://arxiv.org/pdf/2307.12534.pdf
Cho K， van Merriënboer B， Gulcehre C， Bahdanau D， Bougares F， Schwenk H and Bengio Y. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing. Doha， Qatar： Association for Computational Linguistics： 1724-1734 ［DOI： 10.3115/v1/D14-1179http://dx.doi.org/10.3115/v1/D14-1179］
Choi Y， Choi M， Kim M， Ha J W， Kim S and Choo J. 2018. StarGAN： unified generative adversarial networks for multi-domain image-to-image translation//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 8789-8797 ［DOI： 10.1109/CVPR.2018.00916http://dx.doi.org/10.1109/CVPR.2018.00916］
Chollet F. 2017. Xception： deep learning with depthwise separable convolutions//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu， USA： IEEE： 1800-1807 ［DOI： 10.1109/CVPR.2017.195http://dx.doi.org/10.1109/CVPR.2017.195］
Chung J S and Zisserman A. 2017. Out of time： automated lip sync in the wild//ACCV 2016 International Workshops on Computer Vision. Taipei， China： Springer： 251-263 ［DOI： 10.1007/978-3-319-54427-4_19http://dx.doi.org/10.1007/978-3-319-54427-4_19］
Coccomini D A， Messina N， Gennaro C and Falchi F. 2022a. Combining EfficientNet and vision Transformers for video deepfake detection//Proceedings of the 21st International Conference on Image Analysis and Processing. Lecce， Italy： Springer： 219-229 ［DOI： 10.1007/978-3-031-06433-3_19http://dx.doi.org/10.1007/978-3-031-06433-3_19］
Coccomini D A， Zilos G K， Amato G， Caldelli R， Falchi F， Papadopoulos S and Gennaro C. 2022b. MINTIME： multi-identity size-invariant video deepfake detection ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2211.10996.pdfhttps://arxiv.org/pdf/2211.10996.pdf
Cozzolino D， Pianese A， Nießner M and Verdoliva L. 2022. Audio-visual person-of-interest DeepFake detection ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2204.03083.pdfhttps://arxiv.org/pdf/2204.03083.pdf
Cozzolino D， Pianese A， Nießner M and Verdoliva L. 2023. Audio-visual person-of-interest DeepFake detection//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 943-952 ［DOI： 10.1109/CVPRW59228.2023.00101http://dx.doi.org/10.1109/CVPRW59228.2023.00101］
Cozzolino D， Rössler A， Thies J， Niesner M and Verdoliva L. 2021. ID-reveal： identity-aware DeepFake video detection//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 15088-15097 ［DOI： 10.1109/ICCV48922.2021.01483http://dx.doi.org/10.1109/ICCV48922.2021.01483］
Dang H， Liu F， Stehouwer J， Liu X M and Jain A K. 2020. On the detection of digital face manipulation//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5780-5789 ［DOI： 10.1109/CVPR42600.2020.00582http://dx.doi.org/10.1109/CVPR42600.2020.00582］
Dash B and Sharma P. 2023. Are ChatGPT and deepfake algorithms endangering the cybersecurity industry？ A review. International Journal of Engineering and Applied Sciences， 10（1） ［DOI： 10.31873/IJEAS.10.1.01］
Devlin J， Chang M W， Lee K and Toutanova K. 2019. BERT： pre-training of deep bidirectional Transformers for language understanding//Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics： Human Language Technologies， Volume 1. Minneapolis， USA： Association for Computational Linguistics： 4171-4186 ［DOI： 10.18653/v1/N19-1423http://dx.doi.org/10.18653/v1/N19-1423］
Dhariwal P and Nichol A. 2021. Diffusion models beat GANs on image synthesis//Proceedings of the 35th Conference on Neural Information Processing Systems. ［s.l.］： ［s.n.］： 8780-8794
Ding F， Fan B， Shen Z Y， Yu K P， Srivastava G， Dev K and Wan S H. 2023a. Securing facial bioinformation by eliminating adversarial perturbations. IEEE Transactions on Industrial Informatics， 19（5）： 6682-6691 ［DOI： 10.1109/TII.2022.3201572http://dx.doi.org/10.1109/TII.2022.3201572］
Ding F， Shen Z Y， Zhu G P， Kwong S， Zhou Y C and Lyu S W. 2023b. ExS-GAN： synthesizing anti-forensics images via extra supervised GAN. IEEE Transactions on Cybernetics， 53（11）： 7162-7173 ［DOI： 10.1109/TCYB.2022.3210294http://dx.doi.org/10.1109/TCYB.2022.3210294］
Ding F， Shi Y X， Zhu G P and Shi Y Q. 2019. Smoothing identification for digital image forensics. Multimedia Tools and Applications， 78（7）： 8225-8245 ［DOI： 10.1007/s11042-018-6807-6http://dx.doi.org/10.1007/s11042-018-6807-6］
Ding F， Shi Y X， Zhu G P and Shi Y Q. 2020. Real-time estimation for the parameters of Gaussian filtering via deep learning. Journal of Real-Time Image Processing， 17（1）： 17-27 ［DOI： 10.1007/s11554-019-00907-5http://dx.doi.org/10.1007/s11554-019-00907-5］
Ding F， Zhu G P， Li Y C， Zhang X P， Atrey P K and Lyu S W. 2022a. Anti-forensics for face swapping videos via adversarial training. IEEE Transactions on Multimedia， 24： 3429-3441 ［DOI： 10.1109/TMM.2021.3098422http://dx.doi.org/10.1109/TMM.2021.3098422］
Ding M， Yang Z Y， Hong W Y， Zheng W D， Zhou C， Yin D， Lin J Y， Zou X， Shao Z， Yang H X and Tang J. 2021. CogView： mastering text-to-image generation via Transformers//Proceedings of the 35th Conference on Neural Information Processing Systems. ［s.l.］： ［s.n.］： 19822-19835
Ding M， Zheng W D， Hong W Y and Tang J. 2022b. CogView2： faster and better text-to-image generation via hierarchical Transformers//Proceedings of the 36th Conference on Neural Information Processing Systems. ［s.l.］： ［s.n.］： 16890-16902
Dolhansky B， Bitton J， Pflaum B， Lu J K， Howes R， Wang M L and Ferrer C C. 2020. The DeepFake detection challenge （DFDC） dataset ［EB/OL］. ［2023-03-01］. https://arxiv.org/pdf/2006.07397.pdfhttps://arxiv.org/pdf/2006.07397.pdf
Dolhansky B， Howes R， Pflaum B， Baram N and Ferrer C C. 2019. The deepfake detection challenge （DFDC） preview dataset ［EB/OL］. ［2023-03-01］. https://arxiv.org/pdf/1910.08854.pdfhttps://arxiv.org/pdf/1910.08854.pdf
Dong S C， Wang J， Ji R H， Liang J J， Fan H Q and Ge Z. 2023. Implicit identity leakage： the stumbling block to improving deepfake detection generalization//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 3994-4004 ［DOI： 10.1109/CVPR52729.2023.00389http://dx.doi.org/10.1109/CVPR52729.2023.00389］
Dong S C， Wang J， Liang J J， Fan H Q and Ji R H. 2022a. Explaining deepfake detection by analysing image matching//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 18-35 ［DOI： 10.1007/978-3-031-19781-9_2http://dx.doi.org/10.1007/978-3-031-19781-9_2］
Dong X Y， Bao J M， Chen D D， Zhang T， Zhang W M， Yu N H， Chen D， Wen F and Guo B N. 2022b. Protecting celebrities from deepfake with identity consistency Transformer//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 9458-9468 ［DOI： 10.1109/CVPR52688.2022.00925http://dx.doi.org/10.1109/CVPR52688.2022.00925］
Dosovitskiy A， Beyer L， Kolesnikov A， Weissenborn D， Zhai X H， Unterthiner T， Dehghani M， Minderer M， Heigold G， Gelly S， Uszkoreit J and Houlsby N. 2021. An image is worth 16 × 16 words： Transformers for image recognition at scale//Proceedings of the 9th International Conference on Learning Representations. ［s.l.］： OpenReview.net
Elpeltagy M， Ismail A， Zaki M S and Eldahshan K. 2023. A novel smart deepfake video detection system. International Journal of Advanced Computer Science and Applications， 14（1）： 407-419 ［DOI： 10.14569/IJACSA.2023.0140144http://dx.doi.org/10.14569/IJACSA.2023.0140144］
Frank J， Eisenhofer T， Schönherr L， Fischer A， Kolossa D and Holz T. 2020. Leveraging frequency analysis for deep fake image recognition//Proceedings of the 37th International Conference on Machine Learning. ［s.l.］： PMLR： 3247-3258
Gandhi A and Jain S. 2020. Adversarial perturbations fool deepfake detectors//Proceedings of 2020 International Joint Conference on Neural Networks （IJCNN）. Glasgow， UK： IEEE： #9207034 ［DOI： 10.1109/IJCNN48605.2020.9207034http://dx.doi.org/10.1109/IJCNN48605.2020.9207034］
Goodfellow I， Pouget-Abadie J， Mirza M， Xu B， Warde-Farley D， Ozair S， Courville A and Bengio Y. 2020. Generative adversarial networks. Communications of the ACM， 63（11）： 139-144 ［DOI： 10.1145/3422622http://dx.doi.org/10.1145/3422622］
Gravel J， D'Amours-Gravel M and Osmanlliu E. Learning to fake it： limited responses and fabricated references provided by ChatGPT for medical questions. Mayo Clinic Proceedings： Digital Health， 2023， 1（3）： 226-234. ［ DOI：10.1016/j.mcpdig.2023.05.004http://dx.doi.org/10.1016/j.mcpdig.2023.05.004］
Groh M， Epstein Z， Firestone C and Picard R. 2022. Deepfake detection by human crowds， machines， and machine-informed crowds. Proceedings of the National Academy of Sciences of the United States of America， 119（1）： #2110013119 ［DOI： 10.1073/pnas.2110013119http://dx.doi.org/10.1073/pnas.2110013119］
Gu Q Q， Chen S， Yao T P， Chen Y， Ding S H and Yi R. 2022a. Exploiting fine-grained face forgery clues via progressive enhancement learning//Proceedings of the 36th AAAI Conference on Artificial Intelligence. ［s.l.］： AAAI： 735-743 ［DOI： 10.1609/aaai.v36i1.19954http://dx.doi.org/10.1609/aaai.v36i1.19954］
Gu Y W， Zhao X F， Gong C and Yi X W. 2021. Deepfake video detection using audio-visual consistency//Proceedings of the 19th International Workshop on Digital Forensics and Watermarking. Melbourne， Australia： Springer： 168-180 ［DOI： 10.1007/978-3-030-69449-4_13http://dx.doi.org/10.1007/978-3-030-69449-4_13］
Gu Z H， Chen Y， Yao T P， Ding S H， Li J L and Ma L Z. 2022b. Delving into the local： dynamic inconsistency learning for DeepFake video detection//Proceedings of the 36th AAAI Conference on Artificial Intelligence. ［s.l.］： AAAI： 744-752 ［DOI： 10.1609/aaai.v36i1.19955http://dx.doi.org/10.1609/aaai.v36i1.19955］
Guarnera L， Giudice O and Battiato S. 2020a. DeepFake detection by analyzing convolutional traces//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition workshops. Seattle， USA： IEEE： 2841-2850 ［DOI： 10.1109/CVPRW50498.2020.00341http://dx.doi.org/10.1109/CVPRW50498.2020.00341］
Guarnera L， Giudice O and Battiato S. 2020b. Fighting deepfake by exposing the convolutional traces on images. IEEE Access， 8： 165085-165098 ［DOI： 10.1109/ACCESS.2020.3023037http://dx.doi.org/10.1109/ACCESS.2020.3023037］
Guarnera L， Giudice O and Battiato S. 2023. Level up the deepfake detection： a method to effectively discriminate images generated by GAN architectures and diffusion models ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2303.00608.pdfhttps://arxiv.org/pdf/2303.00608.pdf
Gulrajani I， Ahmed F， Arjovsky M， Dumoulin V and Courville A. 2017. Improved training of wasserstein GANs//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach， USA： Curran Associates Inc.： 5769-5779
Haliassos A， Mira R， Petridis S and Pantic M. 2022. Leveraging real talking faces via self-supervision for robust forgery detection//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 14930-14942 ［DOI： 10.1109/CVPR52688.2022.01453http://dx.doi.org/10.1109/CVPR52688.2022.01453］
Haliassos A， Vougioukas K， Petridis S and Pantic M. 2021. Lips don’t lie： a generalisable and robust approach to face forgery detection//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 5037-5047 ［DOI： 10.1109/CVPR46437.2021.00500http://dx.doi.org/10.1109/CVPR46437.2021.00500］
Hasan H R and Salah K. 2019. Combating deepfake videos using blockchain and smart contracts. IEEE Access， 7： 41596-41606 ［DOI： 10.1109/ACCESS.2019.2905689http://dx.doi.org/10.1109/ACCESS.2019.2905689］
He K M， Zhang X Y， Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 770-778 ［DOI： 10.1109/CVPR.2016.90http://dx.doi.org/10.1109/CVPR.2016.90］
He Y， Yu N， Keuper M and Fritz M. 2021. Beyond the spectrum： detecting deepfakes via re-synthesis//Proceedings of the 30th International Joint Conference on Artificial Intelligence. Montreal， Canada： ijcai.org： 2534-2541 ［DOI： 10.24963/ijcai.2021/349http://dx.doi.org/10.24963/ijcai.2021/349］
Ho J， Jain A and Abbeel P. 2020. Denoising diffusion probabilistic models//Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver， Canada： Curran Associates Inc.： #574
Hsu G S， Tsai C H and Wu H Y. 2022. Dual-generator face reenactment//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 632-640 ［DOI： 10.1109/CVPR52688.2022.00072http://dx.doi.org/10.1109/CVPR52688.2022.00072］
Hu J， Liao X， Gao D F， Tsutsui S， Qin Z and Shou M Z. 2023. DeepfakeMAE： facial part consistency aware masked autoencoder for deepfake video detection ［EB/OL］. ［2023-05-06］. https://arxiv.org/pdf/2303.01740.pdfhttps://arxiv.org/pdf/2303.01740.pdf
Hu J， Liao X， Liang J W， Zhou W B and Qin Z. 2022. FInfer： frame inference-based deepfake detection for high-visual-quality videos//Proceedings of the 36th AAAI Conference on Artificial Intelligence. ［s.l.］： AAAI： 951-959 ［DOI： 10.1609/aaai.v36i1.19978http://dx.doi.org/10.1609/aaai.v36i1.19978］
Huang B J， Wang Z Y， Yang J F， Ai J X， Zou Q， Wang Q and Ye D P. 2023. Implicit identity driven deepfake face swapping detection//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 4490-4499 ［DOI： 10.1109/CVPR52729.2023.00436http://dx.doi.org/10.1109/CVPR52729.2023.00436］
Hussain Z F and Ibraheem H R. 2023. Novel convolutional neural networks based jaya algorithm approach for accurate deepfake video detection. Mesopotamian Journal of Cyber Security， 2023： 35-39 ［DOI： 10.58496/MJCS/2023/007http://dx.doi.org/10.58496/MJCS/2023/007］
Jia S， Li X and Lyu S W. 2022. Model attribution of face-swap deepfake videos//Proceedings of 2022 IEEE International Conference on Image Processing （ICIP）. Bordeaux， France： IEEE： 2356-2360 ［DOI： 10.1109/ICIP46576.2022.9897972http://dx.doi.org/10.1109/ICIP46576.2022.9897972］
Jia Y， Zhang Y， Weiss R J， Wang Q， Shen J， Ren F， Chen Z F， Nguyen P， Pang R M， Moreno I L and Wu Y H. 2018. Transfer learning from speaker verification to multispeaker text-to-speech synthesis//Proceedings of the 32nd International Conference on Neural Information Processing Systems. Montreal， Canada： Curran Associates Inc.： 4485-4495
Jiang L M， Li R， Wu W， Qian C and Loy C C. 2020. DeeperForensics-1.0： a large-scale dataset for real-world face forgery detection//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 2886-2895 ［DOI： 10.1109/CVPR42600.2020.00296http://dx.doi.org/10.1109/CVPR42600.2020.00296］
Karras T， Aila T， Laine S and Lehtinen J. 2018. Progressive growing of GANs for improved quality， stability， and variation//Proceedings of the 6th International Conference on Learning Representations. Vancouver， Canada： OpenReview.net
Karras T， Laine S and Aila T. 2019. A style-based generator architecture for generative adversarial networks//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach， USA： IEEE： 4396-4405 ［DOI： 10.1109/CVPR.2019.00453http://dx.doi.org/10.1109/CVPR.2019.00453］
Khalid H， Tariq S， Kim M and Woo S S. 2021. FakeAVCeleb： a novel audio-video multimodal deepfake dataset//Proceedings of the 35th Conference on Neural Information Processing Systems. ［s.l.］： ［s.n.］
Khan S A and Dang-Nguyen D T. 2022. Hybrid Transformer network for deepfake detection//Proceedings of the 19th International Conference on Content-based Multimedia Indexing. Graz， Austria： ACM： 8-14 ［DOI： 10.1145/3549555.3549588http://dx.doi.org/10.1145/3549555.3549588］
Khormali A and Yuan J S. 2022. DFDT： an end-to-end DeepFake detection framework using vision Transformer. Applied Sciences， 12（6）： #2953 ［DOI： 10.3390/app12062953http://dx.doi.org/10.3390/app12062953］
Kingma D P and Welling M. 2014. Auto-encoding variational Bayes//Proceedings of the 2nd International Conference on Learning Representations. Banff， Canada： ［s.n.］
Korshunov P and Marcel S. 2018. DeepFakes： a new threat to face recognition？ Assessment and detection ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/1812.08685.pdfhttps://arxiv.org/pdf/1812.08685.pdf
Korshunova I， Shi W Z， Dambre J and Theis L. 2017. Fast face-swap using convolutional neural networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 3697-3705 ［DOI： 10.1109/ICCV.2017.397http://dx.doi.org/10.1109/ICCV.2017.397］
Kwon P， You J， Nam G， Park S and Chae G. 2021. KoDF： a large-scale Korean DeepFake detection dataset//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 10724-10733 ［DOI： 10.1109/ICCV48922.2021.01057http://dx.doi.org/10.1109/ICCV48922.2021.01057］
Lee S， An J and Woo S S. 2022. BZNet： unsupervised multi-scale branch zooming network for detecting low-quality deepfake videos//Proceedings of 2022 ACM Web Conference 2022. Lyon， France： ACM： 3500-3510 ［DOI： 10.1145/3485447.3512245http://dx.doi.org/10.1145/3485447.3512245］
Li B， Ng T T， Li X L， Tan S Q and Huang J W. 2015. Statistical model of JPEG noises and its application in quantization step estimation. IEEE Transactions on Image Processing， 24（5）： 1471-1484 ［DOI： 10.1109/TIP.2015.2405477http://dx.doi.org/10.1109/TIP.2015.2405477］
Li C J， Wang L， Ji S L， Zhang X H， Xi Z H， Guo S Q and Wang T. 2022. Seeing is living？ Rethinking the security of facial liveness verification in the deepfake era//The 31st USENIX Security Symposium. Boston， USA： 2673-2690
Li G， Cao Y and Zhao X F. 2021. Exploiting facial symmetry to expose deepfakes//Proceedings of 2021 IEEE International Conference on Image Processing （ICIP）. Anchorage， USA： IEEE： 3587-3591 ［DOI： 10.1109/ICIP42928.2021.9506272http://dx.doi.org/10.1109/ICIP42928.2021.9506272］
Li L Z， Bao J M， Zhang T， Yang H， Chen D， Wen F and Guo B N. 2020a. Face X-ray for more general face forgery detection//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5000-5009 ［DOI： 10.1109/CVPR42600.2020.00505http://dx.doi.org/10.1109/CVPR42600.2020.00505］
Li X L， Yang B and Zeng T Y. 2011. Efficient reversible watermarking based on adaptive prediction-error expansion and pixel selection. IEEE Transactions on Image Processing， 20（12）： 3524-3533 ［DOI： 10.1109/TIP.2011.2150233http://dx.doi.org/10.1109/TIP.2011.2150233］
Li X L， Yu N H， Zhang X P， Zhang W M， Li B， Lu W， Wang W and Liu X L. 2021. Overview of digital media forensics technology. Journal of Image and Graphics， 26（6）： 1216-1226
李晓龙， 俞能海， 张新鹏， 张卫明， 李斌， 卢伟， 王伟， 刘晓龙. 2021. 数字媒体取证技术综述. 中国图象图形学报， 26（6）： 1216-1226 ［DOI： 10.11834/jig.210081http://dx.doi.org/10.11834/jig.210081］
Li X R， Ji S L， Wu C M， Liu Z G， Deng S G， Cheng P， Yang M and Kong X W. 2021. Survey on deepfakes and detection techniques. Journal of Software， 32（2）： 496-518
李旭嵘， 纪守领， 吴春明， 刘振广， 邓水光， 程鹏， 杨珉， 孔祥维. 2021. 深度伪造与检测技术综述. 软件学报， 32（2）： 496-518 ［DOI： 10.13328/j.cnki.jos.006140http://dx.doi.org/10.13328/j.cnki.jos.006140］
Li Y， Bian S， Wang C T and Lu W. 2023. CNN and Transformer-coordinated deepfake detection. Journal of Image and Graphics， 28（3）： 804-819
李颖， 边山， 王春桃， 卢伟. 2023. CNN结合Transformer的深度伪造高效检测. 中国图象图形学报， 28（3）： 804-819 ［DOI： 10.11834/jig.220519http://dx.doi.org/10.11834/jig.220519］
Li Y， Bian S， Wang C T， Polat K， Alhudhaif A and Alenezi F. 2023. Exposing low-quality deepfake videos of social network service using spatial restored detection framework. Expert Systems with Applications， 231： #120646 ［DOI： 10.1016/j.eswa.2023.120646http://dx.doi.org/10.1016/j.eswa.2023.120646］
Li Y Z， Yang X， Sun P， Qi H G and Lyu S W. 2020b. Celeb-DF： a large-scale challenging dataset for DeepFake forensics//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 3204-3213 ［DOI： 10.1109/CVPR42600.2020.00327http://dx.doi.org/10.1109/CVPR42600.2020.00327］
Li Z Y， Zhang X H， Pu Y W， Wu Y M and Ji S L. 2023. A survey on multimodal deepfake and detection techniques. Journal of Computer Research and Development， 60（6）： 1396-1416
李泽宇， 张旭鸿， 蒲誉文， 伍一鸣， 纪守领. 2023. 多模态深度伪造及检测技术综述. 计算机研究与发展， 60（6）： 1396-1416 ［DOI： 10.7544/issn1000-1239.202111119http://dx.doi.org/10.7544/issn1000-1239.202111119］
Liang B R， Pan Y， Guo Z Z， Zhou H， Hong Z B， Han X G， Han J Y， Liu J T， Ding E R and Wang J D. 2022. Expressive talking head generation with granular audio-visual control//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 3377-3386 ［DOI： 10.1109/CVPR52688.2022.00338http://dx.doi.org/10.1109/CVPR52688.2022.00338］
Liang R G， Lyu P Z， Zhao Y， Chen P， Xing H， Zhang Y J， Han J Z， He R， Zhao X F， Li M and Chen K. 2020. A survey of audiovisual deepfake detection techniques. Journal of Cyber Security， 5（2）： 1-17
梁瑞刚， 吕培卓， 赵月， 陈鹏， 邢豪， 张颖君， 韩冀中， 赫然， 赵险峰， 李明， 陈恺. 2020. 视听觉深度伪造检测技术研究综述. 信息安全学报， 5（2）： 1-17 ［DOI： 10.19363/J.cnki.cn10-1380/tn.2020.02.01http://dx.doi.org/10.19363/J.cnki.cn10-1380/tn.2020.02.01］
Lin C H， Shen C， Deng J Y， Hu P B， Wang Q， Ma S Q， Li Q and Guan X H. 2023. Digitally forged face content creation and detection. Chinese Journal of Computers， 46（3）： 469-498
蔺琛皓， 沈超， 邓静怡， 胡鹏斌， 王骞， 马仕清， 李琦， 管晓宏. 2023. 虚假数字人脸内容生成与检测技术. 计算机学报， 46（3）： 469-498 ［DOI： 10.11897/SP.J.1016.2023.00469http://dx.doi.org/10.11897/SP.J.1016.2023.00469］
Liu J R， Zhu K M， Lu W， Luo X Y and Zhao X F. 2021a. A lightweight 3D convolutional neural network for deepfake detection. International Journal of Intelligent Systems， 36（9）： 4990-5004 ［DOI： 10.1002/int.22499http://dx.doi.org/10.1002/int.22499］
Liu Y Q， Zhu X B， Zhao X F and Cao Y. 2019. Adversarial learning for constrained image splicing detection and localization based on atrous convolution. IEEE Transactions on Information Forensics and Security， 14（10）： 2551-2566 ［DOI： 10.1109/TIFS.2019.2902826http://dx.doi.org/10.1109/TIFS.2019.2902826］
Liu Z， Lin Y T， Cao Y， Hu H， Wei Y X， Zhang Z， Lin S and Guo B N. 2021b. Swin Transformer： hierarchical vision Transformer using shifted windows//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 9992-10002 ［DOI： 10.1109/ICCV48922.2021.00986http://dx.doi.org/10.1109/ICCV48922.2021.00986］
Liu Z Z， Qi X J and Torr P H S. 2020. Global texture enhancement for fake face detection in the wild//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 8057-8066 ［DOI： 10.1109/CVPR42600.2020.00808http://dx.doi.org/10.1109/CVPR42600.2020.00808］
Lu W， Sun W， Huang J W and Lu H T. 2008. Digital image forensics using statistical features and neural network classifier//Proceedings of 2008 International Conference on Machine Learning and Cybernetics. Kunming， China： IEEE： 2831-2834 ［DOI： 10.1109/ICMLC.2008.4620890http://dx.doi.org/10.1109/ICMLC.2008.4620890］
Lu W， Sun W and Lu H T. 2009. Robust watermarking based on DWT and nonnegative matrix factorization. Computers and Electrical Engineering， 35（1）： 183-188 ［DOI： 10.1016/j.compeleceng.2008.09.004http://dx.doi.org/10.1016/j.compeleceng.2008.09.004］
Luo X Y， Wang D S， Wang P and Liu F L. 2007. Blind image steganalysis based on multi-domain feature scaling and BP network. Journal of Southeast University （Natural Science Edition）， 37（S1）： 87-91
罗向阳， 王道顺， 汪萍， 刘粉林. 2007. 基于图像多域特征缩放与BP网络的信息隐藏盲检测. 东南大学学报（自然科学版）， 37（S1）： 87-91 ［DOI： 10.3321/j.issn：1001-0505.2007.z1.019http://dx.doi.org/10.3321/j.issn：1001-0505.2007.z1.019］
Miyato T， Kataoka T， Koyama M and Yoshida Y. 2018. Spectral normalization for generative adversarial networks//Proceedings of the 6th International Conference on Learning Representations. Vancouver， Canada： OpenReview.net
Mo H X， Chen B L and Luo W Q. 2018. Fake faces identification via convolutional neural network//Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security. Innsbruck， Austria： ACM： 43-47 ［DOI： 10.1145/3206004.3206009http://dx.doi.org/10.1145/3206004.3206009］
Murphy G and Flynn E. 2022. Deepfake false memories. Memory， 30（4）： 480-492 ［DOI： 10.1080/09658211.2021.1919715http://dx.doi.org/10.1080/09658211.2021.1919715］
Myvizhi D and Pamila J C M J. 2022. Extensive analysis of deep learning-based deepfake video detection. Journal of Ubiquitous Computing and Communication Technologies， 4（1）： 1-8 ［DOI： 10.36548/jucct.2022.1.001http://dx.doi.org/10.36548/jucct.2022.1.001］
Nadimpalli A V and Rattani A. 2022. On improving cross-dataset generalization of deepfake detectors//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. New Orleans， USA： IEEE： 91-99 ［DOI： 10.1109/CVPRW56347.2022.00019http://dx.doi.org/10.1109/CVPRW56347.2022.00019］
Neves J C， Tolosana R， Vera-Rodriguez R， Lopes V， Proenca H and Fierrez J. 2020. GANprintR： improved fakes and evaluation of the state of the art in face manipulation detection. IEEE Journal of Selected Topics in Signal Processing， 14（5）： 1038-1048 ［DOI： 10.1109/JSTSP.2020.3007250http://dx.doi.org/10.1109/JSTSP.2020.3007250］
Nguyen H M and Derakhshani R. 2020. Eyebrow recognition for identifying deepfake videos//Proceedings of 2020 International Conference of the Biometrics Special Interest Group （BIOSIG）. Darmstadt， Germany： IEEE： 1-5 ［DOI： 20.500.12116/34328http://dx.doi.org/20.500.12116/34328］
Nirkin Y， Keller Y and Hassner T. 2019. FSGAN： subject agnostic face swapping and reenactment//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 7183-7192 ［DOI： 10.1109/ICCV.2019.00728http://dx.doi.org/10.1109/ICCV.2019.00728］
Peng F， Yin L P and Long M. 2022. BDC-GAN： bidirectional conversion between computer-generated and natural facial images for anti-forensics. IEEE Transactions on Circuits and Systems for Video Technology， 32（10）： 6657-6670 ［DOI： 10.1109/TCSVT.2022.3177238http://dx.doi.org/10.1109/TCSVT.2022.3177238］
Perov I， Gao D H， Chervoniy N， Liu K L， Marangonda S， Umé C， Dpfks M， Facenheim C S， Luis R P， Jiang J， Zhang S， Wu P Y， Zhou B and Zhang W M. 2020. DeepFaceLab： integrated， flexible and extensible face-swapping framework ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2005.05535.pdfhttps://arxiv.org/pdf/2005.05535.pdf
Qian Z X and Zhang X P. 2014. Improved anti-forensics of JPEG compression. Journal of Systems and Software， 91： 100-108 ［DOI： 10.1016/j.jss.2013.12.043http://dx.doi.org/10.1016/j.jss.2013.12.043］
Qian Z X and Zhang X P. 2016. Reversible data hiding in encrypted images with distributed source encoding. IEEE Transactions on Circuits and Systems for Video Technology， 26（4）： 636-646 ［DOI： 10.1109/TCSVT.2015.2418611http://dx.doi.org/10.1109/TCSVT.2015.2418611］
Qin C， Chang C C and Chen P Y. 2012. Self-embedding fragile watermarking with restoration capability based on adaptive bit allocation mechanism. Signal Processing， 92（4）： 1137-1150 ［DOI： 10.1016/j.sigpro.2011.11.013http://dx.doi.org/10.1016/j.sigpro.2011.11.013］
Qin C， Chang C C， Huang Y H and Liao L T. 2013. An inpainting-assisted reversible steganographic scheme using a histogram shifting mechanism. IEEE Transactions on Circuits and Systems for Video Technology， 23（7）： 1109-1118 ［DOI： 10.1109/TCSVT.2012.2224052http://dx.doi.org/10.1109/TCSVT.2012.2224052］
Radford A， Metz L and Chintala S. 2016. Unsupervised representation learning with deep convolutional generative adversarial networks//Proceedings of the 4th International Conference on Learning Representations. San Juan， Puerto Rico： ［s.n.］
Ramesh A， Dhariwal P， Nichol A， Chu C and Chen M. 2022. Hierarchical text-conditional image generation with CLIP latents ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2204.06125.pdfhttps://arxiv.org/pdf/2204.06125.pdf
Ramesh A， Pavlov M， Goh G， Gray S， Voss C， Radford A， Chen M and Sutskever I. 2021. Zero-shot text-to-image generation//Proceedings of the 38th International Conference on Machine Learning. ［s.l.］： PMLR： 8821-8831
Rombach R， Blattmann A， Lorenz D， Esser P and Ommer B. 2022. High-resolution image synthesis with latent diffusion models//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 10674-10685 ［DOI： 10.1109/CVPR52688.2022.01042http://dx.doi.org/10.1109/CVPR52688.2022.01042］
Rössler A， Cozzolino D， Verdoliva L， Riess C， Thies J and Niessner M. 2019. FaceForensics++： learning to detect manipulated facial images//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea （South）： IEEE： 1-11 ［DOI： 10.1109/ICCV.2019.00009http://dx.doi.org/10.1109/ICCV.2019.00009］
Sabir E， Cheng J X， Jaiswal A， AbdAlmageed W， Masi I and Natarajan P. 2019. Recurrent convolutional strategies for face manipulation detection in videos//Proceedings of 2019 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 80-87
Saharia C， Chan W， Saxena S， Li L L， Whang J， Denton E， Ghasemipour S K S， Ayan B K， Mahdavi S S， Gontijo-Lopes R， Salimans T， Ho J， Fleet D J and Norouzi M. 2022. Photorealistic text-to-image diffusion models with deep language understanding//Proceedings of the 36th Conference on Neural Information Processing Systems. ［s.l.］： ［s.n.］： 36479-36494
Shao R， Wu T X， Nie L Q and Liu Z W. 2023. DeepFake-adapter： dual-level adapter for DeepFake detection ［EB/OL］. ［2023-06-01］. https://arxiv.org/pdf/2306.00863.pdfhttps://arxiv.org/pdf/2306.00863.pdf
Shen Y Q， Heacock L， Elias J， Hentel K D， Reig B， Shih G and Moy L. 2023. ChatGPT and other large language models are double-edged swords. Radiology， 307（2）： #e230163 ［DOI： 10.1148/radiol.230163http://dx.doi.org/10.1148/radiol.230163］
Shi L， Zhang J and Shan S G. 2023. Real face foundation representation learning for generalized deepfake detection ［EB/OL］. ［2023-03-15］. https://arxiv.org/pdf/2303.08439.pdfhttps://arxiv.org/pdf/2303.08439.pdf
Shin S Y and Lee J. 2022. The effect of deepfake video on news credibility and corrective influence of cost-based knowledge about deepfakes. Digital Journalism， 10（3）： 412-432 ［DOI： 10.1080/21670811.2022.2026797http://dx.doi.org/10.1080/21670811.2022.2026797］
Shiohara K and Yamasaki T. 2022. Detecting deepfakes with self-blended images//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 18699-18708 ［DOI： 10.1109/CVPR52688.2022.01816http://dx.doi.org/10.1109/CVPR52688.2022.01816］
Siarohin A， Lathuilière S， Tulyakov S， Ricci E and Sebe N. 2019. First order motion model for image animation//Proceedings of the 33rd International Conference on Neural Information Processing Systems. Vancouver， Canada： Curran Associates Inc.： #641
Song H K， Woo S H， Lee J， Yang S， Cho H， Lee Y， Choi D and Kim K W. 2022a. Talking face generation with multilingual TTS//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 21393-21398 ［DOI： 10.1109/CVPR52688.2022.02074http://dx.doi.org/10.1109/CVPR52688.2022.02074］
Song L C， Fang Z， Li X D， Dong X Y， Jin Z C， Chen Y F and Lyu S W. 2022b. Adaptive face forgery detection in cross domain//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 467-484 ［DOI： 10.1007/978-3-031-19830-4_27http://dx.doi.org/10.1007/978-3-031-19830-4_27］
Sun J X， Deng Q Y， Li Q， Sun M Y， Ren M and Sun Z N. 2022. AnyFace： free-style text-to-face synthesis and manipulation//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 18666-18675 ［DOI： 10.1109/CVPR52688.2022.01813http://dx.doi.org/10.1109/CVPR52688.2022.01813］
Tak H， Todisco M， Wang X， Jung J W， Yamagishi J and Evans N. 2022. Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2202.12233.pdfhttps://arxiv.org/pdf/2202.12233.pdf
Tan L， Yu K P， Shi N， Yang C X， Wei W and Lu H M. 2022. Towards secure and privacy-preserving data sharing for COVID-19 medical records： a blockchain-empowered approach. IEEE Transactions on Network Science and Engineering， 9（1）： 271-281 ［DOI： 10.1109/TNSE.2021.3101842http://dx.doi.org/10.1109/TNSE.2021.3101842］
Tan L F， Wang Y H， Wang J F， Yang L， Chen X X and Guo Y F. 2023. Deepfake video detection via facial action dependencies estimation//Proceedings of the 37th AAAI Conference on Artificial Intelligence. Washington， USA： AAAI： 5276-5284 ［DOI： 10.1609/aaai.v37i4.25658http://dx.doi.org/10.1609/aaai.v37i4.25658］
Tan M K， Xu S K， Zhang S H and Chen Q. 2021. A review on deep adversarial visual generation. Journal of Image and Graphics， 26（12）： 2751-2766
谭明奎， 许守恺， 张书海， 陈奇. 2021. 深度对抗视觉生成综述. 中国图象图形学报， 26（12）： 2751-2766 ［DOI： 10.11834/jig.210252http://dx.doi.org/10.11834/jig.210252］
Tan M X and Le Q. 2019. EfficientNet： rethinking model scaling for convolutional neural networks//Proceedings of the 36th International Conference on Machine Learning. Long Beach， USA： PMLR： 6105-6114
Tewari A， Zollhöfer M， Kim H， Garrido P， Bernard F， Pérez P and Theobalt C. 2017. MoFA： model-based deep convolutional face autoencoder for unsupervised monocular reconstruction//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice， Italy： IEEE： 3735-3744 ［DOI： 10.1109/ICCV.2017.401http://dx.doi.org/10.1109/ICCV.2017.401］
Thies J， Zollhöfer M and Nießner M. 2019. Deferred neural rendering： image synthesis using neural textures. ACM Transactions on Graphics， 38（4）： #66 ［DOI： 10.1145/3306346.3323035http://dx.doi.org/10.1145/3306346.3323035］
Thies J， Zollhöfer M， Stamminger M， Theobalt C and Niessner M. 2016. Face2Face： real-time face capture and reenactment of RGB videos//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas， USA： IEEE： 2387-2395 ［DOI： 10.1109/CVPR.2016.262http://dx.doi.org/10.1109/CVPR.2016.262］
Tian C， Luo Z M， Shi G M and Li S Z. 2023. Frequency-aware attentional feature fusion for deepfake detection//Proceedings of ICASSP 2023-2023 IEEE International Conference on Acoustics， Speech and Signal Processing （ICASSP）. Rhodes Island， Greece： IEEE： 1-5 ［DOI： 10.1109/ICASSP49357.2023.10094654http://dx.doi.org/10.1109/ICASSP49357.2023.10094654］
Wang J K， Wu Z X， Ouyang W H， Han X T， Chen J J， Jiang Y G and Li S N. 2022a. M2TR： multi-modal multi-scale Transformers for deepfake detection//Proceedings of 2022 International Conference on Multimedia Retrieval. Newark， USA： ACM： 615-623 ［DOI： 10.1145/3512527.3531415http://dx.doi.org/10.1145/3512527.3531415］
Wang J W， Li T， Luo X Y， Shi Y Q and Jha S K. 2019. Identifying computer generated images based on quaternion central moments in color quaternion wavelet domain. IEEE Transactions on Circuits and Systems for Video Technology， 29（9）： 2775-2785 ［DOI： 10.1109/TCSVT.2018.2867786http://dx.doi.org/10.1109/TCSVT.2018.2867786］
Wang P P， Cao Y and Zhao X F. 2017a. Segmentation based video steganalysis to detect motion vector modification. Security and Communication Networks， 2017： #8051389 ［DOI： 10.1155/2017/8051389http://dx.doi.org/10.1155/2017/8051389］
Wang R Y， Chu B L， Yang Z and Zhou L N. 2022. An overview of visual DeepFake detection techniques. Journal of Image and Graphics， 27（1）： 43-62
王任颖， 储贝林， 杨震， 周琳娜. 2022. 视觉深度伪造检测技术综述. 中国图象图形学报， 27（1）： 43-62 ［DOI： 10.11834/jig.210410http://dx.doi.org/10.11834/jig.210410］
Wang T Y， Cheng H， Chow K P and Nie L Q. 2022b. Deep convolutional pooling Transformer for deepfake detection ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2209.05299.pdfhttps://arxiv.org/pdf/2209.05299.pdf
Wang T Y and Chow K P. 2023. Noise based deepfake detection via multi-head relative-interaction//Proceedings of the 37th AAAI Conference on Artificial Intelligence. Washington， USA： AAAI： 14548-14556 ［DOI： 10.1609/aaai.v37i12.26701http://dx.doi.org/10.1609/aaai.v37i12.26701］
Wang X， Thome N and Cord M. 2017b. Gaze latent support vector machine for image classification improved by weakly supervised region selection. Pattern Recognition， 72： 59-71 ［DOI： 10.1016/j.patcog.2017.07.001http://dx.doi.org/10.1016/j.patcog.2017.07.001］
Wang X Y， Huang J J， Ma S Q， Nepal S and Xu C. 2022c. DeepFake disrupter： the detector of DeepFake is my friend//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 14900-14909 ［DOI： 10.1109/CVPR52688.2022.01450http://dx.doi.org/10.1109/CVPR52688.2022.01450］
Wang Y， Yu K， Chen C， Hu X Y and Peng S L. 2023. Dynamic graph learning with content-guided spatial-frequency relation reasoning for deepfake detection//Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Vancouver， Canada： IEEE： 7278-7287 ［DOI： 10.1109/CVPR52729.2023.00703http://dx.doi.org/10.1109/CVPR52729.2023.00703］
Wang Y G， Lei Y Q and Huang J W. 2010. An image copy detection scheme based on radon transform//Proceedings of 2010 IEEE International Conference on Image Processing. Hong Kong， China： IEEE： 1009-1012 ［DOI： 10.1109/ICIP.2010.5653456http://dx.doi.org/10.1109/ICIP.2010.5653456］
Wang Y G， Zhu G P and Shi Y Q. 2018. Transportation spherical watermarking. IEEE Transactions on Image Processing， 27（4）： 2063-2077 ［DOI： 10.1109/TIP.2018.2795745http://dx.doi.org/10.1109/TIP.2018.2795745］
Xia Z M， Qiao T， Xu M， Zheng N and Xie S C. 2022. Towards DeepFake video forensics based on facial textural disparities in multi-color channels. Information Sciences， 607： 654-669 ［DOI： 10.1016/j.ins.2022.06.003http://dx.doi.org/10.1016/j.ins.2022.06.003］
Xie T， Yu L Y， Luo C W， Xie H T and Zhang Y D. 2023. Survey of deep face manipulation and fake detection. Journal of Tsinghua University （Science and Technology）， 63（9）： 1350-1365
谢天， 于灵云， 罗常伟， 谢洪涛， 张勇东. 2023. 深度人脸伪造与检测技术综述. 清华大学学报（自然科学版）， 63（9）： 1350-1365 ［DOI： 10.16511/j.cnki.qhdxxb.2023.21.002http://dx.doi.org/10.16511/j.cnki.qhdxxb.2023.21.002］
Xu C， Zhang J N， Hua M， He Q， Yi Z L and Liu Y. 2022a. Region-aware face swapping//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 7622-7631 ［DOI： 10.1109/CVPR52688.2022.00748http://dx.doi.org/10.1109/CVPR52688.2022.00748］
Xu Y， Raja K and Pedersen M. 2022b. Supervised contrastive learning for generalizable and explainable DeepFakes detection//Proceedings of 2022 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops. Waikoloa， USA： IEEE： 379-389 ［DOI： 10.1109/WACVW54805.2022.00044http://dx.doi.org/10.1109/WACVW54805.2022.00044］
Xu Y， Raja K， Verdoliva L and Pedersen M. 2023. Learning pairwise interaction for generalizable DeepFake detection//Proceedings of 2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops. Waikoloa， USA： IEEE： 1-11 ［DOI： 10.1109/WACVW58289.2023.00074http://dx.doi.org/10.1109/WACVW58289.2023.00074］
Xu Y B， Yin Y Q， Jiang L M， Wu Q Y， Zheng C Y， Loy C C， Dai B and Wu W. 2022c. TransEditor： Transformer-based dual-space GAN for highly controllable facial editing//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 7673-7682 ［DOI： 10.1109/CVPR52688.2022.00753http://dx.doi.org/10.1109/CVPR52688.2022.00753］
Xu Y Y， Deng B L， Wang J L， Jing Y Q， Pan J and He S F. 2022d. High-resolution face swapping via latent semantics disentanglement//Proceedings of 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans， USA： IEEE： 7632-7641 ［DOI： 10.1109/CVPR52688.2022.00749http://dx.doi.org/10.1109/CVPR52688.2022.00749］
Xu Z P， Liu J R， Lu W， Xu B Z， Zhao X F， Li B and Huang J W. 2021. Detecting facial manipulated videos based on set convolutional neural networks. Journal of Visual Communication and Image Representation， 77： #103119 ［DOI： 10.1016/j.jvcir.2021.103119http://dx.doi.org/10.1016/j.jvcir.2021.103119］
Yang F， Li J W， Lu W and Weng J. 2017. Copy-move forgery detection based on hybrid features. Engineering Applications of Artificial Intelligence， 59： 73-83 ［DOI： 10.1016/j.engappai.2016.12.022http://dx.doi.org/10.1016/j.engappai.2016.12.022］
Yang S C， Wang J， Sun Y L and Tang J H. 2022. Multi-level features global consistency for human facial deepfake detection. Journal of Image and Graphics， 27（9）： 2708-2720
杨少聪， 王健， 孙运莲， 唐金辉. 2022. 多级特征全局一致性的伪造人脸检测. 中国图象图形学报， 27（9）： 2708-2720 ［DOI： 10.11834/jig.211254http://dx.doi.org/10.11834/jig.211254］
Yang T Y， Huang Z Y， Cao J， Li L and Li X R. 2022. Deepfake network architecture attribution//Proceedings of the 36th AAAI Conference on Artificial Intelligence. ［s.l.］： AAAI： 4662-4670 ［DOI： 10.1609/aaai.v36i4.20391http://dx.doi.org/10.1609/aaai.v36i4.20391］
Yang X， Li Y Z and Lyu S W. 2019. Exposing deep fakes using inconsistent head poses//Proceedings of ICASSP 2019-2019 IEEE International Conference on Acoustics， Speech and Signal Processing （ICASSP）. Brighton， UK： IEEE： 8261-8265 ［DOI： 10.1109/ICASSP.2019.8683164http://dx.doi.org/10.1109/ICASSP.2019.8683164］
Yi R， Ye Z P， Zhang J， Bao H J and Liu Y J. 2020. Audio-driven talking face video generation with learning-based personalized head pose ［EB/OL］. ［2023-02-15］. https://arxiv.org/pdf/2002.10137.pdfhttps://arxiv.org/pdf/2002.10137.pdf
Yu N， Skripniuk V， Abdelnabi S and Fritz M. 2021. Artificial fingerprinting for generative models： rooting deepfake attribution in training data//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 14428-14437 ［DOI： 10.1109/ICCV48922.2021.01418http://dx.doi.org/10.1109/ICCV48922.2021.01418］
Zhang H， Goodfellow I， Metaxas D and Odena A. 2019. Self-attention generative adversarial networks//Proceedings of International Conference on Machine Learning. Long Beach， USA： PMLR： 7354-7363
Zhang L， Qiao T， Xu M， Zheng N and Xie S C. 2022. Unsupervised learning-based framework for deepfake video detection. IEEE Transactions on Multimedia ［DOI： 10.1109/TMM.2022.3182509http://dx.doi.org/10.1109/TMM.2022.3182509］
Zhang Q B， Lu W and Weng J. 2016. Joint image splicing detection in DCT and Contourlet transform domain. Journal of Visual Communication and Image Representation， 40： 449-458 ［DOI： 10.1016/j.jvcir.2016.07.013http://dx.doi.org/10.1016/j.jvcir.2016.07.013］
Zhang W M， Ma K D and Yu N H. 2014. Reversibility improved data hiding in encrypted images. Signal Processing， 94： 118-127 ［DOI： 10.1016/j.sigpro.2013.06.023http://dx.doi.org/10.1016/j.sigpro.2013.06.023］
Zhang Z Y， Yi X W and Zhao X F. 2021. Fake speech detection using residual network with Transformer encoder//Proceedings of 2021 ACM Workshop on Information Hiding and Multimedia Security. ［s.l.］： ACM： 13-22 ［DOI： 10.1145/3437880.3460408http://dx.doi.org/10.1145/3437880.3460408］
Zhao B， Zhang S Z， Xu C X， Sun Y F and Deng C B. 2021a. Deep fake geography？ When geospatial data encounter Artificial Intelligence. Cartography and Geographic Information Science， 48（4）： 338-352 ［DOI： 10.1080/15230406.2021.1910075http://dx.doi.org/10.1080/15230406.2021.1910075］
Zhao T C， Xu X， Xu M Z， Ding H， Xiong Y J and Xia W. 2021b. Learning self-consistency for deepfake detection//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 15003-15013 ［DOI： 10.1109/ICCV48922.2021.01475http://dx.doi.org/10.1109/ICCV48922.2021.01475］
Zheng Y L， Bao J M， Chen D， Zeng M and Wen F. 2021. Exploring temporal coherence for more general video face forgery detection//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 15024-15034 ［DOI： 10.1109/ICCV48922.2021.01477http://dx.doi.org/10.1109/ICCV48922.2021.01477］
Zhou W B， Zhang W M， Yu N H， Zhao H Q， Liu H G and Wei T Y. 2021. An overview of Deepfake forgery and defense techniques. Journal of Signal Processing， 37（12）： 2338-2355
周文柏， 张卫明， 俞能海， 赵汉卿， 刘泓谷， 韦天一. 2021. 人脸视频深度伪造与防御技术综述. 信号处理， 37（12）： 2338-2355 ［DOI： 10.16798/j.issn.1003-0530.2021.12.007http://dx.doi.org/10.16798/j.issn.1003-0530.2021.12.007］
Zhou Y， Fan B， Atrey P K and Ding F. 2023. Exposing deepfakes using dual-channel network with multi-axis attention and frequency analysis//Proceedings of 2023 ACM Workshop on Information Hiding and Multimedia Security. Chicago， USA： ACM： 169-174 ［DOI： 10.1145/3577163.3595103http://dx.doi.org/10.1145/3577163.3595103］
Zhou Y P and Lim S N. 2021. Joint audio-visual deepfake detection//Proceedings of 2021 IEEE/CVF International Conference on Computer Vision. Montreal， Canada： IEEE： 14780-14789 ［DOI： 10.1109/ICCV48922.2021.01453http://dx.doi.org/10.1109/ICCV48922.2021.01453］
Zhu K M， Xu W B， Lu W and Zhao X F. 2022. Deepfake video detection with feature interaction amongst key frames. Journal of Image and Graphics， 27（1）： 188-202
祝恺蔓， 徐文博， 卢伟， 赵险峰. 2022. 多关键帧特征交互的人脸篡改视频检测. 中国图象图形学报， 27（1）： 188-202 ［DOI： 10.11834/jig.210408http://dx.doi.org/10.11834/jig.210408］
Zhu X S， Qian Y J， Zhao X F， Sun B and Sun Y. 2018. A deep learning approach to patch-based image inpainting forensics. Signal Processing： Image Communication， 67： 90-99 ［DOI： 10.1016/j.image.2018.05.015http://dx.doi.org/10.1016/j.image.2018.05.015］
Zhuo W Q， Li D Z， Wang W and Dong J. 2023. Data-free model compression for light-weight DeepFake detection. Journal of Image and Graphics， 28（3）： 820-835
卓文琦， 李东泽， 王伟， 董晶. 2023. 面向轻量级深度伪造检测的无数据模型压缩. 中国图象图形学报， 28（3）： 820-835 ［DOI： 10.11834/jig.220559http://dx.doi.org/10.11834/jig.220559］
Zi B J， Chang M H， Chen J J， Ma X J and Jiang Y G. 2020. WildDeepfake： a challenging real-world dataset for deepfake detection//Proceedings of the 28th ACM International Conference on Multimedia. Seattle， USA： ACM， 2382-2390 ［DOI： 10.1145/3394171.3413769http://dx.doi.org/10.1145/3394171.3413769］
相关作者
相关机构
京公网安备11010802024621