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纸质出版日期： 2023-11-16 ，
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戴昀书， 费建伟， 夏志华， 刘家男， 翁健. 2023. 局部相似度异常的强泛化性伪造人脸检测. 中国图象图形学报， 28(11):3453-3470
Dai Yunshu， Fei Jianwei， Xia Zhihua， Liu Jianan， Weng Jian. 2023. Local similarity anomaly for general face forgery detection. Journal of Image and Graphics， 28(11):3453-3470
目的
2
人脸伪造技术迅猛发展，对社会信息安全构成了严重威胁，亟需强泛化性伪造人脸检测算法抵抗多种多样的伪造模型。目前的研究发现伪造算法普遍包含人脸与背景融合的操作，这意味着任何伪造方式都难以避免在人脸边缘遗留下伪造痕迹。根据这一发现，本文将模型的学习目标从特定的伪造痕迹特征转化为更加普适的人脸图像局部相似度特征，并提出了局部相似度异常的深度伪造人脸检测算法。
方法
2
首先提出了局部相似度预测（local similarity predicator，LSP）模块，通过一组局部相似度预测器分别计算RGB图像中间层特征图的局部异常，同时，为了捕捉频域中的真伪线索，还提出了可学习的空域富模型卷积金字塔（spatial rich model convolutional pyramid，SRMCP）来提取多尺度的高频噪声特征。
结果
2
在多个数据集上进行了大量实验。在泛化性方面，本文以ResNet18为骨干网络的模型在FF++ 4个子集上的跨库检测精度分别以0.77%、5.59%、6.11%和4.28%的优势超越了对比方法。在图像压缩鲁棒性方面，在3种不同压缩效果下，分别以2.48%、4.83%和10.10%的优势超越了对比方法。
结论
2
本文方法能够大幅度提升轻量型卷积神经网络的检测性能，相比于绝大部分工作都取得了更优异的泛化性和鲁棒性效果。
Objective
2
In recent years， the development of DeepFake has made great progress， and the highly realistic forged face images created by such technology are posing a great threat not only to people’s privacy and security but also to the international political situation. Therefore， detection methods with good generalization ability need to be developed. In their early stages of development， forged faces had low fidelity with obvious defects. Therefore， traditional digital forensic algorithms and deep learning models could achieve good detection performances. However， with the development of DeepFake， these forged faces become increasingly realistic， thus posing a challenge to detection algorithms. Researchers have focused on the essential differences between real and forged faces to improve the detection performances of their algorithms. The process of DeepFake can be decomposed into the following steps： 1） detect and crop the face in the target image； 2） forge the face using a forgery algorithm； 3） paste the forged face back to the original image and use image fusion technology to eliminate the boundary defects and improve the visual effect. Step 3 often results in easily detectable local forgery traces， which are important cues for distinguishing real faces from fake ones. Many researchers have attempted to build models that can learn such traces to improve accuracy or to implement tampering localization. However， given that both the local traces and the image fusion methods involved in different forgery techniques widely differ， the detection algorithms for different forgery techniques have limited generalization ability. Therefore， although the local traces caused by Step 3 above are universal， directly learning such features for real and forged face recognition contributes little to generalizability.
Method
2
This paper proposes a DeepFake detection method based on local similarity anomalies to achieve high generalizability. Instead of directly learning local forgery traces to distinguish real faces from fake ones， this method transforms the learning objective into the similarity of local features. Specifically， the face region of the forged face image has source features that differ from the background region， and although these two types of regions have uniform source features internally， the fusion boundary between the face and background contains conflicting source features and thus has low level of local similarity. These local similarity anomalies are independent of both the specific forgery algorithm and the fusion algorithm and can be regarded as heterogeneous features that are highly consistent with the essential difference between real and fake faces. To cache these traces， this paper proposes the local similarity predicator module. By decomposing the local depth features of face images into horizontal and vertical groups， the learning objective is converted from recognizing specific forgery traces to predicting the similarity of source features within the image by calculating the similarity of local depth features and their neighbors so as to capture the essential differences between real and fake faces in a general way. In addition， previous studies find that frequency domain features contain important clues for distinguishing real from fake faces. The proposed method draws on the domain knowledge of steganalysis and constructs a learnable convolutional pyramid module based on the spatial rich model（SRM）， which compensates for the limited ability to express true and false features in the RGB space and improves the in-domain detection performance. This study also proposes the spatial rich model convolutional pyramid， which inherits the high-frequency noise features extracted by the spatial rich model convolutional pyramid （SRMCP） kernel， can be continuously updated during the training， and can be extended to a pyramid architecture with different receptive fields to effectively capture high-frequency noise features at different scales.
Result
2
The overall results of FF++ are compared under three compression factors. The proposed method， which uses ResNet18 as its backbone， achieves extremely high detection accuracy on both raw and compressed datasets. This method not only significantly outperforms the classical digital forensic algorithms but also surpasses some of the recently proposed advanced algorithms for deep forgery detection. Specifically， the proposed method achieves 99.72%， 98.34%， and 90.73% accuracies on RAW， C23， and C40， respectively， and its average accuracy is 2.31% and 13.33% （20.26% on the C40 dataset） higher than those of Xception and MesoNet， respectively. The proposed method also outperforms a metric learning method published in CVPR 2021 that incorporates the frequency and space domains. Specifically， the proposed method achieves 0.29%， 1.63%， and 1.22% higher accuracies on RAW， C23， and C40， respectively， compared with this metric learning method. Overall， the proposed method takes the lead in terms of accuracy. Experimental results reveal that the local similarity module can effectively capture the inherent features of forged faces， thus substantially improving detection accuracy and achieving high accuracy even with a simple ResNet18 as the backbone. The average cross-domain area under curves （AUCs） of the proposed method reach 91.40%， 96.03%， 99.08%， and 96.05% on the four subsets of FF++， which are 15.41%， 16.47%， 21.11%， and 14.7% higher than those of Xception， respectively. In addition， the average accuracies of the proposed method are improved by 0.77%， 5.59%， 6.11%， and 4.28%， respectively， compared with state-of-the-art methods. The cross-domain results on Celeb-DF show that the proposed method outperforms the existing methods with the help of ResNet18. Although recently introduced methods have made significant progress in cross-domain detection with an average accuracy exceeding 70%， the cross-domain accuracies of the proposed method are 1.11%， 3.73%， and 5.17% higher compared with those of state-of-the-art methods.
Conclusion
2
The method proposed in this paper can greatly improve the detection performance of lightweight convolutional neural networks and achieves better generalization and robustness compared with other recently proposed methods. The local similarity learning module will be further optimized in future work to ensure that it can predict local anomalies with different types of forged faces to further improve its generalizability on unknown forged faces.
深度伪造人脸检测空域富模型（SRM）卷积金字塔局部学习相似度多任务学习
deep face forgery detectionspatially rich model （SRM）convolutional pyramidlocal similarity learningmulti-task learning
Afchar D， Nozick V， Yamagishi J and Echizen I. 2018. Mesonet： a compact facial video forgery detection network//Proceedings of 2018 IEEE International Workshop on Information Forensics and Security. Hong Kong， China： IEEE： 1-7 ［DOI： 10.1109/WIFS.2018.8630761http://dx.doi.org/10.1109/WIFS.2018.8630761］
Agarwal S， Farid H， Gu Y M， He M M， Nagano K and Li H. 2019. Protecting world leaders against deep fakes//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 38-45
Agarwal A， Singh R， Vatsa M and Noore A. 2017. SWAPPED！ digital face presentation attack detection via weighted local magnitude pattern//Proceedings of 2017 IEEE International Joint Conference on Biometrics. Denver， USA： IEEE： 659-665 ［DOI： 10.1109/BTAS.2017.8272754http://dx.doi.org/10.1109/BTAS.2017.8272754］
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 Workshop. Seoul， Korea （South）： IEEE： 1205-1207 ［DOI： 10.1109/ICCVW.2019.00152http://dx.doi.org/10.1109/ICCVW.2019.00152］
Bitouk D， Kumar N， Dhillon S， Belhumeur P and Nayar S K. 2008. Face swapping： automatically replacing faces in photographs. ACM Transactions on Graphics， 27（3）： #1360638 ［DOI： 10.1145/1360612.1360638http://dx.doi.org/10.1145/1360612.1360638］
Blanz V， Scherbaum K， Vetter T and Seidel H P. 2004. Exchanging faces in images. Computer Graphics Forum， 23（3）： 669-676
Bondi L， Cannas E D， Bestagini P and Tubaro S. 2020. Training strategies and data augmentations in CNN-based DeepFake video detection//Proceedings of 2020 IEEE International Workshop on Information Forensics and Security. New York， USA： IEEE： #9360901 ［DOI： 10.1109/WIFS49906.2020.9360901http://dx.doi.org/10.1109/WIFS49906.2020.9360901］
Bonettini N， Cannas E D， Mandelli S， Bondi L， Bestagini P and Tubaro S. 2021. Video face manipulation detection through ensemble of cnns//Proceedings of the 25th International Conference on Pattern Recognition. Milan， Italy： IEEE： 5012-5019 ［DOI： 10.1109/ICPR48806.2021.9412711http://dx.doi.org/10.1109/ICPR48806.2021.9412711］
Chen P， Liang T， Liu J， Dai J and Han J Z. 2020. Forged facial video detection based on global temporal and local spatial feature. Journal of Cyber Security， 5（2）： 73-83
陈鹏， 梁涛， 刘锦， 戴娇， 韩冀中. 2020. 融合全局时序和局部空间特征的伪造人脸视频检测方法. 信息安全学报， 5（2）： 73-83 ［DOI： 10.19363/J.cnki.cn10-1380/tn.2020.02.06http://dx.doi.org/10.19363/J.cnki.cn10-1380/tn.2020.02.06］
Chen S， Yao T P， Chen Y， Ding S H， Li J L and Ji R R. 2021. Local relation learning for face forgery detection//Proceedings of the 35th AAAI Conference on Artificial Intelligence. Menlo Park， USA： AAAI： 1081-1088 ［DOI： 10.1609/aaai.v35i2.16193http://dx.doi.org/10.1609/aaai.v35i2.16193］
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］
Chugh K， Gupta P， Dhall A and Subramanian R. 2020. Not made for each other-audio-visual dissonance-based deepfake detection and localization//Proceedings of the 28th ACM International Conference on Multimedia. Seattle， USA： ACM： 439-447 ［DOI： 10.1145/3394171.3413700http://dx.doi.org/10.1145/3394171.3413700］
Cozzolino D， Poggi G and Verdoliva L. 2017. Recasting residual-based local descriptors as convolutional neural networks： an application to image forgery detection//Proceedings of the 5th ACM Workshop on Information Hiding and Multimedia Security. Philadelphia， USA： ACM： 159-164 ［DOI： 10.1145/3082031.3083247http://dx.doi.org/10.1145/3082031.3083247］
De La Hunty M， Asthana A and Goecke R. 2010. Linear facial expression transfer with active appearance models//Proceedings of the 20th International Conference on Pattern Recognition. Istanbul， Turkey： IEEE： 3789-3792 ［DOI： 10.1109/ICPR.2010.923http://dx.doi.org/10.1109/ICPR.2010.923］
Fernandes S， Raj S， Ortiz E， Vintila I， Salter M， Urosevic G and Jha S. 2019. Predicting heart rate variations of deepfake videos using neural ODE//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision Workshop. Seoul， Korea （South）： IEEE： 1721-1729 ［DOI： 10.1109/ICCVW.2019.00213http://dx.doi.org/10.1109/ICCVW.2019.00213］
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. Online： PMLR： 3247-3258 ［DOI： 10.48550/arXiv.2003.08685http://dx.doi.org/10.48550/arXiv.2003.08685］
Fridrich J and Kodovsky J. 2012. Rich models for steganalysis of digital images. IEEE Transactions on Information Forensics and Security， 7（3）： 868-882 ［DOI： 10.1109/TIFS.2012.2190402http://dx.doi.org/10.1109/TIFS.2012.2190402］
Gu Z H， Chen Y， Yao T P， Ding S H， Li J L， Huang F Y and Ma L Z. 2021. Spatiotemporal inconsistency learning for deepfake video detection//Proceedings of the 29th ACM International Conference on Multimedia. Online： ACM： 3473-3481
Han B， Han X G， Zhang H， Li J Z and Cao X C. 2021. Fighting fake news： two stream network for deepfake detection via learnable SRM. IEEE Transactions on Biometrics， Behavior， and Identity Science， 3（3）： 320-331 ［DOI： 10.1109/TBIOM.2021.3065735http://dx.doi.org/10.1109/TBIOM.2021.3065735］
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 P S， Li W C， Zhang J Y， Wang H X and Jiang X H. 2022. Overview of passive forensics and anti-forensics techniques for GAN-generated image. Journal of Image and Graphics， 27（1）： 88-110
何沛松， 李伟创， 张婧媛， 王宏霞， 蒋兴浩. 2022. 面向GAN生成图像的被动取证及反取证技术综述. 中国图象图形学报， 27（1）： 88-110 ［DOI： 10.11834/jig.210430http://dx.doi.org/10.11834/jig.210430］
Hu J， Liao X， Wang W and Qin Z. 2022. Detecting compressed deepfake videos in social networks using frame-temporality two-stream convolutional network. IEEE Transactions on Circuits and Systems for Video Technology， 32（3）： 1089-1102 ［DOI： 10.1109/TCSVT.2021.3074259http://dx.doi.org/10.1109/TCSVT.2021.3074259］
Hu Z H， Xie H T， Wang Y X， Li J H， Wang Z Y and Zhang Y D. 2021. Dynamic inconsistency-aware deepFake video detection//Proceedings of the 30th International Joint Conference on Artificial Intelligence. Montreal， Canada： IJCAI： 736-742
Kong C Q， Chen B L， Li H L， Wang S Q， Rocha A and Kwong S. 2022. Detect and locate： exposing face manipulation by semantic-and noise-level telltales. IEEE Transactions on Information Forensics and Security， 17： 1741-1756 ［DOI： 10.1109/TIFS.2022.3169921http://dx.doi.org/10.1109/TIFS.2022.3169921］
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］
Li J M， Xie H T， Li J H， Wang Z Y and Zhang Y D. 2021. Frequency-aware discriminative feature learning supervised by single-center loss for face forgery detection//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 6454-6463 ［DOI： 10.1109/CVPR46437.2021.00639http://dx.doi.org/10.1109/CVPR46437.2021.00639］
Li L Z， Bao J M， Yang H， Chen D and Wen F. 2020a. Advancing high fidelity identity swapping for forgery detection//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 5073-5082 ［DOI： 10.1109/CVPR42600.2020.00512http://dx.doi.org/10.1109/CVPR42600.2020.00512］
Li L Z， Bao J M， Zhang T， Yang H， Chen D， Wen F and Guo B N. 2020b. 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， Ni R R， Yang P P， Fu Z Q and Zhao Y. 2023. Artifacts-disentangled adversarial learning for deepfake detection. IEEE Transactions on Circuits and Systems for Video Technology， 33（4）： 1658-1670 ［DOI： 10.1109/TCSVT.2022.3217950http://dx.doi.org/10.1109/TCSVT.2022.3217950］
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 Z， Chang M C and Lyu S W. 2018. In ictu oculi： exposing AI created fake videos by detecting eye blinking//Proceedings of 2018 IEEE International Workshop on Information Forensics and Security. Hong Kong， China： IEEE： #8630787 ［DOI： 10.1109/WIFS.2018.8630787http://dx.doi.org/10.1109/WIFS.2018.8630787］
Li Y Z and Lyu S W. 2019. Exposing deepFake videos by detecting face warping artifacts//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach， USA： IEEE： 46-52
Liang J H， Shi H F and Deng W H. 2022. Exploring disentangled content information for face forgery detection//Proceedings of the 17th European Conference on Computer Vision. Tel Aviv， Israel： Springer： 128-145 ［DOI： 10.1007/978-3-031-19781-9_8http://dx.doi.org/10.1007/978-3-031-19781-9_8］
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］
Liu H G， Li X D， Zhou W B， Chen Y F， He Y， Xue H， Zhang W M and Yu N H. 2021. Spatial-phase shallow learning： rethinking face forgery detection in frequency domain//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 772-781 ［DOI： 10.1109/CVPR46437.2021.00083http://dx.doi.org/10.1109/CVPR46437.2021.00083］
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］
Malik S. 2003. Digital Face Replacement in Photographs. Toronto， Canada： University of Toronto
Marra F， Gragnaniello D， Verdoliva L and Poggi G. 2019. Do gans leave artificial fingerprints?//Proceedings of 2019 IEEE Conference on Multimedia Information Processing and Retrieval. San Jose， USA： IEEE： 506-511 ［DOI： 10.1109/MIPR.2019.00103http://dx.doi.org/10.1109/MIPR.2019.00103］
Masi I， Killekar A， Mascarenhas R M， Gurudatt S P and Abdalmageed W. 2020. Two-branch recurrent network for isolating deepfakes in videos//Proceedings of the 16th European Conference on Computer Vision. Glasgow， UK： Springer： 667-684 ［DOI： 10.1007/978-3-030-58571-6_39http://dx.doi.org/10.1007/978-3-030-58571-6_39］
McCloskey S and Albright M. 2019. Detecting GAN-generated imagery using saturation cues//Proceedings of 2019 IEEE International Conference on Image Processing. Taipei， China： IEEE： 4584-4588 ［DOI： 10.1109/ICIP.2019.8803661http://dx.doi.org/10.1109/ICIP.2019.8803661］
Naruniec J， Helminger L， Schroers C and Weber R M. 2020. High-resolution neural face swapping for visual effects. Computer Graphics Forum， 39（4）： 173-184 ［DOI： 10.1111/cgf.14062http://dx.doi.org/10.1111/cgf.14062］
Nguyen H H， Yamagishi J and Echizen I. 2019. Capsule-forensics： using capsule networks to detect forged images and videos//Proceedings of 2019 IEEE International Conference on Acoustics， Speech and Signal Processing. Brighton， UK： IEEE： 2307-2311 ［DOI： 10.1109/ICASSP.2019.8682602http://dx.doi.org/10.1109/ICASSP.2019.8682602］
Pighin F， Hecker J， Lischinski D， Szeliski R and Salesin D H. 1998. Synthesizing realistic facial expressions from photographs//Proceedings of the 25th ACM SIGGRAPH. New York， USA： ACM： 75-84
Qi H， Guo Q， Xu F J， Xie X F， Ma L， Feng W， Liu Y and Zhao J J. 2020. DeepRhythm： exposing DeepFakes with attentional visual heartbeat rhythms//Proceedings of the 28th ACM International Conference on Multimedia. Seattle， USA： ACM： 4318-4327 ［DOI： 10.1145/3394171.3413707http://dx.doi.org/10.1145/3394171.3413707］
Qian Y Y， Yin G J， Sheng L， Chen Z X and Shao J. 2020. Thinking in frequency： face forgery detection by mining frequency-aware clues//Proceedings of the 16th European Conference on computer Vision. Glasgow， UK： Springer： 86-103 ［DOI： 10.1007/978-3-030-58610-2_6http://dx.doi.org/10.1007/978-3-030-58610-2_6］
Rahmouni N， Nozick V， Yamagishi J and Echizen I. 2017. Distinguishing computer graphics from natural images using convolution neural networks//Proceedings of 2017 IEEE Workshop on Information Forensics and Security. Rennes， France： IEEE： #8267647 ［DOI： 10.1109/WIFS.2017.8267647http://dx.doi.org/10.1109/WIFS.2017.8267647］
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： #9 ［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
Wang J， Sun Y L and Tang J H. 2022. LiSiam： localization invariance siamese network for deepfake detection. IEEE Transactions on Information Forensics and Security， 17： 2425-2436 ［DOI： 10.1109/TIFS.2022.3186803http://dx.doi.org/10.1109/TIFS.2022.3186803］
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 S Y， Wang O， Zhang R， Owens A and Efros A A. 2020. CNN-generated images are surprisingly easy to spot... for now//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle， USA： IEEE： 8692-8701 ［DOI： 10.1109/CVPR42600.2020.00872http://dx.doi.org/10.1109/CVPR42600.2020.00872］
Wu X， Xie Z， Gao Y T and Xiao Y. 2020. SSTNet： detecting manipulated faces through spatial， steganalysis and temporal features//Proceedings of 2020 IEEE International Conference on Acoustics， Speech and Signal Processing. Barcelona， Spain： IEEE： 2952-2956 ［DOI： 10.1109/ICASSP40776.2020.9053969http://dx.doi.org/10.1109/ICASSP40776.2020.9053969］
Yang J C， Li A Y， Xiao S， Lu W and Gao X B. 2021. MTD-Net： learning to detect deepfakes images by multi-scale texture difference. IEEE Transactions on Information Forensics and Security， 16： 4234-4245 ［DOI： 10.1109/TIFS.2021.3102487http://dx.doi.org/10.1109/TIFS.2021.3102487］
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］
Yu N， Davis L and Fritz M. 2019. Attributing fake images to gans： Learning and analyzing gan fingerprints//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul， Korea： IEEE： 7555-7565
Yu P P， Fei J W， Xia Z H， Zhou Z L and Weng J. 2022. Improving generalization by commonality learning in face forgery detection. IEEE Transactions on Information Forensics and Security， 17： 547-558 ［DOI： 10.1109/TIFS.2022.3146781http://dx.doi.org/10.1109/TIFS.2022.3146781］
Zhang D C， Li C Y， Lin F Z， Zeng D and Ge S M. 2021. Detecting deepfake videos with temporal dropout 3DCNN//Proceedings of the 30th International Joint Conference on Artificial Intelligence. Montreal， Canada： IJCAI： 1288-1294
Zhang Y， Zheng L L and Thing V L L. 2017. Automated face swapping and its detection//Proceedings of the 2nd IEEE International Conference on Signal and Image Processing. Singapore， Singapore： IEEE： 15-19 ［DOI： 10.1109/SIPROCESS.2017.8124497http://dx.doi.org/10.1109/SIPROCESS.2017.8124497］
Zhang Y X， Li G， Cao Y and Zhao X F. 2020. A method for detecting human-face-tampered videos based on interframe difference. Journal of Cyber Security， 5（2）： 49-72
张怡暄， 李根， 曹纭， 赵险峰. 2020. 基于帧间差异的人脸篡改视频检测方法. 信息安全学报， 5（2）： 49-72 ［DOI： 10.19363/J.cnki.cn10-1380/tn.2020.02.05http://dx.doi.org/10.19363/J.cnki.cn10-1380/tn.2020.02.05］
Zhao H Q， Wei T Y， Zhou W B， Zhang W M， Chen D D and Yu N H. 2021. Multi-attentional deepfake detection//Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville， USA： IEEE： 2185-2194 ［DOI： 10.1109/CVPR46437.2021.00222http://dx.doi.org/10.1109/CVPR46437.2021.00222］
Zhou P， Han X T， Morariu V I and Davis L S. 2018. Learning rich features for image manipulation detection//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City， USA： IEEE： 1053-1061 ［DOI： 10.1109/CVPR.2018.00116http://dx.doi.org/10.1109/CVPR.2018.00116］
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］
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