Computer Science ›› 2022, Vol. 49 ›› Issue (2): 12-30.doi: 10.11896/jsjkx.210900146

• Computer Vision: Theory and Application • Previous Articles     Next Articles

Survey on Generalization Methods of Face Forgery Detection

DONG Lin1, HUANG Li-qing1,2,3, YE Feng1,2,3, HUANG Tian-qiang1,2,3, WENG Bin1,2,3, XU Chao1,2,3   

  1. 1 College of Computer and Cyber Security,Fujian Normal University,Fuzhou 350117,China
    2 Digital Fujian Institute of Big Data Security Technology,Fuzhou 350117,China
    3 Fujian Provincial Engineering Research Center of Big Data Analysis and Application,Fuzhou 350117,China
  • Received:2021-09-16 Revised:2021-10-25 Online:2022-02-15 Published:2022-02-23
  • About author:DONG Lin,born in 1999,postgraduate,is a member of China Computer Federation.Her main research interests include computer vision and multi-media security.
    YE Feng,born in 1978,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include computer vision and artificial intelligence.
  • Supported by:
    National Key R&D Program Special Fund(2018YFC1505805),National Natural Science Foundation of China(62072106),Science and Technology Plan Innovation Strategy Research of Fujian Province,China(2020R0178,2021R0041) and Education Department Program of Fujian Province,China(JT180078).

Abstract: The rapid development of deep learning technology provides powerful tools for the research of deepfake.Forged videos and images are more and more difficult for human eyes to distinguish between real and fake.Videos and images on the internet may have a huge negative impact on social life,such as financial fraud,the spread of fake news,and personal bullying.At present,the fake face detection technology based on deep learning has reached a high accuracy on multiple benchmark databases such as FaceForensics++,but the detection accuracy on cross-databases is much lower than accuracy on the source database,that is,it is difficult for many detection methods to generalize to different types of forgeries,or unknown types of forgeries,which also motivates more scholars to focus on generalization methods.The generalization research of face forgery detection focuses on methods based on deep learning.Firstly,the commonly used datasets including real-world datasets and multi-task datasets for forgery detection are discussed and compared.Secondly,it classifies and summarizes the generalization of video and image tampering detection from three aspects:data,features,and learning strategies.The data refers to data augmentation in deepfake detection.The features include single-domain features such as frequency domain features and multi-domain features.The learning strategies consist of transfer learning,multi-task learning,meta-learning,and incremental learning.And the advantages and shortcomings of three different types are analyzed.Finally,the future development direction and challenges of face tampering detection generalization are discussed.

Key words: Face forgery detection, Generalization, Media forensics, Video image classification, Video image tampering

CLC Number: 

  • TP309
[1]KARRAS T,LAINE S,AILA T.A style-based generator architecture for generative adversarial networks[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019.
[2]DANG H,LIU F,STEHOUWER J,et al.On the detection ofdigital face manipulation[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020:5780-5789.
[3]NEVES J C,TOLOSANA R,VERA-RODRIGUEZ R,et al.GANprintR:Improved fakes and evaluation of the state-of-the-art in face manipulation detection [J].IEEE Journal of Selected Topics in Signal Processing,2020.
[4]GOODFELLOW I,OUFET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial nets[C]//Neural Information Processing Systems (NeuralIPS'14).2014:2672-2680.
[5]KARRAS T,AILA T,LAINE S,et al.Progressive growing ofgans for improved quality,stability,and variation[C]//Procee-dings of the International Conference on Learning Representations (ICLR).2018.
[6]FaceSwap[EB/OL].(2018-10-29) [2021-10-12].https://github.com/MarekKowalski/FaceSwap.
[7]DeepFakes[EB/OL].(2018-10-29)[2021-10-12].https://github.com/deepfakes/faceswap.
[8]LI L Z,BAO J,YANG H,et al.FaceShifter:Towards high fide-lity and occlusion aware face swapping[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[9]NIRKIN Y,KELLER Y,HASSNER T.Fsgan:Subject agnostic face swapping and reenactment[C]//IEEE International Confe-rence on Computer Vision (ICCV).2019:7184-7193.
[10]LIU M,DING Y K,XIA M,et al.Stgan:A unified selectivetransfer network for arbitrary image attribute editing[C]//IEEE Conference on Computer Vision and Pattern Recognition.2019:3673-3682.
[11]THIES J,ZOLLHÖFER M,STAMMINGER M,et al.Face-2Face:Real-time face capture and reenactment of RGB videos[C]//IEEE Conference on Computer Vision and Pattern Recognition.2016:2387-2395.
[12]THIES J,ZOLLHÖFER M,NIEßNER M.Deferred neural rendering:Image synthesis using neural textures [J].ACM Tran-sactions on Graphics (TOG),2019,38(4):1-12.
[13]GONZALEZ-SOSA E,FIERREZ J,VERA-RODRIGUEZ R,et al.Facial soft biometrics for recognition in the wild:Recent works,annotation and COTS evaluation[J]. IEEE Transactions on Information Forensics and Security,2018,13(8):2001-2014.
[14]CHOI Y,CHOI M,KIM M,et al.StarGAN:Unified generative adversarial networks for multi-domain image-to-image translation[C]//IEEE/CVF Conference on Computer Vision and Pattern Recognition.2018.
[15]HE Z L,ZUO W M,KAN M,et al.AttGAN:Facial attributeediting by only changing what you want[J]. IEEE Transactions on Image Processing,2019,28(11):5464-5478.
[16]FakeApp[EB/OL].(2018-09-01)[2021-10-12].https://www.fakeapp.com.
[17]SUWAJANAKORN S,SEITZ S M,KEMELMACHER-SHLIZERMAN I.Synthesizing obama:learning lip sync from audio [J].ACM Transations on Grapgics(TOG),2017,36(4):95.1-95.13.
[18]LI Y,CHING M C,LYU S.In ictu oculi:Exposing ai generated fake face videos by detecting eye blinking[C]//2018 IEEE International Workshop on Information Forensics and Security (WIFS).IEEE,2018:1-7.
[19]FERNANDES S,RAJ S,ORTIZ E,et al.Predicting heart rate variations of deepfake videos using neural ode[C]//IEEE International Conference on Computer Vision Workshops.2019:1721-1729.
[20]YANG X,LI Y,LYU S.Exposing deep fakes using inconsistent head poses[C]//Proceedings of 2019 IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).IEEE,2019:8261-8265.
[21]AGARWAL S,FARIDL H.Protecting world leaders againstdeep fakes[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops.2019:38-45.
[22]YU N,DAVIS L,FRITZ M.Attributing Fake Images to GANs:Learning and analyzing GAN fingerprints[C]//IEEE International Conference on Computer Vision (ICCV).2019:7556-7566.
[23]MI Z J,JIANG X H,SUN T F,et al.Gan-generated image detection with self-attention mechanism against gan generator defect[J].IEEE Journal of Selected Topics in Signal Processing,2020.
[24]SUN X W,WU B T,CHEN W.Identifying invariant textureviolation for robust deepfake detection[J].arXiv:2012.10580,2020.
[25]CHOLLET F.Xception:Deep learning with depthwise separable convolutions[C]//Proceedings of IEEE Conference on Compu-ter Vision and Pattern Recognition.2017.
[26]NGUYEN H H,YAMAGISHI J,ECHIZEN I.Capsule-forensics:Using capsule networks to detect forged images and videos[C]//Proceedings of 2019 IEEE International Conference onAcoustics,Speech and Signal Processing (ICASSP).IEEE,2019:2307-2311.
[27]GUERA D,DELP J.Deepfake video detection using recurrent neural networks[C]//2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).IEEE,2018:1-6.
[28]SABIR E,CHENG J,JAISWAL A,et al.Recurrent convolu-tional strategies for face manipulation detection in videos[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops.2019:80-87.
[29]AMERINI I,GALTERI L,CALDELLI R,et al.Deepfake video detection through optical flow based CNN[C]//Proceedings of the IEEE International Conference on Computer Vision Workshops.2019:1205-1207.
[30]WODAJO D,ATNAFU S.Deepfake video detection using con-volutional vision transformer [J].arXiv:2102.11126,2021.
[31]KORSHUNOV P,MARCEL S.Deepfakes:a new threat to face recognition? assessment and detection[J].arXiv:1812.08685,2018.
[32]VidTIMIT Audio-Video Dataset[EB/OL]. [2021-10-12].http://conradsanderson.id.au/vidtimit.
[33]Faceswap-GAN[EB/OL].(2018-08-27) [2021-10-12].https://github.com/shaoanlu/faceswap-GAN.
[34]ROSSLER A,COZZOLINO D,VERDOLIVA L,et al.FaceForensics++:Learning to detect manipulated facial images[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).2019:1-11.
[35]LI X R,JI S L,WU C M,et al.Survey on deepfakes and detection Techniques [J].Journal of Software,2021,32(2):496-518.
[36]Google AI blog.Contributing data to deepfake detection re-search [EB/OL].[2021-10-21].https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html.
[37]LI Y,SUN P,QI H G,et al.Celeb-DF:A large-scale challenging dataset for deepfake forensics[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition.IEEE,2020.
[38]DOLHANSKY B,HOWES R,PFLAUM B,et al.The deepfake detection challenge (dfdc) preview dataset [J].arXiv:1910.08854,2019.
[39]DOLHANSKY B,BITTON J,PFLAUM B,et al.The DeepFake Detection Challenge (DFDC) dataset [J].arXiv:2006.07397,2020.
[40]ZI B J,CHANG M H,CHEN J J,et al.WildDeepfake:A challenging real-world dataset for deepfake detection[J].ACM MM,2020.
[41]JIANG L M,LI R,WU W,et al.Deeperforensics-1.0:A large-scale dataset for real-world face forgery detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020:2886-2895.
[42]ZHOU T F,WANG W G,LIANG Z Y,et al.Face forensics in the wild[C]//IEEE Conference on Computer Vision and Pattern Recognition.2021.
[43]PEROV I,GAO D H,CHERVONIY N,et al.DeepFaceLab:A simple,flexible and extensible face swapping framework[J].arXiv:2005.05535,2020.
[44]HE Y N,GAN B,CHEN S Y,et al.ForgeryNet:A versatilebenchmark for comprehensive forgery analysis[C]//IEEE Conference on Computer Vision and Pattern Recognition.2021.
[45]LE T N,NGUYEN H H,YAMAGISHI J,et al.OpenForensics:Large-Scale challenging dataset for multi-face forgery detection and segmentation in-the-wild [J].arXiv:2107.14480,2021.
[46]ZAKHAROV E,SHYSHEYA A,BURKOV E,et al.Few-shot adversarial learning of realistic neural talking head models[C]//IEEE International Conference on Computer Vision (ICCV).2019.
[47]POLYAK A,WOLF L,TAIGMAN Y.TTS skins:Speaker conversion via asr[J].arXiv:1904.08983,2019.
[48]CAO H W,COOPER D G,KEUTMANN M K,et al.Crema-d:Crowd-sourced emotional multimodal actors dataset[J].IEEE Transactions on Affective Computing,2014,5(4):377-390.
[49]LIVINGSTONE S R,RUSSO F A.The ryerson audio-visual da-tabase of emotional speech and song (RAVDESS):A dynamic,multimodal set of facial andvocal expressions in North American English[J].PLOSONE,2018,13(5):e0196391.
[50]CHUNG J S,NAGRANI A,ZISSERMAN A.Voxceleb2:Deep speaker recognition[J].arXiv:1806.05622,2018.
[51]EPHRAT A,MOSSERI I,LANG O,et al.Looking to listen at the cocktail party:A speaker-independent audio-visual model for speech separation[J].arXiv:1804.03619,2018.
[52]SIAROHIN A,LATHUILIÈRE S,TULYAKOV S,et al.First order motion model for image animation[C]//Neural Information Processing Systems (NeuralIPS'19).2019.
[53]KARRAS T,LAINE S,AITTALA M,et al.Analyzing and improving the image quality of stylegan[C]//IEEE/CVF Confe-rence on Computer Vision and Pattern Recognition.2020.
[54]CHEN L,MADDOX R K,DUAN Z Y,et al.Hierarchical cross-modal talking face generation with dynamic pixel-wise loss[C]//IEEE Conference on Computer Vision and Pattern Recognition.2019.
[55]CHOI Y,UH Y,YOO J,et al.Stargan v2:Diverse image synthesis for multiple domains[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020:188-197.
[56]FRIED O,TEWARI A,ZOLLHOFER M,et al.Text-based editing of talking-head video[J].ACM Transactions on Graphics (TOG),2019,38(4):1-14.
[57]DENG Y,YANG J L,CHEN D,et al.Disentangled and controllable face image generation via 3d imitative-contrastive learning[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[58]JO Y,PARK J.Sc-fegan:Face editing generative adversarial network with user's sketch and color[C]//ICCV.2019.
[59]LEE C H,LIU Z W,WU L Y,et al.Maskgan:Towards diverse and interactive facial image manipulation[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[60]KUZNETSOVA A,ROM H,ALLDRIN N,et al.The open images dataset v4:Unified image classification,object detection,and visual relationship detection at scale [J].International Journal of Computer Vision,2020.
[61]SHEN Y J,GU J J,TANG X O,et al.Interpreting the latent space of gans for semantic face editing[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[62]PIDHORSKYI S,ADJEROH D,DORETTO G.Adversarial latent autoencoders[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[63]NIRKIN Y,MASI I,TUAN A T,et al.On face segmentation,face swapping,and face perception[C]//2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018).IEEE,2018:98-105.
[64]BLANZ V,SCHERBAUM K,VETTER T,et al.Exchangingfaces in images[C]//EuroGraphics.2004.
[65]ZHANG H,CISSE M,DAUPHIN Y N,et al.Mixup:Beyond empirical risk minimization[C]//Proceedings of the Internatio-nal Conference on Learning Representations (ICLR).2018.
[66]YUN S,HAN D,OH S J,et al.CutMix:Regularization strategy to train strong classifiers with localization features [C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV).2019.
[67]CUBUK E D,ZOPH B,MAN D,et al.AutoAugment:Learning augmentation policies from data[C]//IEEE Conference on Computer Vision and Pattern Recognition.2019.
[68]CUBUK E D,ZOPH B,SHLENS J,et al.RandAugment:Practical automated data augmentation with a reduced search space [J].arXiv:1909.13719,2019.
[69]WANG S Y,WANG O,ZHANG R,et al.CNN-generated images are surprisingly easy to spot for now[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020:8692-8701.
[70]WANG J,WU Z X,CHEN J J,et al.M2TR:Multi-modal multi-scale transformers for deepfakedetection[J].arXiv:2104.09770,2021.
[71]LI Y Z,LYU S W.Exposing deepfake videos by detecting face warping artifacts[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops.2019:46-52.
[72]HULZEBOSCH N,IBRAHIMI S,WORRING M.DetectingCNN-generated facial images in real-world scenarios[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[73]COZZOLINO D,THIES J,RÖSSLER A,et al.ForensicTransfer:Weakly-supervised domain adaptation for forgery detection [J].arXiv:1812.02510,2018.
[74]XUAN X H,PENG B,WANG W,et al.On the generalization of GAN image forensics[C]//Proceedings of the Chinese Confe-rence on Biometric Recognition.2019:134-141.
[75]HE Y,YU N,KEUPER M,et al.Beyong the spectrum:Detecting deepfakes via reSynthesis[C]//International Joint Confe-rence on Artificial Intelligence(IJCAI).2021.
[76]JEON H,BANG Y,KIM J,et al.T-GD:Transferable GAN-ge-nerated images detection framework[C]//ICML.2020.
[77]GUO Z Q,YANG G B,CHEN J Y,et al.Fake face detection viaadaptive manipulation traces extraction network[J].arXiv:2005.04945,2020.
[78]LI Y Q,BAI T.Deepfake detection method in videos based on convolutional LSTM [J].Information Technology and Network Security,2021,40(40):28-32.
[79]DEVRIES T,TAYLOR G W.Improved Regularization of Con-volutional Neural Networks with Cutout [J].arXiv:1708.04552,2017.
[80]YANG C,LIM S N.One-shot domain adaptation for face gene-ration[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[81]BONDI L,CANNAS E D,BESTAGINI P,et al.Training strategies and data augmentations in CNN-based deepfake video detection [J].arXiv:2011.07792,2020.
[82]QIAN Y Y,YIN G J,SHENG L,et al.Thinking in frequency:Face forgery detection by mining frequency-aware clues[C]//Proceedings of the European Conference on Computer Vision (ECCV).2020.
[83]YU Y,NI R G,ZHAO Y.Mining Generalized Features for Detecting AI-Manipulated Fake Faces[J].arXiv:2010.14129,2020.
[84]CHEN Y P,FAN H Q,XU B,et al.Drop an octave:Reducing spatial redundancy in convolutional neural networks with octave convolution[C]//Proceedings of the IEEE International Confe-rence on Computer Vision (ICCV).2019.
[85]LIU H G,LI X D,ZHOU W B,et al.Spatial-Phase shallowlearning:Rethinking Face Forgery Detection in Frequency Domain[C]//IEEE Conference on Computer Vision and Pattern Recognition.2021.
[86]ZHOU P,HAN X T,MORARIU V I,et al.Two-stream neural networks for tampered face detection[C]//IEEE Conferenceon Computer Vision and Pattern Recognition Workshops.2017:1831-1839.
[87]AFCHAR D,NOZICK V,YAMAGISHI J,et al.Mesonet:acompact facial video forgery detection network[C]//IEEE International Workshop on Information Forensics and Security (WIFS'18).2018:1-7.
[88]MASI I,KILLEKAR A,MASCARENHAS R M,et al.Two-branch recurrent network for isolating deepfakes in videos[C]//16th European Conference on Computer Vision (ECCV).2020.
[89]NGUYEN H H,FANG F M,YAMAGISHI J,et al.Multi-task learning for detecting and segmenting manipulated facial images and videos[C]//Proceedings of the IEEE International Confe-rence on Biometrics:Theory,Applications and Systems (BTAS).2019.
[90]LI X D,LANG Y N,CHEN Y F,et al.Sharp multiple instance learning for deepfake video detection[C]//Proceedings of the 28th ACM International Conference on Multimedia.2020:1864-1872.
[91]KHODABAKHSH A,RAMACHANDRA A,RAJA A,et al.Fake face detection methods:Can they be generalized[C]//2018 International Conference of the Biometrics Special Interest Group.Darmstadt,Germany,2018:1-6.
[92]LIU Z Z,QI X J,TORR P.Global texture enhancement for fake face detection in the wild[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[93]ZHAO H Q,ZHOU W B,CHEN D D,et al.Multi-attentional deepfake detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.2021.
[94]LI L,BAO J,ZHANG T,et al.Face x-ray for more general face forgery detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.2020.
[95]HU Y J,GAO Y F,LIU B B,et al.Deepfake videos detectionbased on image segmentation with deep neural networks[J].Journal of Electronics & Information Technology,2021,43(1):162-170.
[96]HE K,ZHANG X,REN S,et al.Deep residual learning forimage recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[97]SCHROFF F,KALENICHENKO D,PHILBIN J.Facenet:Aunified embedding for face recognition and clustering[C]//IEEE Conference on Computer Vision and Pattern Recognition.2015:815-823.
[98]ZHAO T C,XU X,XU M Z,et al.Learning to recognize patchwise consistency for deepfake detection[J].arXiv:2012.09311,2020.
[99]SHANG Z H,XIE H T,ZHA Z J,et al.PRRNet:Pixel-Region relation network for face forgery detection [J].Pattern Recognition,2021,116:107950.
[100]YU C M,CHANG C T,TI Y W.Detecting deepfake-forged contents with separable convolutional neural network and image segmentation[J].arXiv:1912.12184,2019.
[101]CHEN Z H,YANG H.Attentive semantic exploring formani-pulated face detection[C]//IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).2021:1985-1989.
[102]DU M,PENTYALA S,LI Y N,et al.Towards generalizabledeepfake detection with locality-aware AutoEncoder[C]//Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM'20).2020.
[103]SUN Z,HAN Y J,HUA Z Y,et al.Improving the efficiency and robustness of deepfakes detection through precise geometric features[C]//IIEEE Conference on Computer Vision and Pattern Recognition.2021.
[104]CALDELLI R,GALTERI L,AMERINI I,et al.Optical flowbased CNN for detection of unlearnt deepfake manipulations [J].Pattern Recognition Letters,2021,146(10):31-37.
[105]GANIYUSUFOGLUA I,NGÔ L M,SAVOV N,et al.Spatio-temporal features for generalized detection of deepfake videos [C]//Computer Vision and Image Understanding.2020.
[106]TRINH L,TSANG M,RAMBHATLA S,et al.Interpretableand trust worthy deepfake detection via dynamic prototypes[C]//2021 IEEE Winter Conference on Applications of Computer Vision (WACV'21).2021.
[107]GU Z H,CHEN Y,YAO T P,et al.Spatiotemporal Inconsistency Learning for DeepFake Video Detection[J].arXiv:2109.01860,2021.
[108]HAN Y C,HUA G,ZHANG H J.Inception3D net based video RE forgery detection jointly exploiting eye and mouth areas[J].Journal of Signal Processing,2021,37(4):567-577.
[109]XING H,LI M.Deepfake Video Detection Based on 3D Convolutional Neural Networks [J].Computer Science,2021,48(7):86-92.
[110]KHAN S A,DAI H.Video Transformer for Deepfake Detection with Incremental Learning[C]//Proceedings of the 29th ACM International Conference on Multimedia(MM '21).ACM,2021.
[111]GUO J Z,ZHU X G,LEI Z.3DDFA[EB/OL].[2021-10-12].https://github.com/ cleardusk/3DDFA.
[112]GUO J Z,ZHU X G,YANG Y,et al.Towards fast,accurate and stable 3D dense face alignment[C]//Proceedings of the European Conference on Computer Vision (ECCV).2020.
[113]LI J M,XIE H T,LI J H,et al.Frequency-aware discriminative feature learning supervised by single-center loss for face for-gerydetection[C]//IEEE Conference on Computer Vision and Pattern Recognition.2021.
[114]CHEN S,YAO T P,CHEN Y,et al.Local Relation learning for face forgery detection[C]//AAAI.2021.
[115]LUO Y C,ZHANG Y,YAN J C,et al.Generalizing face forgery detection with high-frequency features[J].arXiv:2103.12376,2021.
[116]FRIDRICH J,KODOVSKY J.Rich models for steganalysis of digital images [J].IEEE Transactions on Information Forensics and Security,2012,7(3):868-882.
[117]CHINTHA A,RAO A,SOHRAWARDI S,et al.Leveraging edges and optical flow on faces for deepfake detection[C]//2020 IEEE International Joint Conference on Biometrics (IJCB).2020.
[118]WANG G X,ZHOU J H,WU Y.Exposing deep-faked videos by anomalous co-motion pattern detection [J].arXiv:2008.04848,2020.
[119]HALIASSOS A,VOUGIOUKAS K,PETRIDIS S,et al.LipsDon't Lie:A generalisable and robust approach to face forgery detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.2021.
[120]KIRKPATRICK J,PASCANU R,RABINOWITZ N,et al.Overcoming catastrophic forgetting in neural networks[J].PNAS,2017,114(13):3521-3526.
[121]ANEJA S,NIEßNER M.Generalized zero and few-shot transfer for facial forgery detection [J].arXiv:2006.11863,2020.
[122]TARIQ S,LEE S,WOO S S.A convolutional LSTM based residual network for deepfake video detection [J].arXiv:2009.07480,2020.
[123]TARIQ S,LEE S,WOO S S.One detector to rule them all:Towards a general deepfake attack detection framework[C]//Proceedingsof the Web Conference.2021.
[124]LEE S,TARIQ S,KIM J,et al.TAR:Generalized forensicframework to detect deepfakes using weakly supervised lear-ning[C]//IFIP-SEC.2021.
[125]TARIQ S,LEE S,KIM H,et al.Gan is a friend or foe?:a frame-work to detect various fake face images[C]//Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing.ACM,2019:1296-1303.
[126]SUN K,LIU H,YE Q X,et al.Domain general face forgery detection by learning to weight[C]//AAAI Conference on Artificial Intelligence.2021:2638-2646.
[127]MARRA F,SALTORI C,BOATO G,et al.Incremental learning for the detection andclassification of GAN-generated images [J].arXiv:1910.01568,2019.
[128]REBUFFI S A,KOLESNIKOV A,SPERL G,et al.iCaRL:Incremental classifier and representation learning[C]//IEEE Conference on Computer Vision and Pattern Recognition.2017:2001-2010.
[129]KIM M,TARIQ S,WOO S S.CoReD:Generalizing fake media detection with continual representation using distillation[C]//29th ACM International Conference on Multimedia (ACMMM '21).2021.
[130]DONG X Y,BAO J M,CHEN D D,et al.Identity-driven deepfake detection[J].arXiv:2012.03930,2020.
[131]HOWARD A G,ZHU M L,CHEN B,et al.Mobilenets:Effi-cient convolutional neural networks for mobile vision applications[J].arXiv:1704.04861,2017.
[132]TANAKA M,KIYA H.Fake-image detection with robust hashing[C]//2021 IEEE 3rd Global Conference on Life Sciences and Technologies (LifeTech).2021.
[133]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//NIPS.2017:5998-6008.
[134]MIAO C T,CHU Q,LI W H,et al.Towards generalizable and robust face manipulation detection via bag-of-local-feature[J].arXiv:2103.07915,2021.
[135]FUNG S,LU X Q,ZHANG C,et al.DeepfakeUCL:Deepfake detection via unsupervised contrastive learning[C]//The annual International Joint Conference on Neural Networks (IJCNN).2021.
[136]CHEN Q F,KOLTUN V.Photographic image synthesis withcascaded refinement networks[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV).2017.
[137]LI K,ZHANG T H,MALIK J.Diverse image synthesis from semantic layouts via conditional IMLE[C]//Proceedings of the IEEE International Conference on Computer Vision (ICCV).2019.
[138]CHEN C,CHEN Q F,XU J,et al.Learning to see in the dark[C]//IEEE Conference on Computer Vision and Pattern Recognition.2018.
[139]DAI T,CAI J R,ZHANG Y B,et al.Second-order attention net-work for single image super-resolution[C]//IEEE Conference on Computer Vision and Pattern Recognition.2019.
[140]GULRAJANI I,AHMED F,ARJOVSKY M,et al.Improvedtraining of wasserstein gans[C]//Neural Information Proces-sing Systems.2017:5767-5777.
[141]PUMAROLA A,AGUDO A,MARTINEZ A M,et al.Ganimation:Anatomically aware facial animation from a single image[C]//Proceedings of the European conference on computer vision (ECCV).2018:818-833.
[142]CHEN Y C,XU X G,TIAN Z T,et al.Homomorphic latentspace interpolation for unpaired image-to-image translation[C]//IEEE Conference on Computer Vision and Pattern Recognition.2019:2408-2416.
[143]ZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the inception architecture for computer vision[C]//IEEE Confe-rence on Computer Vision and Pattern Recognition.2016:2818-2826.
[144]IIZUKA S,SIMO-SERRA E,ISHIKAWA H.Globally and locally consistent image completion [J].ACM Transactions on Graphics (TOG),2017,36(4):107.1-107.14.
[145]YU J H,LIN Z,YANG J M,et al.Generative image inpainting with contextual attention[C]//IEEE Conference on Computer Vision and Pattern Recognition.2018.
[146]RUSSAKOVSKY O,DENG J,SU H,et al.ImageNet large scale visual recognition challenge[J].International Journal of Computer Vision,2015,115(3):211-252.
[147]KINGMA D P,DHARIWAL P.Glow:generative flow with invertible 1×1 convolutions[C]//NIPS.2018:10236-10245.
[148]LI X R,YU K.A Deepfakes detection technique based on two-stream network[J].Journal of Cyber Security,2020,5(2):84-91.
[149]CHEN T,KUMAR A,NAGARSHETH P,et al.Generalization of audio deepfake detection[C]//Proceedings of the Odyssey 2020 Speaker and Language Recognition Workshop.2020:132-137.
[150]JIA Z Y,FANG H,ZHANG W M.MBRS:Enhancing robustness of DNN-based watermarking by mini-batch of real and si-mulated JPEG compression[C]//29th ACM International Confe-rence on Multimedia.2021.
[151]BAO Y X,LU T L,DU Y H.Overview of Deepfake Video Detection Technology [J].Computer Science,2020,47(9):283-292.
[1] BAO Yu-xuan, LU Tian-liang, DU Yan-hui. Overview of Deepfake Video Detection Technology [J]. Computer Science, 2020, 47(9): 283-292.
[2] ZHANG Wang-ce, FAN Jing, WANG Bo-ru and NI Min. (α,k)-anonymized Model for Missing Data [J]. Computer Science, 2020, 47(6A): 395-399.
[3] CAO Min-zi, ZHANG Lin-lin, BI Xue-hua, ZHAO Kai. Personalized (α,l)-diversity k-anonymity Model for Privacy Preservation [J]. Computer Science, 2018, 45(11): 180-186.
[4] WANG Li-wen. Design and Implementation of Arbitrage Trading System Based on Generalization [J]. Computer Science, 2017, 44(Z6): 529-533.
[5] CAI Yi, ZHU Xiu-fang, SUN Zhang-li and CHEN A-jiao. Semi-supervised and Ensemble Learning:A Review [J]. Computer Science, 2017, 44(Z6): 7-13.
[6] MENG Xiao-long YANG Yan WANG Hong-jun XIAO Wen-chao. Occasion Determination of Clustering Ensemble [J]. Computer Science, 2015, 42(7): 48-51.
[7] XU Hua. Integration of Dual-layer Fuzzy System with Center-constrained Minimal Enclosing Ball [J]. Computer Science, 2014, 41(12): 172-175.
[8] SUN Chong, LU Yan-sheng. Clustering-based Algorithms to Semantic Summarizing the Table with Multi-attributes' Hierarchical Structures [J]. Computer Science, 2012, 39(3): 170-173.
[9] YU Xu,YANG Jing,XIE Zhi-qiang. Research on Virtual Sample Generation Technology [J]. Computer Science, 2011, 38(3): 16-19.
[10] . Cloud-based Architecture for Media Forensics [J]. Computer Science, 2011, 38(12): 28-30.
[11] LI Jun-huai,LIU Hai-ling,PING Jun,ZHANG Jing,CHEN Xiao-ming. Method of Association Rule Privacy Protection Based on Temporal Constraint [J]. Computer Science, 2009, 36(9): 201-204.
[12] LIU Jin-fu,YU Da-ren. Structural Risk Minimization for Controlling Generalization Performance of Rough Set Learning Machine [J]. Computer Science, 2009, 36(12): 210-213.
[13] . [J]. Computer Science, 2008, 35(3): 158-160.
[14] . [J]. Computer Science, 2008, 35(11): 137-138.
[15] . [J]. Computer Science, 2006, 33(2): 201-204.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!