Finding Facial Forgery Artifacts with Parts-Based Detectors
暂无分享,去创建一个
[1] Daiheng Gao,et al. DeepFaceLab: A simple, flexible and extensible face swapping framework , 2020, ArXiv.
[2] David Bau,et al. What makes fake images detectable? Understanding properties that generalize , 2020, ECCV.
[3] Hany Farid,et al. Exposing digital forgeries by detecting traces of resampling , 2005, IEEE Transactions on Signal Processing.
[4] Fernando De la Torre,et al. Facial Action Transfer with Personalized Bilinear Regression , 2012, ECCV.
[5] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[6] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Siwei Lyu,et al. Exposing DeepFake Videos By Detecting Face Warping Artifacts , 2018, CVPR Workshops.
[8] Davis E. King,et al. Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..
[9] Junichi Yamagishi,et al. MesoNet: a Compact Facial Video Forgery Detection Network , 2018, 2018 IEEE International Workshop on Information Forensics and Security (WIFS).
[10] Weihong Wang,et al. Exposing digital forgeries in video by detecting duplication , 2007, MM&Sec.
[11] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[12] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[13] Larry S. Davis,et al. Two-Stream Neural Networks for Tampered Face Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[14] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.
[15] Bolin Chen,et al. Fake Faces Identification via Convolutional Neural Network , 2018, IH&MMSec.
[16] Sébastien Marcel,et al. DeepFakes: a New Threat to Face Recognition? Assessment and Detection , 2018, ArXiv.
[17] Alin C. Popescu,et al. Exposing digital forgeries in color filter array interpolated images , 2005, IEEE Transactions on Signal Processing.
[18] Jian Yang,et al. DSFD: Dual Shot Face Detector , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Davide Cozzolino,et al. Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection , 2017, IH&MMSec.
[20] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[21] Andrew Owens,et al. Fighting Fake News: Image Splice Detection via Learned Self-Consistency , 2018, ECCV.
[22] Andreas Rössler,et al. FaceForensics++: Learning to Detect Manipulated Facial Images , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[23] Belhassen Bayar,et al. A Deep Learning Approach to Universal Image Manipulation Detection Using a New Convolutional Layer , 2016, IH&MMSec.
[24] Justus Thies,et al. Face2Face: real-time face capture and reenactment of RGB videos , 2019, Commun. ACM.
[25] Junichi Yamagishi,et al. Distinguishing computer graphics from natural images using convolution neural networks , 2017, 2017 IEEE Workshop on Information Forensics and Security (WIFS).
[26] Timo Aila,et al. A Style-Based Generator Architecture for Generative Adversarial Networks , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Justus Thies,et al. Deferred Neural Rendering: Image Synthesis using Neural Textures , 2019 .
[28] Alberto Del Bimbo,et al. Deepfake Video Detection through Optical Flow Based CNN , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[29] Fang Wen,et al. Face X-Ray for More General Face Forgery Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Celeb-DF: A Large-Scale Challenging Dataset for DeepFake Forensics , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] V. Lempitsky,et al. Few-Shot Adversarial Learning of Realistic Neural Talking Head Models , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[32] Tal Hassner,et al. FSGAN: Subject Agnostic Face Swapping and Reenactment , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).