[1] FaceBook. Deepfake detection challenge(DFDC)[EB/OL]. https://www.kaggle.com/c/deepfake-detection-challenge

[2] ChallengeDeeperForensics. 2020.@ECCV SenseHuman Workshop[EB/OL]. https://competitions.codalab.org/competitions/25228

[4] Peng B. DeepFake Game Competition (DFGC)@IJCB 2021[EB/OL]. http://dfgc2021.iapr-tc4.org/

[6] House-Energy and Commerce. Deep falsified report act of 2019[EB/OL]. https://www.congress.gov/bill/116th-congress/house-bill/3600/

[7] Agarwal S, Farid H, Gu Y et al. Protecting world leaders against deep fakes[C], 38-45(2020).

[8] Amerini I, Galteri L, Caldelli R et al. Deepfake video detection through optical flow based CNN[C], 1205-1207(2019).

[9] Güera D, Delp E J. Deepfake video detection using recurrent neural networks[C], 18455926(2018).

[10] Li Y Z, Chang M C, Lyu S W. In Ictu oculi: exposing AI created fake videos by detecting eye blinking[C], 18431664(2018).

[11] Matern F, Riess C, Stamminger M. Exploiting visual artifacts to expose deepfakes and face manipulations[C], 83-92(2019).

[12] Yang X, Li Y Z, Lyu S W. Exposing deep fakes using inconsistent head poses[C], 8261-8265(2019).

[13] Nataraj L, Mohammed T M, Manjunath B S et al. Detecting GAN generated fake images using co-occurrence matrices[J]. Electronic Imaging, 31, 532(2019).

[14] Li H D, Li B, Tan S Q et al. Identification of deep network generated images using disparities in color components[J]. Signal Processing, 174, 107616(2020).

[15] Xuan X S, Peng B, Wang W et al. On the generalization of GAN image forensics[EB/OL]. https://arxiv.org/abs/1902.11153

[16] Afchar D, Nozick V, Yamagishi J et al. MesoNet: a compact facial video forgery detection network[C], 18439519(2018).

[17] Nguyen H H, Yamagishi J, Echizen I. Capsule-forensics: using capsule networks to detect forged images and videos[C], 2307-2311(2019).

[18] Tan M X, Le Q V. EfficientNet: rethinking model scaling for convolutional neural networks[EB/OL]. https://arxiv.org/abs/1905.11946

[19] Dang H, Liu F, Stehouwer J et al. On the detection of digital face manipulation[C], 5780-5789(2020).

[20] Rössler A, Cozzolino D, Verdoliva L et al. FaceForensics++: learning to detect manipulated facial images[C], 19398261(2019).

[21] Chen D G, Yin P B et al. Research on identification of wild mushroom species based on improved Xception transfer learning[J]. Laser & Optoelectronics Progress, 58, 0810023(2021).

[22] Li X R, Yu K. A Deepfakes detection technique based on two-stream network[J]. Journal of Cyber Security, 5, 84-91(2020).

[23] Geng P Z, Fan H X, Zhang Y Y et al. Deep forgery detection method based on tampering artifacts[J]. Computer Engineering, 47, 156-162(2021).

[24] Zhou T, Lü X Q, Ren G Y et al. Facial expression classification based on ensemble convolutional neural network[J]. Laser & Optoelectronics Progress, 57, 141501(2020).

[25] Yun S, Han D, Chun S et al. CutMix: regularization strategy to train strong classifiers with localizable features[C], 6022-6031(2019).

[26] Wang K X, Li Z R, Wang X B et al. Automated classification method for crime scene sketches[J]. Laser & Optoelectronics Progress, 57, 041009(2020).

[27] Zhang K P, Zhang Z P, Li Z F et al. Joint face detection and alignment using multitask cascaded convolutional networks[J]. IEEE Signal Processing Letters, 23, 1499-1503(2016).

[28] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[29] Zhang H Y, Cisse M, Dauphin Y N et al. Mixup: beyond empirical risk minimization[EB/OL]. https://arxiv.org/abs/1710.09412