[1] Tran L, Yin X, Liu X M. Representation learning by rotating your faces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41, 3007-3021(2019).
[2] Rössler A, Cozzolino D, Verdoliva L et al. FaceForensics: learning to detect manipulated facial images[C](2019).
[3] Shu K, Sliva A, Wang S H et al. Fake news detection on social media: a data mining perspective[J]. ACM SIGKDD Explorations Newsletter, 19, 22-36(2017).
[4] Tolosana R, Vera-Rodriguez R, Fierrez J et al. Deepfakes and beyond: a survey of face manipulation and fake detection[J]. Information Fusion, 64, 131-148(2020).
[5] Amerini I, Galteri L, Caldelli R et al. Deepfake video detection through optical flow based CNN[C], 1205-1207(2019).
[6] Agarwal S, Farid H, Gu Y et al. Protecting world leaders against deep fakes[C], 38-45(2019).
[7] Güera D, Delp E J. Deepfake video detection using recurrent neural networks[C](2018).
[9] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).
[10] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).
[11] Tan M, Le Q. Efficientnet: rethinking model scaling for convolutional neural networks[C], 6105-6114(2019).
[12] Zhou P, Han X T, Morariu V I et al. Learning rich features for image manipulation detection[C], 1053-1061(2018).
[13] Nguyen H H, Yamagishi J, Echizen I. Capsule-forensics: using capsule networks to detect forged images and videos[C], 2307-2311(2019).
[14] Afchar D, Nozick V, Yamagishi J et al. MesoNet: a compact facial video forgery detection network[C](2018).
[15] Geng P Z, Tang Y Q, Fan H X et al. Research on deep forgery detection based on CutMix algorithm and improved xception network[J]. Laser & Optoelectronics Progress, 59, 1615007(2022).
[16] Rahaman N, Baratin A, Arpit D et al. On the spectral bias of neural networks[C], 5301-5310(2019).
[17] Zhang X, Karaman S, Chang S F. Detecting and simulating artifacts in GAN fake images[C](2019).
[18] Zhao H Q, Wei T Y, Zhou W B et al. Multi-attentional deepfake detection[C], 2185-2194(2021).
[19] Mo H X, Chen B L, Luo W Q. Fake faces identification via convolutional neural network[C], 43-47(2018).
[20] Masi I, Killekar A, Mascarenhas R M et al. Two-branch recurrent network for isolating deepfakes in videos[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12352, 667-684(2020).
[21] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 3-19(2018).
[22] He Y H, Lin J, Liu Z J et al. AMC: AutoML for model compression and acceleration on mobile devices[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 815-832(2018).
[23] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).
[24] Young I T, van Vliet L J. Recursive implementation of the Gaussian filter[J]. Signal Processing, 44, 139-151(1995).
[25] Belkin M, Sun J, Wang Y S. Discrete Laplace operator on meshed surfaces[C], 278-287(2008).
[26] Li Y Z, Yang X, Sun P et al. Celeb-DF: a large-scale challenging dataset for DeepFake forensics[C], 3204-3213(2020).
[27] Yong H W, Huang J Q, Hua X S et al. Gradient centralization: a new optimization technique for deep neural networks[M]. Vedaldi A, Bischof H, Brox T, et al. Computer vision-ECCV 2020. Lecture notes in computer science, 12346, 635-652(2020).