Abstract
Deepfake is a machine learning and artificial intelligence-based technique which is used to generate fake faces and replaces them in a video or image. These days deepfakes are mostly used to spread fake news, audio, video, etc. Deepfakes are also used in political campaigns to manipulate the videos of leaders and spread hatred. Today, it has become very easy to generate deepfake images as there are various commercial softwares available for generating deepfakes; also, there are various free of cost apps available like faceapp, fakeapp, etc. The website thispersondoesnotexist.com generates a fake person image that does not exist every time you click it. The automation in video manipulation generates more threat to original content as it is becoming more and more easier to manipulate images and generate fake news. It can be very dangerous in the upcoming time to detect what is fake and what is real. The main component which makes deepfakes more and more real is general adversarial networks (GANs); by using GAN, we can generate high-quality deepfakes which cannot be detected by the human eye. There are various techniques generated to detect deepfakes, but to the best of our knowledge, we can say that there is no foolproof method to detect deepfakes, and there is a strong need for a technique which can prevent current facial recognition systems from deepfakes. In this paper, we try to give a brief review of existing deepfake detection techniques. There are many techniques developed in this area but most of them can be categorized in facial artifacts, neural networks, 3D head position.
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References
Matern F, Riess C, Stamminger M (2019) Exploiting visual artifacts to expose deepfakes and face manipulations. In: 2019 IEEE Winter applications of computer vision workshops (WACVW). IEEE, pp 83–92
Ding X, Raziei Z, Larson EC, Olinick EV, Krueger P, Hahsler M (2020) Swapped face detection using deep learning and subjective assessment. EURASIP J Inf Secur 2020:1–12
Nightingale SJ, Wade KA, Watson DG (2017) Can people identify original and manipulated photos of real-world scenes? Cogn Res Principles Implications 2(1):30
Schetinger V, Oliveira MM, da Silva R, Carvalho TJ (2017) Humans are easily fooled by digital images. Comput Graph 68:142–151
Wang Z, She Q, Ward TE (2020) Generative adversarial networks in computer vision: a survey and taxonomy. arXiv preprint arXiv:1906.01529
Li Y, Yang X, Sun P, Qi H, Lyu S (2020) Celeb-df: A large-scale challenging dataset for deepfake forensics. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 3207–3216
Rossler A, Cozzolino D, Verdoliva L, Riess C, Thies J, Nießner M (2019) Faceforensics++: Learning to detect manipulated facial images. In: Proceedings of the IEEE international conference on computer vision, pp 1–11
Thies J, Zollhöfer M, Nießner M (2019) Deferred neural rendering: image synthesis using neural textures. ACM Trans Graph (TOG) 38(4):1–12
Nirkin Y, Wolf L, Keller Y, Hassner T (2020) Deepfake detection based on the discrepancy between the face and its context. arXiv preprint arXiv:2008.12262
Sabir E, Cheng J, Jaiswal A, AbdAlmageed W, Masi I, Natarajan P (2019) Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI) 3(1)
Chang X, Wu J, Yang T, Feng G (2020) Deepfake face image detection based on improved vgg convolutional neural network. In: 2020 39th Chinese control conference (CCC). IEEE, pp 7252–7256
Afchar D, Nozick V, Yamagishi J, Echizen, I.: Mesonet: a compact facial video forgery detection network. In: 2018 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 1–7
Güera D, Delp EJ (2018) Deepfake video detection using recurrent neural networks. In: 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS). IEEE, pp 1–6
Montserrat DM, Hao H, Yarlagadda SK, Baireddy S, Shao R, Horváth J, Bartusiak E, Yang J, Güera D, Zhu F et al (2020) Deepfakes detection with automatic face weighting. arXiv preprint arXiv:2004.12027
Zhang W, Zhao C, Li Y (2020) A novel counterfeit feature extraction technique for exposing face-swap images based on deep learning and error level analysis. Entropy 22(2):249
Wu X, Xie Z, Gao Y, Xiao Y (2020): Sstnet: Detecting manipulated faces through spatial, steganalysis and temporal features. In: ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2952–2956
Masi I, Killekar A, Mascarenhas,RM, Gurudatt SP, AbdAlmageed W (2020) Two-branch recurrent network for isolating deepfakes in videos. In: European conference on computer vision. Springer, pp 667–684
Milborrow S, Morkel J, Nicolls F (2010) The MUCT landmarked face database. Pattern Recognition Association of South Africa. http://www.milbo.org/muct
Laptev I, Marszalek M, Schmid C, Rozenfeld B (2008) Learning realistic human actions from movies. In: 2008 IEEE conference on computer vision and pattern recognition. IEEE, pp 1–8
Kingma DP, Dhariwal P (2018) Glow: generative flow with invertible 1x1 convolutions. arXiv preprint arXiv:1807.03039
Karras T, Aila T, Laine S, Lehtinen J (2017) Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196
Dolhansky B, Howes R, Pflaum B, Baram N, Ferrer CC (2019) The deepfake detection challenge (dfdc) preview dataset. arXiv preprint arXiv:1910.08854
Tariq S, Lee S, Woo SS (2020) A convolutional lstm based residual network for deepfake video detection. arXiv preprint arXiv:2009.07480
Bennett SL, Goubran R, Knoefel F (2016) Adaptive eulerian video magnification methods to extract heart rate from thermal video. In: 2016 IEEE international symposium on medical measurements and applications (MeMeA). IEEE, pp 1–5
Balakrishnan G, Durand F, Guttag J (2013) Detecting pulse from head motions in video. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3430–3437
Ciftci UA, Demir I, Yin L (2020) Fakecatcher: detection of synthetic portrait videos using biological signals. IEEE Trans Pattern Anal Mach Intell
Fernandes S, Raj S, Ortiz E, Vintila I, Salter M, Urosevic G, Jha S (2019) Predicting heart rate variations of deepfake videos using neural ode. In: Proceedings of the IEEE international conference on computer vision workshops, p 0
Chen RT, Rubanova Y, Bettencourt J, Duvenaud DK (2018) Neural ordinary differential equations. In: Advances in neural information processing systems, pp 6571–6583
Yang X, Li Y, Lyu S (2019) Exposing deep fakes using inconsistent head poses. In: ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 8261–8265
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Rani, R., Kumar, T., Sah, M.P. (2022). A Review on Deepfake Media Detection. In: Sharma, H., Shrivastava, V., Kumari Bharti, K., Wang, L. (eds) Communication and Intelligent Systems . Lecture Notes in Networks and Systems, vol 461. Springer, Singapore. https://doi.org/10.1007/978-981-19-2130-8_28
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