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Emergence of deepfakes and video tampering detection approaches: A survey

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Abstract

Digital content, particularly the digital videos recorded at specific angle, though, provides a truthful picture of reality but the widespread proliferation of easy-to-use content editing softwares doubt about its authenticity. Recently, Artificial Intelligence (AI) based content altering mechanism, known as deepfake, became popular on social media platforms, wherein any person can be able to purport the behaviour of another person in a video who is actually not there. Depending on the type of manipulation performed, different types of deepfakes are described in this paper. Moreover, rely on digital content for trustworthy evidence as well as to avoid spread of misinformation, integrity and authenticity of digital content has-been of utmost concerns. This paper aims to present a survey of the state-of-art video integrity verification techniques with special emphasis on emerging deepfake video detection approaches. Seeing the advancement in creation of more realistic deepfake videos, this review facilitates the development of more generalized methods with a thorough discussion on different research trends in the wake of deepfake detection.

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Notes

  1. GAN: Generative Adversarial Network

  2. http://www.fakeapp.com/

  3. United States v Beeler, 62 F Supp. 2d 136 (July 1, 1999, United States District Court, D. Maine).

  4. Dolan v State of Florida, 743 S. 2d 544 (July 21, 1999, Court of Appeal of Florida, Fourth District).

  5. Defense Advanced Research Project Agency

  6. Coarse-to-Fine Deep Convolutional Neural Network

  7. Residual Network

  8. This dataset is available as a part of FaceForensics.

  9. DeepFake Detection Challenge

  10. Deep Neural Network

  11. https://www.ncbi.nlm.nih.gov/pubmed/9399231.

  12. http://bionumbers.hms.harvard.edu/bionumber.aspx?id=100706&ver=0.

  13. CNN: Convolutional Neural Network

  14. LRCN: Long-Term Recurrent CNN, a combination of CNN and LSTM

  15. LSTM: Long Short Term Memory

  16. VAE: Variational AutoEncoder

  17. Progressive Growing GAN

  18. https://github.com/EricGzq/Hybrid-Fake-Face-Dataset

  19. Gated Recurrent Unit

  20. Root Mean Square Energy

  21. Available at: https://www.descript.com/lyrebird-ai?source=lyrebird

    Table 15 Lip-sync Deepfake detection techniques (A: Accuracy, EER: Effective Error Rate)
    Full size table

  22. Available at: https://www.asvspoof.org/

  23. Available at: https://github.com/resemble-ai/Resemblyzer

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This Work is carried out at Design Innovation Center, Panjab University, Chandigarh, INDIA, established by the Ministry of Education, Government of India.

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    Staffy Kingra, Naveen Aggarwal & Nirmal Kaur

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Kingra, S., Aggarwal, N. & Kaur, N. Emergence of deepfakes and video tampering detection approaches: A survey. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-022-13100-x

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