Skip to main content

Impact of Media Forensics and Deepfake in Society

  • Chapter
  • First Online:
Breakthroughs in Digital Biometrics and Forensics

Abstract

Technologies for producing and modifying content for multimedia have reached to the point that they could now deliver an extremely high degree of realism. Free software tools accessible on the Internet enable anyone with no special expertise to generate incredibly convincing fake images and videos. These technologies provide plenty of new opportunities in various sectors such as fine arts, marketing, film-making, and video games. They could also be employed to influence thoughts of the society in election campaigns, attempt crime, blackmail, or defame individuals. Thus, automated and effective methods of identifying fake media content are urgently needed. This chapter aims to provide an assessment of approaches for validating multimedia integrity as well as detecting modified images and videos. Deepfakes generated using deep learning (DL) techniques will be highlighted particularly, as well as recent data-driven forensics strategies to counter them. The results emphasize the shortcomings of present forensics techniques, the most crucial concerns, emerging obstacles, and future research possibilities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Cheapfakes rely on low-cost, easily accessible software or no software at all. They employ standard methods such as slowing, speeding, re-staging, editing, and re-contextualizing footage [39].

References

  1. S. Agarwal, H. Farid, Y. Gu, M. He, K. Nagano, H. Li, Protecting world leaders against deep fakes, in CVPR Workshops, vol. 1 (2019)

    Google Scholar 

  2. C. Barnes, E. Shechtman, A. Finkelstein, D.B. Goldman, PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 24 (2009)

    Google Scholar 

  3. M. Barni, A. Costanzo, E. Nowroozi, B. Tondi, CNN-based detection of generic contrast adjustment with JPEG post-processing, in 2018 25th IEEE International Conference on Image Processing (ICIP) (2018), pp. 3803–3807. https://doi.org/10.1109/ICIP.2018.8451698

  4. M. Barni, L. Bondi, N. Bonettini, P. Bestagini, A. Costanzo, M. Maggini, B. Tondi, S. Tubaro, Aligned and non-aligned double JPEG detection using convolutional neural networks. J. Vis. Commun. Image Represent. 49, 153–163 (2017)

    Article  Google Scholar 

  5. M. Barni, K. Kallas, E. Nowroozi, B. Tondi, CNN detection of GAN-generated face images based on cross-band co-occurrences analysis, in 2020 IEEE International Workshop on Information Forensics and Security (WIFS) (IEEE, Piscataway, 2020), pp. 1–6

    Google Scholar 

  6. M. Barni, E. Nowroozi, B. Tondi, Higher-order, adversary-aware, double JPEG-detection via selected training on attacked samples, in 2017 25th European Signal Processing Conference (EUSIPCO) (2017), pp. 281–285. https://doi.org/10.23919/EUSIPCO.2017.8081213

  7. M. Barni, E. Nowroozi, B. Tondi, Detection of adaptive histogram equalization robust against JPEG compression, in 2018 International Workshop on Biometrics and Forensics (IWBF) (2018), pp. 1–8

    Google Scholar 

  8. M. Barni, E. Nowroozi, B. Tondi, Improving the security of image manipulation detection through one-and-a-half-class multiple classification. Multimedia Tools Appl. 79(3), 2383–2408 (2020)

    Article  Google Scholar 

  9. B. Biggio, I. Corona, Z.M. He, P.P. Chan, G. Giacinto, D.S. Yeung, F. Roli, One-and-a-half-class multiple classifier systems for secure learning against evasion attacks at test time, in International Workshop on Multiple Classifier Systems (Springer, Berlin, 2015), pp. 168–180

    Google Scholar 

  10. B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, F. Roli, Evasion attacks against machine learning at test time, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Springer, Berlin, 2013), pp. 387–402

    Google Scholar 

  11. B. Biggio, G. Fumera, F. Roli, Security evaluation of pattern classifiers under attack. IEEE Trans. Knowl. Data Eng. 26(4), 984–996 (2014)

    Article  Google Scholar 

  12. M. Conti, S. Milani, E. Nowroozi, G. Orazi, Do not deceive your employer with a virtual background: a video conferencing manipulation-detection system. CoRR abs/2106.15130 (2021). https://arxiv.org/abs/2106.15130

  13. D. Cozzolino, L. Verdoliva, Single-image splicing localization through autoencoder-based anomaly detection, in 2016 IEEE International Workshop on Information Forensics and Security (WIFS) (IEEE, Piscataway, 2016), pp. 1–6

    Google Scholar 

  14. Dataset for Real and Virtual Backgrounds of Video Calls. https://zenodo.org/record/5572910. Accessed 28 Mar 2022

  15. T.J. De Carvalho, C. Riess, E. Angelopoulou, H. Pedrini, A. de Rezende Rocha, Exposing digital image forgeries by illumination color classification. IEEE Trans. Inform. Forensics Secur. 8(7), 1182–1194 (2013)

    Article  Google Scholar 

  16. Z. Dias, A. Rocha, S. Goldenstein, Video phylogeny: recovering near-duplicate video relationships, in 2011 IEEE International Workshop on Information Forensics and Security (IEEE, Piscataway, 2011), pp. 1–6

    Google Scholar 

  17. A. Esteva, K. Chou, S. Yeung, N. Naik, A. Madani, A. Mottaghi, Y. Liu, E. Topol, J. Dean, R. Socher, Deep learning-enabled medical computer vision. NPJ Digit. Med. 4(1), 1–9 (2021)

    Article  Google Scholar 

  18. H. Farid, Image forgery detection. IEEE Signal Process. Mag. 26(2), 16–25 (2009)

    Article  Google Scholar 

  19. H. Farid, Photo Forensics (MIT Press, Cambridge, MA, 2016)

    Book  Google Scholar 

  20. A. Ferreira, E. Nowroozi, M. Barni, VIPPrint: validating synthetic image detection and source linking methods on a large scale dataset of printed documents. J. Imaging 7(3), 50 (2021)

    Google Scholar 

  21. M. Fontani, T. Bianchi, A. De Rosa, A. Piva, M. Barni, A framework for decision fusion in image forensics based on Dempster–Shafer theory of evidence. IEEE Trans. Inform. Forensics Secur. 8(4), 593–607 (2013)

    Article  Google Scholar 

  22. T. Gloe, R. Böhme, The’dresden image database’ for benchmarking digital image forensics, in Proceedings of the 2010 ACM Symposium on Applied Computing (2010), pp. 1584–1590

    Google Scholar 

  23. T. Gloe, M. Kirchner, A. Winkler, R. Böhme, Can we trust digital image forensics? in Proceedings of the 15th ACM International Conference on Multimedia (2007), pp. 78–86

    Google Scholar 

  24. D. Güera, S. Baireddy, P. Bestagini, S. Tubaro, E.J. Delp, We need no pixels: video manipulation detection using stream descriptors (2019). arXiv preprint arXiv:1906.08743

    Google Scholar 

  25. H. Huang, P.S. Yu, C. Wang, An introduction to image synthesis with generative adversarial nets (2018). arXiv preprint arXiv:1803.04469

    Google Scholar 

  26. M.K. Johnson, H. Farid, Exposing digital forgeries in complex lighting environments. IEEE Trans. Inform. Forensics Secur. 2(3), 450–461 (2007)

    Article  Google Scholar 

  27. T. Karras, T. Aila, S. Laine, J. Lehtinen, Progressive growing of GANs for improved quality, stability, and variation (2017). arXiv preprint arXiv:1710.10196

    Google Scholar 

  28. E. Kee, J.F. O’Brien, H. Farid, Exposing photo manipulation with inconsistent shadows. ACM Trans. Graph. 32(3), 1–12 (2013)

    Article  Google Scholar 

  29. L. Kennedy, S.F. Chang, Internet image archaeology: automatically tracing the manipulation history of photographs on the web, in Proceedings of the 16th ACM international conference on Multimedia (2008), pp. 349–358

    Google Scholar 

  30. P. Korshunov, S. Marcel, Deepfakes: a new threat to face recognition? Assessment and detection (2018). arXiv preprint arXiv:1812.08685

    Google Scholar 

  31. H. Li, B. Li, S. Tan, J. Huang, Identification of deep network generated images using disparities in color components. Signal Process. 174, 107616 (2020)

    Article  Google Scholar 

  32. L. Li, J. Bao, T. Zhang, H. Yang, D. Chen, F. Wen, B. Guo, Face x-ray for more general face forgery detection, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020), pp. 5001–5010

    Google Scholar 

  33. Y. Li, S. Lyu, Exposing deepfake videos by detecting face warping artifacts (2018). arXiv preprint arXiv:1811.00656

    Google Scholar 

  34. J. Lukáš, J. Fridrich, M. Goljan, Detecting digital image forgeries using sensor pattern noise, in Security, Steganography, and Watermarking of Multimedia Contents VIII, vol. 6072 (SPIE, 2006), pp. 362–372

    Google Scholar 

  35. G. Mahfoudi, B. Tajini, F. Retraint, F. Morain-Nicolier, J.L. Dugelay, P. Marc, DEFACTO: image and face manipulation dataset, in 2019 27th European Signal Processing Conference (EUSIPCO) (IEEE, Piscataway, 2019), pp. 1–5

    Google Scholar 

  36. E. Nowroozi, A. Dehghantanha, R.M. Parizi, K.K.R. Choo, A survey of machine learning techniques in adversarial image forensics. Comput. Secur. 100, 102092 (2021)

    Article  Google Scholar 

  37. E. Nowroozi, Y. Mekdad, M. Conti, S. Milani, S. Uluagac, B. Yanikoglu, Real or virtual: a video conferencing background manipulation-detection system (2022). https://arxiv.org/abs/2204.11853

  38. E. Nowroozi, A. Zakerolhosseini, Double jpeg compression detection using statistical analysis. Adv. Comput. Sci. Int. J. 4(3), 70–76 (2015)

    Google Scholar 

  39. B. Paris, J. Donovan, Deepfakes and cheap fakes (2019)

    Google Scholar 

  40. J. Park, D. Cho, W. Ahn, H.K. Lee, Double jpeg detection in mixed JPEG quality factors using deep convolutional neural network, in Proceedings of the European Conference on Computer Vision (ECCV) (2018), pp. 636–652

    Google Scholar 

  41. A. Piva, An overview on image forensics. Int. Scholarly Res. Not. 2013 (2013)

    Google Scholar 

  42. Reuters: Fact check-doctored video appears to show Putin announcing peace (2022). https://www.reuters.com/article/factcheck-putin-address-idUSL2N2VK1CC. Accessed 23 Mar 2022

  43. A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess, J. Thies, M. Nießner, Faceforensics+ +: learning to detect manipulated facial images, in Proceedings of the IEEE/CVF International Conference on Computer Vision (2019), pp. 1–11

    Google Scholar 

  44. E. Sabir, J. Cheng, A. Jaiswal, W. AbdAlmageed, I. Masi, P. Natarajan, Recurrent convolutional strategies for face manipulation detection in videos. Interfaces (GUI) 3(1), 80–87 (2019)

    Google Scholar 

  45. D. Shullani, M. Fontani, M. Iuliani, O. Al Shaya, A. Piva, Vision: a video and image dataset for source identification. EURASIP J. Inform. Secur. 2017(1), 1–16 (2017)

    Google Scholar 

  46. P. Singh, H. Farid, Robust homomorphic image hashing, in CVPR Workshops (2019), pp. 11–18

    Google Scholar 

  47. A. Swaminathan, M. Wu, K.R. Liu, Digital image forensics via intrinsic fingerprints. IEEE Trans. Inform. Forensics Secur. 3(1), 101–117 (2008)

    Article  Google Scholar 

  48. C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, R. Fergus, Intriguing properties of neural networks (2013). arXiv preprint arXiv:1312.6199

    Google Scholar 

  49. L. Verdoliva, Media forensics and deepfakes: an overview. IEEE J. Sel. Topics Signal Process. 14(5), 910–932 (2020)

    Article  Google Scholar 

  50. A. Voulodimos, N. Doulamis, A. Doulamis, E. Protopapadakis, Deep learning for computer vision: a brief review. Comput. Intell. Neurosc. 2018 (2018)

    Google Scholar 

  51. Q. Wang, R. Zhang, Double jpeg compression forensics based on a convolutional neural network. EURASIP J. Inform. Secur. 2016(1), 1–12 (2016)

    Article  Google Scholar 

  52. X. Wu, A.G. Hauptmann, C.W. Ngo, Practical elimination of near-duplicates from web video search, in Proceedings of the 15th ACM International Conference on Multimedia (2007), pp. 218–227

    Google Scholar 

  53. X. Yang, Y. Li, H. Qi, S. Lyu, Exposing GAN-synthesized faces using landmark locations, in Proceedings of the ACM Workshop on Information Hiding and Multimedia Security (2019), pp. 113–118

    Google Scholar 

  54. P. Yu, Z. Xia, J. Fei, Y. Lu, A survey on deepfake video detection. IET Biom. 10(6), 607–624 (2021)

    Article  Google Scholar 

  55. P. Zhou, B.C. Chen, X. Han, M. Najibi, A. Shrivastava, S.N. Lim, L. Davis, Generate, segment, and refine: towards generic manipulation segmentation, in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34 (2020), pp. 13058–13065

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ehsan Nowroozi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nowroozi, E., Seyedshoari, S., Mohammadi, M., Jolfaei, A. (2022). Impact of Media Forensics and Deepfake in Society. In: Daimi, K., Francia III, G., Encinas, L.H. (eds) Breakthroughs in Digital Biometrics and Forensics. Springer, Cham. https://doi.org/10.1007/978-3-031-10706-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-10706-1_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-10705-4

  • Online ISBN: 978-3-031-10706-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics