Skip to main content
Springer Nature Link
Log in

A Machine Learning Model to Detect Fake Voice

  • Conference paper
  • First Online:
Applied Informatics (ICAI 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1277))

Included in the following conference series:

Abstract

Nowadays, there are different digital tools that permit the editing of digital content as audio files and they are easily accessed in mobile devices and personal computers. Audio forgery detection has been one of the main topics in the forensics field, as it is necessary to have reliable evidence in court. These audio recordings that are used as digital evidence may be forged and methods that are able to detect if they have been forged are required as new ways of generation of fake content continue growing. One method to generate fake content is imitation, in which a speaker can imitate another, using signal processing techniques. In this work, a passive forgery detection approach is proposed by manually extracting the entropy features of original and forged audios created using an imitation method and then using a machine learning model with logistic regression to classify the audio recordings. The results showed an accuracy of 0.98 where all forged audios were successfully detected.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
€34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Germany)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 85.59
Price includes VAT (Germany)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 106.99
Price includes VAT (Germany)
  • Compact, lightweight 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

') var buybox = document.querySelector("[data-id=id_"+ timestamp +"]").parentNode var buyingOptions = buybox.querySelectorAll(".buying-option") ;[].slice.call(buyingOptions).forEach(initCollapsibles) var buyboxMaxSingleColumnWidth = 480 function initCollapsibles(subscription, index) { var toggle = subscription.querySelector(".buying-option-price") subscription.classList.remove("expanded") var form = subscription.querySelector(".buying-option-form") var priceInfo = subscription.querySelector(".price-info") var buyingOption = toggle.parentElement if (toggle && form && priceInfo) { toggle.setAttribute("role", "button") toggle.setAttribute("tabindex", "0") toggle.addEventListener("click", function (event) { var expandedBuyingOptions = buybox.querySelectorAll(".buying-option.expanded") var buyboxWidth = buybox.offsetWidth ;[].slice.call(expandedBuyingOptions).forEach(function(option) { if (buyboxWidth buyboxMaxSingleColumnWidth) { toggle.click() } else { if (index === 0) { toggle.click() } else { toggle.setAttribute("aria-expanded", "false") form.hidden = "hidden" priceInfo.hidden = "hidden" } } }) } initialStateOpen() if (window.buyboxInitialised) return window.buyboxInitialised = true initKeyControls() })()

Institutional subscriptions

Similar content being viewed by others

References

  1. Digital evidence|NIST. https://www.nist.gov/topics/digital-evidence. Accessed 05 Nov 2019

  2. A Simplified Guide To Forensic Audio and Video Analysis. http://www.forensicsciencesimplified.org/av/AudioVideo.pdf. Accessed 28 Aug 2019

  3. ANSI National Accreditation Board|ANAB. https://www.anab.org/. Accessed 26 Aug 2019

  4. Teerakanok, S., Uehara, T.: Digital media tampering detection techniques: an overview. In: Proceedings of IEEE 41st Annual Computer Software and Applications Conference, pp. 170–174. https://doi.org/10.1109/COMPSAC.2017.109, https://doi.org/10.1109/COMPSAC.2017.109

  5. Imran, M., Ali, Z., Bakhsh, S.T., Akram, S.: Blind detection of copy-move forgery in digital audio forensics. IEEE Access, 12843–12855. https://doi.org/10.1109/ACCESS.2017.2717842

  6. Ali, Z., Imran, M., Alsulaiman, M.: An automatic digital audio authentication/forensics system. IEEE Access, 2994–3007. https://doi.org/10.1109/ACCESS.2017.2672681

  7. Chen, J., Xiang, S., Huang, H., Liu, W.: Detecting and locating digital audio forgeries based on singularity analysis with wavelet packet. Multimedia Tools Appl. 75(4), 2303–2325 (2014). https://doi.org/10.1007/s11042-014-2406-3

    Article  Google Scholar 

  8. Ballesteros, D.M., Moreno, J.M.: Highly transparent steganography model of speech signals using Efficient Wavelet Masking. Expert Syst. Appl. 39(10), 9141–9149. https://doi.org/10.1016/j.eswa.2012.02.066

  9. Goodfellow, I.J., et al.: Generative adversarial networks, pp. 1–9. https://arxiv.org/abs/1406.2661

  10. Audio Generation with GANs - Neuronio - Medium. https://medium.com/neuronio/audio-generation-with-gans-428bc2de5a89. Accessed 16 Sept 2019

  11. Ning, Y., He, S., Wu, Z., Xing, C., Zhang, L.-J.: Review of deep learning based speech synthesis. Appl. Sci. 9(19), 1–16. https://doi.org/10.3390/app9194050

  12. NBA Warif et al.: Copy-move forgery detection: survey, challenges and future directions. J. Netw. Comput. Appl., 259–278 (2016). https://doi.org/10.1016/j.jnca.2016.09.008

  13. Gul, E., Ozturk, S.: A novel hash function based fragile watermarking method for image integrity. Multimedia Tools Appl. 78(13), 17701–17718 (2019). https://doi.org/10.1007/s11042-018-7084-0

    Article  Google Scholar 

  14. Milani, S., Piazza, P.F., Bestagini, P., Tubaro, S.: Audio tampering detection using multimodal features, 4563–4567. https://doi.org/10.1109/ICASSP.2014.6854466

  15. Cuccovillo, L., Mann, S., Tagliasacchi, M., Aichroth, P.: Audio tampering detection via microphone classification. In: 2013 IEEE International Workshop on Multimedia Signal Processing. MMSP 2013, pp. 177–182 (2013). https://doi.org/10.1109/MMSP.2013.6659284

  16. Xie, Z., Lu, W., Liu, X., Xue, Y., Yeung, Y.: Copy-move detection of digital audio based on multi-feature decision. J. Inf. Secur. Appl., 37–46. https://doi.org/10.1016/j.jisa.2018.10.003

  17. Pan, X., Zhang, X., Lyu, S.: Detecting splicing in digital audios using local noise level estimation. In: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, pp. 1841–1844. https://doi.org/10.1109/ICASSP.2012.6288260

  18. Isak-Zatega, S., Lipovac, A., Lipovac, V.: Logistic regression based in-service assessment of mobile web browsing service quality acceptability. EURASIP J. Wireless Commun. Netw. 2020(1), 1–21 (2020). https://doi.org/10.1186/s13638-020-01708-2

    Article  Google Scholar 

  19. Javed, A., Ejaz, A., Liaqat, S., Ashraf, A., Ihsan, M.B.: Automatic target classifier for a ground surveillance radar using linear discriminant analysis and logistic regression. In: European Microwave Week 2012: “Space for Microwaves”, EuMW 2012, Conference Proceedings, pp. 302–305 (2012)

    Google Scholar 

  20. Goodfellow, I.J., Bengio, Y., Courville, A.: Deep Learning

    Google Scholar 

  21. Ballesteros, D.M., Moreno, J.M.: On the ability of adaptation of speech signals and data hiding. Expert Syst. Appl. 39(16), 12574–12579 (2012). https://doi.org/10.1016/j.eswa.2012.05.027

    Article  Google Scholar 

  22. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. J.. [Online]. Available: http://www.math.harvard.edu/~ctm/home/text/others/shannon/entropy/entropy.pdf. Accessed 16 Sept 2019

Download references

Author information

Authors and Affiliations

  1. Universidad Militar Nueva Granada, Bogotá, Colombia

    Yohanna Rodríguez-Ortega, Dora María Ballesteros & Diego Renza

Authors
  1. Yohanna Rodríguez-Ortega
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dora María Ballesteros
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diego Renza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yohanna Rodríguez-Ortega .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco Jose de Caldas, Bogota, Colombia

    Hector Florez

  2. Covenant University, Ota, Nigeria

    Sanjay Misra

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rodríguez-Ortega, Y., Ballesteros, D.M., Renza, D. (2020). A Machine Learning Model to Detect Fake Voice. In: Florez, H., Misra, S. (eds) Applied Informatics. ICAI 2020. Communications in Computer and Information Science, vol 1277. Springer, Cham. https://doi.org/10.1007/978-3-030-61702-8_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-61702-8_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-61701-1

  • Online ISBN: 978-3-030-61702-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics