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Digital multimedia audio forensics: past, present and future

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Abstract

Digital audio forensics is used for a variety of applications ranging from authenticating audio files to link an audio recording to the acquisition device (e.g., microphone), and also linking to the acoustic environment in which the audio recording was made, and identifying traces of coding or transcoding. This survey paper provides an overview of the current state-of-the-art (SOA) in digital audio forensics and highlights some open research problems and future challenges in this active area of research. The paper categorizes the audio file analysis into container and content-based analysis in order to detect the authenticity of the file. Existing SOA, in audio forensics, is discussed based on both container and content-based analysis. The importance of this research topic has encouraged many researchers to contribute in this area; yet, further scopes are available to help researchers and readers expand the body of knowledge. The ultimate goal of this paper is to introduce all information on audio forensics and encourage researchers to solve the unanswered questions. Our survey paper would contribute to this critical research area, which has addressed many serious cases in the past, and help solve many more cases in the future by using advanced techniques with more accurate results.

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References

  1. Alexander A, Forth O, Tunstall D (2012) Music and noise fingerprinting and reference cancellation applied to forensic audio enhancement. In: Audio engineering society conference: 46th international conference: audio forensics

  2. Balasubramaniyan VA, Poonawalla A, Ahamad M, Hunter MT, Traynor P (2010) PinDr0p: using single-ended audio features to determine call provenance. In Proceedings of the 17th ACM conference on computer and communications security, pp 109–120

  3. Bang KH, Park Y-C, Youn D-H (2006) A dual audio transcoding algorithm for digital multimedia broadcasting services. In: Audio Engineering Society Convention 120

  4. Bianchi T, Rosa AD, Fontani M, Rocciolo G, Piva A (2014) Detection and localization of double compression in MP3 audio tracks. EURASIP J Inf Secur 2014:10

    Article  Google Scholar 

  5. Boll S (1979) Suppression of acoustic noise in speech using spectral subtraction. Acoustics, Speech and Signal Processing, IEEE Transactions on 27:113–120

    Article  Google Scholar 

  6. Brixen EB (2007) Techniques for the authentication of digital audio recordings. In: Audio Engineering Society Convention 122

  7. Buchholz R, Kraetzer C, Dittmann J (2009) Microphone classification using Fourier coefficients. In: Information hiding, pp 235–246

  8. Chaudhary UA, Malik H (2010) Automatic recording environment identification using acoustic features. In: Audio Engineering Society Convention 129

  9. Chen N, Xiao H-D, Wan W (2011) Audio hash function based on non-negative matrix factorisation of mel-frequency cepstral coefficients. Information Security, IET 5:19–25

    Article  Google Scholar 

  10. Cuccovillo L, Mann S, Tagliasacchi M, Aichroth P (2013) Audio tampering detection via microphone classification. In: Multimedia Signal Processing (MMSP), 2013 I.E. 15th International Workshop on, pp 177–182

  11. D’Alessandro B, Shi YQ (2009) MP3 bit rate quality detection through frequency spectrum analysis. In: Proceedings of the 11th ACM workshop on multimedia and security, pp 57–62

  12. Ding H, Havelock DI (2010) Drift-compensated adaptive filtering for improving speech intelligibility in cases with asynchronous inputs. EURASIP J Adv Signal Process 2010:12

  13. Garcia-Romero D, Espy-Wilson CY (2010) Automatic acquisition device identification from speech recordings. In: Acoustics Speech and Signal Processing (ICASSP), 2010 I.E. International Conference on, pp 1806–1809

  14. Gerazov B, Kokolanski Z, Arsov G, Dimcev V (2012) Tracking of electrical network frequency for the purpose of forensic audio authentication. In: Optimization of Electrical and Electronic Equipment (OPTIM), 2012 13th International Conference on, 2012, pp 1164–1169

  15. Grigoras C (2007) Applications of ENF criterion in forensic audio, video, computer and telecommunication analysis. Forensic Sci Int 167:136–145

    Article  Google Scholar 

  16. Grigoras C (2009) Applications of ENF analysis in forensic authentication of digital audio and video recordings. J Audio Eng Soc 57:643–661

    Google Scholar 

  17. Grigoras C (2010) Statistical tools for multimedia forensics. In: Audio engineering society conference: 39th international conference: audio forensics: practices and challenges

  18. Gupta S, Cho S, Kuo C-C (2012) Current developments and future trends in audio authentication. MultiMedia, IEEE 19:50–59

    Article  Google Scholar 

  19. Hatje U, Musialik CM (2005) Frequency-domain processors for efficient removal of noise and unwanted audio events. In: Audio Engineering Society Conference: 26th International Conference: Audio Forensics in the Digital Age

  20. Hermansky H (1990) Perceptual linear predictive (PLP) analysis of speech. The Journal of the Acoustical Society of America 87:1738–1752

    Article  Google Scholar 

  21. Hicsonmez S, Sencar HT, Avcibas I (2011) Audio codec identification through payload sampling. In: Information Forensics and Security (WIFS), 2011 I.E. international workshop on, pp 1–6

  22. http://cybertechnos.com/datasets

  23. Ikram S, Malik H (2010) Digital audio forensics using background noise. In: Multimedia and Expo (ICME), 2010 I.E. International Conference on, pp 106–110

  24. Jenner F, Kwasinski A (2012) Highly accurate non-intrusive speech forensics for codec identifications from observed decoded signals. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 I.E. international conference on, pp 1737–1740

  25. Ju F-S, Fang C-M (2006) Time-frequency domain fast audio transcoding. In: Multimedia, 2006. ISM’06. Eighth IEEE international symposium on, pp 750–753

  26. Koenig BE (1990) Authentication of forensic audio recordings. J Audio Eng Soc 38:3–33

    Google Scholar 

  27. Koenig BE, Lacey DS (2009) Forensic authentication of digital audio recordings. J Audio Eng Soc 57:662–695

    Google Scholar 

  28. Koenig BE, Lacey DS (2012) Forensic authenticity analyses of the header data in re-encoded WMA files from small Olympus audio recorders. J Audio Eng Soc 60:255–265

    Google Scholar 

  29. Koenig BE, Lacey DS, Killion SA (2007) Forensic enhancement of digital audio recordings. J Audio Eng Soc 55:352–371

    Google Scholar 

  30. Korycki R (2014a) Authenticity examination of compressed audio recordings using detection of multiple compression and encoders’ identification. Forensic Sci Int 238:33–46

    Article  Google Scholar 

  31. Korycki R (2014b) Detection of montage in lossy compressed digital audio recordings. Archives of Acoustics 39:65–72

    Google Scholar 

  32. Kraetzer C, Oermann A, Dittmann J, Lang A (2007) Digital audio forensics: a first practical evaluation on microphone and environment classification. In: Proceedings of the 9th workshop on Multimedia & security, pp 63–74

  33. C. Kraetzer, K. Qian, M. Schott, and J. Dittmann (2011) A context model for microphone forensics and its application in evaluations. In: IS&T/SPIE Electronic Imaging, pp 78800P–78800P-15

  34. Kurniawan F, Rahim MSM, Khalil MS, Khan MK (2016) Statistical-based audio forensic on identical microphones. International Journal of Electrical and Computer Engineering (IJECE) 6:2211–2218

    Article  Google Scholar 

  35. Lim JS, Oppenheim AV (1979) Enhancement and bandwidth compression of noisy speech. Proc IEEE 67:1586–1604

    Article  Google Scholar 

  36. Liu Q, Sung AH, Qiao M (2010) Detection of double MP3 compression. Cogn Comput 2:291–296

    Article  Google Scholar 

  37. Luo D, Yang R, Huang J (2015) Identification of AMR decompressed audio. Digital Signal Processing 37:85–91

    Article  Google Scholar 

  38. Lv Z, Hu Y, Li C-T, Liu B-B (2013) Audio forensic authentication based on MOCC between ENF and reference signals. In: Signal and Information Processing (ChinaSIP), 2013 I.E. China Summit & International Conference on, pp 427–431

  39. Maher R (2009) Audio forensic examination. Signal Processing Magazine, IEEE 26:84–94

    Article  Google Scholar 

  40. Maher RC (2010) Overview of audio forensics. In: Intelligent multimedia analysis for security applications. Springer, vol. 282, pp. 127–144

  41. Malik H (2013) Acoustic environment identification and its applications to audio forensics. Information Forensics and Security, IEEE Transactions on 8:1827–1837

    Article  Google Scholar 

  42. Malik H, Farid H (2010) Audio forensics from acoustic reverberation. In: Acoustics Speech and Signal Processing (ICASSP), 2010 I.E. International Conference on, pp 1710–1713

  43. Malik H, Zhao H (2012) Recording environment identification using acoustic reverberation. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 I.E. International Conference on, pp 1833–1836

  44. Mansour MF (2009) Strategies for bit allocation reuse in audio transcoding. In: ICASSP, pp 157–160

  45. Mansour MF (2012) A transcoding system for audio standards. IEEE transactions on multimedia 14:1381–1389

    Article  Google Scholar 

  46. McAulay R, Malpass M (1980) Speech enhancement using a soft-decision noise suppression filter. Acoustics, Speech and Signal Processing, IEEE Transactions on 28:137–145

    Article  Google Scholar 

  47. Moon C-B, Kim H, Kim BM (2014) Audio recorder identification using reduced noise features. In: Ubiquitous information technologies and applications, Springer, pp 35–42

  48. Muhammad G, Alotaibi YA, Alsulaiman M, Huda MN (2010) Environment recognition using selected MPEG-7 audio features and Mel-Frequency Cepstral Coefficients. In: Digital Telecommunications (ICDT), 2010 Fifth International Conference on, pp 11–16

  49. Nikias CL (1993) Higher-order spectral analysis. In: Engineering in Medicine and Biology Society, 1993. Proceedings of the 15th Annual International Conference of the IEEE. pp 319–319

  50. Olanrewaju R, Khalifa O (2012) Digital audio watermarking; techniques and applications, In: Computer and Communication Engineering (ICCCE), 2012 International Conference on,pp 830–835

  51. Owen T (1996) AES recommended practice for forensic purposes-managing recorded audio materials intended for examination. J Audio Eng Soc 44(4):275

    Google Scholar 

  52. Paliwal K, Wójcicki K, Schwerin B (2010) Single-channel speech enhancement using spectral subtraction in the short-time modulation domain. Speech Comm 52:450–475

    Article  Google Scholar 

  53. Qiao M, Sung AH, Liu Q (2010) Revealing real quality of double compressed MP3 audio. In: Proceedings of the international conference on multimedia, pp 1011–1014

  54. Qiao M, Sung AH, Liu Q (2013) Improved detection of MP3 double compression using content-independent features. In: Signal Processing, Communication and Computing (ICSPCC), 2013 I.E. international conference on, pp 1–4

  55. Rabiner LR, Schafer RW (1978) Digital processing of speech signals, vol 100. Prentice-hall, Englewood Cliffs

    Google Scholar 

  56. Ratnam R, Jones DL, Wheeler BC, O’Brien WD Jr, Lansing CR, Feng AS (2003) Blind estimation of reverberation time. The Journal of the Acoustical Society of America 114:2877–2892

    Article  Google Scholar 

  57. Rodríguez DPN, Apolinário JA, Biscainho LWP (2010) Audio authenticity: detecting ENF discontinuity with high precision phase analysis. Information Forensics and Security, IEEE Transactions on 5:534–543

    Article  Google Scholar 

  58. Shanmugasundaram K, Kharrazi M, Memon N (2004) Nabs: a system for detecting resource abuses via characterization of flow content type. In: Computer security applications conference, 2004. 20th Annual, pp 316–325

  59. Sharma D, Naylor PA, Gaubitch ND, Brookes M (2012) Non intrusive codec identification algorithm. In: Acoustics, Speech and Signal Processing (ICASSP), 2012 I.E. international conference on, pp 4477–4480

  60. Soulodre GA (2010) About this dereverberation business: A method for extracting reverberation from audio signals. In: Audio Engineering Society Convention 129

  61. Takagi K, Miyaji S, Sakazawa S, Takishima Y (2006) Conversion of MP3 to AAC in the compressed domain. In: Multimedia Signal Processing, 2006 I.E. 8th Workshop on, pp 132–135

  62. Tsoukalas DE, Mourjopoulos JN, Kokkinakis G (1997) Speech enhancement based on audible noise suppression. Speech and Audio Processing, IEEE Transactions on 5:497–514

    Article  MATH  Google Scholar 

  63. Weiss M, Aschkenasy E, Parsons T (1975) Study and development of the INTEL technique for improving speech intelligibility. DTIC Document

  64. Yang R, Qu Z, Huang J (2008) Detecting digital audio forgeries by checking frame offsets. In Proceedings of the 10th ACM workshop on multimedia and security, pp 21–26

  65. Yang R, Shi Y-Q, Huang J (2009) Defeating fake-quality MP3. In: Proceedings of the 11th ACM workshop on multimedia and security, pp 117–124

  66. Yang R, Shi YQ, Huang J (2010) Detecting double compression of audio signal. In: IS&T/SPIE electronic imaging, pp 75410 K–75410 K-10

  67. Yang R, Qu Z, Huang J (2012) Exposing MP3 audio forgeries using frame offsets. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 8:35

    Google Scholar 

  68. Yiu K-K, Mak M-W, Kung S-Y (2003) Environment adaptation for robust speaker verification. In: INTERSPEECH

  69. Zhang Y, Zhao Y (2013) Modulation domain blind speech separation in noisy environments. Speech Comm 55:1081–1099

    Article  Google Scholar 

  70. Zhao H, Malik H (2012) Audio forensics using acoustic environment traces. In: Statistical Signal Processing Workshop (SSP), 2012 IEEE, 2012, pp 373–376

  71. Zhao H, Malik H (2013) Audio recording location identification using acoustic environment signature. Information Forensics and Security, IEEE Transactions on 8:1746–1759

    Article  Google Scholar 

  72. Zhao H, Chen Y, Wang R, Malik H (2014) Audio source authentication and splicing detection using acoustic environmental signature. In: Proceedings of the 2nd ACM workshop on Information hiding and multimedia security, pp 159–164

  73. Zhou J, Garcia-Romero D, Espy-Wilson CY (2011) Automatic speech codec identification with applications to tampering detection of speech recordings. In proceedings of Interspeech, Florence, Italy, August, 2011, pp. 2533–2536

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Acknowledgement

“This Project was funded by the National Plan for Science, Technology and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, Award Number (12-INF2634-02)”.

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Correspondence to Muhammad Khurram Khan.

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Zakariah, M., Khan, M.K. & Malik, H. Digital multimedia audio forensics: past, present and future. Multimed Tools Appl 77, 1009–1040 (2018). https://doi.org/10.1007/s11042-016-4277-2

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  • DOI: https://doi.org/10.1007/s11042-016-4277-2

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