In ambito di perizia fonica viene sempre più spesso richiesta la verifica di eventuali manipolazioni, plagio o alterazioni su registrazioni audio ambientali o telefoniche, prodotte con registratori analogici o digitali, smartphone, telecamere, disponibili online oppure prodotte in cause civili o penali come prova informatica.
La Rete Europea degli Istituti di Scienze Forensi (ENFSI) fondata nel 1995, con lo scopo di migliorare lo scambio reciproco di informazioni nel campo delle scienze forensi, ha prodotto un utile manuale dedicato proprio alle best practice per l’analisi dell’autenticità di registrazioni audio digitali, finalizzato proprio a fornire delle linee guida per il perito fonico circa risorse, metodi, qualità, gestione e interpretazione delle analisi foniche.
Il manuale per la verifica di manipolazioni su file audio e registrazioni ENFSI-FSA-BPM-002 tramite tecniche di audio forensics è disponibile gratuitamente in pdf sul sito ENFSI per il download nella versione del 9 dicembre 2022 e contiene 26 pagine d’indicazioni su terminologia, risorse, metodi princìpi di validazione e gestione delle evidenze digitali consistenti in registrazioni audio digitali.
La guida ENSI che spiega come verificare se un file audio è autentica e come rilevare manipolazioni o plagio su registrazioni è stato prodotto con il supporto di Anna Bartle, Dagmar Boss, Alexander G. Boyarov, Luca Cuccovillo, Catalin Grigoras, Marcin Michałek, Dan Nyberg e altri ricercatori.
L’analisi dell’autenticità delle registrazioni audio digitali tramite metodologie di audio forensics si basa sulle tracce lasciate all’interno della registrazione durante il processo di registrazione e da altre operazioni di editing successive. In altre parole, durante la registrazione e l’editing successivo, possono essere lasciati segni o tracce che possono essere utilizzati per determinare se la registrazione è stata modificata o meno e il perito fonico incaricato può tentare di rilevarle e valutare l’integrità, originalità, autenticità delle registrazioni o, nei casi migliori, l’assenza di manipolazioni, alterazioni o modifiche sui file audio registrati.
Il primo obiettivo dell’analisi audio forense è quello di rilevare ed identificare quali tracce di manipolazione possono essere recuperate dalla registrazione audio, e di documentarne le proprietà. In una seconda fase, le proprietà di potenziale manipolazione delle tracce recuperabili vengono analizzate per determinare se supportano o contrastano l’ipotesi che la registrazione sia stata modificata.
Tuttavia, non è sempre facile per il perito fonico distinguere le tracce dovute alla registrazione da quelle dovute alle operazioni di post-produzione. Per questo motivo, uno degli obiettivi fondamentali di qualsiasi analisi di autenticità di tracce audio e registrazioni sonore è quello di determinare se le caratteristiche osservate di un pezzo di prova audio sono state introdotte dal processo di registrazione originale o dalle azioni successive. In sintesi, l’analisi audio forense dell’autenticità delle registrazioni audio digitali è un processo complesso che richiede la valutazione di una vasta gamma di tracce e proprietà, al fine di stabilire se la registrazione è stata alterata o manipolata in qualche modo.
Di assoluto rilievo la parte relativa ai metodi di audio forensics per accertare la presenza di manipolazioni o manomissioni su file audio, che riporta alcuni utili consigli come:
- Continuità delle tracce varianti con il tempo
- Invariabilità delle tracce invarianti con il tempo
- Invariabilità delle tracce periodiche
- Rilevamento delle tracce di post-processing
- Confronto tra le tracce di registrazione e le informazioni contestuali
La bibliografia e i riferimenti del testo, infine, rappresentano una raccolta di know-how audio forense inestimabile per le attività di perizia fonica e validazione dell’assenza di manipolazioni e integrità/originalità delle registrazioni. Si riportano, per comodità, i riferimenti bibliografici del manuale, suddivisi per categorie, così da agevolarne la lettura:
Informazioni Generali
[1] BRD-GEN-003, Code of conduct, ENFSI, version 002, 2005.
[2] SWGDE, Digital & Multimedia Evidence Glossary, version 3.0, 2016.
[3] ASTM E2916, Standard Terminology for Digital and Multimedia Evidence Examination,
2013.
[4] ENFSI-BPM-FIT-01, Best practice manual for the forensic examination of digital
technology, version 01, 2015.
[5] SWGDE, Best Practices for Digital Audio Authentication, version 1.2, 21/01/2017.
Autenticazione forense dell’audio
[6] D. Bergfeld and K. Junte, “The effects of peripheral stimuli and equipment used on speech intelligibility in noise,” in AES International Conference on Audio Forensics, Arlington, VA, USA, 2017.
[7] B. E. Koenig and D. S. Lacey, “Forensic authentication of digital audio recordings,” Journal of the Audio Engineering Society, vol. 57, no. 9, pp. 662–695, 2009.
[8] E. B. Brixen, “Techniques for the authentication of digital audio recordings,” in 112th AES Convention, Vienna, Austria, 2007.
[9] C. Grigoras, D. Rappaport, and J. M. Smith, “Analytical framework for digital audio authentication,” in AES International Conference on Audio Forensics, Denver, CO, USA, 2012.
Struttura e analisi delle formanti
[10] C. Grigoras and J. M. Smith, “Large scale test of digital audio file structure and format for forensic analysis,” in AES International Conference on Audio Forensics, Arlington, VA, USA, 2017.
[11] J. M. Smith, D. S. Lacey, B. E. Koenig, and C. Grigoras, “Triage approach for the forensic analysis of apple iOS audio files recorded using the “Voice Memos” app,” in AES International Conference on Audio Forensics, Arlington, VA, USA, 2017.
[12] M. Michałek, “Test audio recordings and their use in authenticity examinations. Database of properties of digital audio recorders and recordings,” Problems of Forensic Sciences, vol. 105, pp. 355–369, 2016.
[13] B. E. Koenig and D. S. Lacey, “Forensic authenticity analyses of the metadata in re- encoded WAV files,” in AES International Conference on Audio Forensics, London, United Kingdom, 2014.
[14] B. E. Koenig, D. S. Lacey, and C. E. Reimond, “Selected characteristics of MP3 files re- encoded with audio editing software,” Journal of Forensic Identification, vol. 64, no. 3, pp. 304–321, 2014.
[15] B. E. Koenig and D. S. Lacey, “Forensic authenticity analyses of the header data in re- encoded WMA files from small Olympus audio recorders,” Journal of the Audio Engineering Society, vol. 60, no. 4, pp. 255–265, 2012.
[16] M. Michałek, “Properties of recordings and audio files saved in AMR format and an assessment of the possibility of applying them in authenticity examinations,” Problems of Forensic Sciences, vol. 109, pp. 27–42, 2017.
[17] M. Michałek, “Metadata in audio files compliant with ISO/IEC 14496-12 and their characteristics as well as the evaluation of usability in the investigation of the authenticity of recordings,” Problems of Forensic Sciences, vol. 115, pp. 241–261, 2018.
[18] M. Michałek, “The characteristics of popular audio recording applications installed on smartphones with an Android operating system in relation to forensic audio analyses,” Problems of Forensic Sciences, vol. 120, pp. 335–361, 2019.
[19] B. E. Koenig and D. S. Lacey, “Forensic authenticity analyses of the metadata in re- encoded iPhone M4A files,” in AES International Conference on Audio Forensics, Arlington, VA, USA, 2017.
Analisi nel dominio del tempo
[20] B. E. Koenig and D. S. Lacey, “The average direct current offset values for small digital audio recorders in an acoustically consistent environment,” Journal of Forensic Sciences, vol. 59, no. 4, pp. 960–966, 2014.
[21] B. E. Koenig, D. S. Lacey, C. Grigoras, S. G. Price, and J. M. Smith, “Evaluation of the average DC offset values for nine small digital audio recorders,” Journal of the Audio Engineering Society, vol. 61, no. 6, pp. 439–448, 2013.
[22] B. E. Koenig, D. S. Lacey, C. Grigoras, S. G. Price, and J. M. Smith, “Evaluation of the average DC offset values for nine small digital audio recorders,” in AES International Conference on Audio Forensics, Denver, CO, USA, 2012.
[23] A. J. Cooper, “Detecting butt-spliced edits in forensic digital audio recordings,” in AES International Conference on Audio Forensics, Hillerød, Denmark, 2010.
Analisi delle tracce di codifica
[24] C. Grigoras, “Statistical tools for multimedia forensics: Compression effects analysis,” in AES International Conference on Audio Forensics, Hillerød, Denmark, 2010.
[25] C. Grigoras and J. M. Smith, “Quantization level analysis for forensic media authentication,” in AES International Conference on Audio Forensics, London, United Kingdom, 2014.
[26] L. Cuccovillo and P. Aichroth, “Inverse decoding of PCM A-law and μ-law,” in AES International Conference on Audio Forensics, Porto, Portugal, 2019.
[27] D. Seichter, L. Cuccovillo, and P. Aichroth, “AAC encoding detection and bitrate estimation using a convolutional neural network,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016, pp. 2069– 2073.
[28] C. Grigoras and J. M. Smith, “Forensic analysis of AAC encoding on Apple iPhone Voice Memos recordings,” in AES International Conference on Audio Forensics, Porto, Portugal, 2019.
[29] A.G. Boyarov and I.S. Siparov, “Forensic Investigation of MP3 Audio Recordings,” Theory and Practice of Forensic Science, vol. 14, no. 4, pp. 125–136, 2019.
[30] R. Korycki, “Authenticity examination of lossy compressed digital audio recordings,” in EAA Conference – Forum Acusticum, Kraków, Poland, 2014.
[31] S. Moehrs, J. Herre, and R. Geiger, “Analysing decompressed audio with the “Inverse Decoder” – towards an operative algorithm,” in 112th AES Convention, Munich, Germany, 2002.
[32] J. Herre and M. Schug, “Analysis of Decompressed Audio – the inverse decoder,” in 109th AES Convention, Los Angeles, CA, USA, 2000.
[33] D. Gärtner, C. Dittmar, P. Aichroth, L. Cuccovillo, S. Mann, and G. Schuller, “Efficient cross-codec framing grid analysis for audio tampering detection,” in 136th AES Convention, Berlin, Germany, 2014.
[34] R. Korycki, “Detection of montage in lossy compressed digital audio recordings,” Archives of Acoustics, vol. 39, no. 1, pp. 65–72, 2014.
[35] R. Yang, Z. Qu, and J. Huang, “Detecting digital audio forgeries by checking frame offsets,” in ACM Workshop on Multimedia and Security, Oxford, United Kingdom, 2008, pp. 21–26.
[36] J. Zhou, D. Garcia-Romero, and C. Y. Espy-Wilson, “Automatic speech codec identification with applications to tampering detection of speech recordings,” in ISCA Annual Conference (INTERSPEECH), Florence, Italy, 2011, pp. 2533–2536.
[37] R. Korycki, “Authenticity investigation of digital audio recorded as MP3 files,” Issues of Forensic Science, vol. 283, no. 1, pp. 54–67, 2014.
Analisi del microfono (risposta nella frequenza)
[38] L. Cuccovillo and P. Aichroth, “Open-set microphone classification via blind channel analysis,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Shanghai, China, 2016, pp. 2069–2073.
[39] L. Cuccovillo, S. Mann, M. Tagliasacchi, and P. Aichroth, “Audio tampering detection via microphone classification,” in IEEE International Workshop on Multimedia Signal Processing (MMSP), Pula, Italy, 2013, pp. 177–182.
[40] D. Luo, P. Korus, and J. Huang, “Band energy difference for source attribution in audio forensics,” IEEE Transactions on Information Forensics and Security, vol. 14, no. 9, pp. 2179–2189, 2018.
[41] C. Krätzer, A. Oermann, J. Dittmann, and A. Lang, “Digital audio forensics: A first practical evaluation on microphone and environment classification,” in ACM Workshop on Multimedia and Security, Dallas, TX, USA, 2007, pp. 63–74.
Analisi del microfono (risposta termica)
[42] R. Buchholz, C. Krätzer, and J. Dittmann, “Microphone classification using fourier coefficients,” in Springer International Workshop on Information Hiding (IH), Darmstadt, Germany, 2009, pp. 235–246.
[43] R. Aggarwal, S. Singh, A. Kumar Roul, and N. Khanna, “Cellphone identification using noise estimates from recorded audio,” in IEEE International Conference on Communications and Signal Processing (ICCSP), Melmaruvathur, India, 2014, pp. 1218– 1222.
[44] M. Jahanirad, A. W. Abdul Wahab, N. B. Anuar, M. Y. Idna Idris, and M. N. Ayub, “Blind identification of source mobile devices using VoIP calls,” in IEEE Region 10 Symposium, Kuala Lumpur, Malaysia, 2014, pp. 486–491.
Analisi della frequenza della rete elettrica
[45] C. Grigoras and J. M. Smith, “Advances in ENF analysis for digital media authentication,” in AES International Conference on Audio Forensics, Denver, CO, USA, 2012.
[46] ENFSI. (2009). “Best practice guidelines for ENF analysis in forensic authentication of digital evidence FSAAWG-BPM-ENF-001 (1.0).”
[47] C. Grigoras, “Digital audio recording analysis: The electric network frequency (ENF) criterion,” The International Journal of Speech, Language and the Law, vol. 12, no. 2, pp. 63–76, 2005.
[48] L. Cuccovillo and P. Aichroth, “Increasing the temporal resolution of ENF analysis via harmonic distortion,” in AES International Conference on Audio Forensics, Arlington, VA, USA, 2017.
[49] M. Fuentes, P. Zinemanas, P. Cancela, and J. A. Apolinário, “Detection of ENF discontinuities using PLL for audio authenticity,” in IEEE Latin American Symposium on Circuits & Systems (LASCAS), Florianopolis, Brazil, 2016, pp. 79–82.
[50] D. P. Nicolalde Rodríguez, J. A. Apolinário, and L.W. Pereira Biscainho, “Audio authenticity: Detecting ENF discontinuity with high precision phase analysis,” IEEE Transactions on Information Forensics and Security, vol. 5, no. 3, pp. 534–543, 2010.
[51] M. Michałek, “The application of powerline hum in digital recording authenticity analysis,” Problems of Forensic Sciences, vol. 80, pp. 355–364, 2009.
[52] M. Huijbregtse and Z. Geradts, “Using the ENF criterion for determining the time of recording of short digital audio recordings,” in Springer International Workshop on Computational Forensics (IWCF), The Hague, The Netherlands, 2009, pp. 116–124.
[53] C. Grigoras, “Applications of ENF criterion in forensic audio, video, computer and telecommunication analysis,” Forensic Science International, vol. 167, no. 2-3, pp. 136– 145, 2007.
[54] M. Kajstura, A. Trawínska, and J. Hebenstreit, “Application of the electrical network frequency (ENF) criterion: A case of a digital recording,” Forensic Science International, vol. 155, no. 2-3, pp. 165–171, 2005.
[55] N. Campos and A. Ferreira, “Real-time monitoring of ENF and THD quality parameters of the electrical grid in Portugal,” in AES International Conference on Audio Forensics, London, United Kingdom, 2014.
[56] A. Hajj-Ahmad, R. Garg, and M. Wu, “ENF-based region-of-recording identification for media signals,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 6, pp. 1125– 1136, 2015.
[57] Ž. Šarić, A. Žunić, T. Zrnić, M. Knežević, D. Despotović, and T. Delić, “Improving location of recording classification using electric network frequency (ENF) analysis,” in IEEE International Symposium on Intelligent Systems and Informatics (SISY), Subotica, Serbia, 2016, pp. 51–56.
[58] J. Zjalic, C. Grigoras, and J. M. Smith, “A low cost, cloud based, portable, remote ENF system,” in AES International Conference on Audio Forensics, Arlington, VA, USA, 2017.
[59] C. Grigoras, J. M. Smith, and C. Jenkins, “Advances in ENF database configuration for forensic authentication of digital media,” in 131st AES Convention, New York City, NY,
USA, 2011.
[60] G. Hua, Y. Zhang, and V. L. L. Goh Jonathan; Thing, “Audio authentication by exploring
the Absolute-Error-Map of ENF signals,” IEEE Transactions on Information Forensics and
Security, vol. 11, no. 5, pp. 1003-1016, 2016.
[61] C. Grigoras, “Applications of ENF analysis in forensic authentication of digital audio and
video recordings,” Journal of the Audio Engineering Society, vol. 57, no. 9, pp. 643–661, 2009.
Rilevamento di manipolazioni da copia o spostamento
[62] M. Imran, Z. Ali, S. T. Bakhsh, and S. Akram, “Blind detection of copy-move forgery in digital audio forensics,” IEEE Access, vol. 5, pp. 12 843–12 855, 2017.
[63] M. Maksimović, L. Cuccovillo, and P. Aichroth, “Copy-move forgery detection and localization via partial audio matching,” in AES International Conference on Audio Forensics, Porto, Portugal, 2019.
Analisi ambientale
[64] H. Malik and H. Farid, “Audio forensics from acoustic reverberation,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, TX, USA, 2010, pp. 1710–1713.
[65] H. Malik and H. Zhao, “Recording environment identification using acoustic reverberation,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan, 2012, pp. 1833–1836.
[66] H. Moore, M. Brookes, and P. A. Naylor, “Room identification using roomprints,” in AES International Conference on Audio Forensics, London, United Kingdom, 2014.
Rilevamento di desampling
[67] D. Vázquez-Padín and P. Comesaña, “ML estimation of the resampling factor,” in IEEE International Workshop on Information Forensics and Security (WIFS), Costa Adeje, Spain, 2012, pp. 1833–1836.
[68] D. Vázquez-Padín, P. Comesaña, and F. Pérez-González, “Set-membership identification of resampled signals,” in IEEE International Workshop on Information Forensics and Security (WIFS), Guangzhou, China, 2013, pp. 150–155.
Rilevamento della doppia codifica
[69] T. Bianchi, A. De Rosa, M. Fontani, G. Rocciolo, and A. Piva, “Detection and classification of double compressed MP3 audio tracks,” in ACM workshop on Information hiding and multimedia security, Montpellier, France, 2013, pp. 159–164.
[70] Q. Huang, R. Wang, D. Yan, and J. Zhang, “AAC audio compression detection based on QMDCT coefficient,” in Springer International Conference on Cloud Computing and Security (ICCCS), Haikou, China, 2018, pp. 347–359.
[71] R. Korycki, “Authenticity examination of compressed audio recordings using detection of multiple compression and encoders’ identification,” Forensic Science International, vol. 283, no. 1-3, pp. 54–67, 2014.
