Multimedia Forensics

Audio Forensics

The use of digital media (images, audio, and video recordings) as evidence is rapidly growing in multiple civic applications, including litigation, journalism, and law enforcement. Verifying the authenticity and integrity of digital evidence is a challenging task given the ease with which digital contents can be copied and altered using widely available editing tools. In particular, in the absence of helping data, such as digital watermarks or side information and if the only input is the media itself, authentication poses a unique challenge to digital forensic science. Shown in Figure 1 is the general overview of the audio forensics problem.


Figure 1: General overview of the audio forensics problem

The goal of this research is to:

  1. Study characteristics of distortions (or fingerprint) due acquisition device (microphone) and acoustic environment, by mathematically modeling microphone nonlinearities using audio recordings made with a diverse set of microphones in a complex set of acoustic environments;
  2. Investigate uniqueness of microphone fingerprint, by analyzing inter-class and intra-class variability of microphone fingerprints;
  3. Investigate robustness to microphone fingerprint to various attacks such as lossy compression, noise addition, filtering, etc., by exploring key parameters of the problem space; and
  4. Determine whether microphone and acoustic environment fingerprints can be used for digital audio forensics applications such as linking a given digital audio recording to “the” acquisition device or linking a given digital audio recording to an acoustic environment.


Findings:

We have developed techniques for audio authentication and splicing detection [1, 5], microphone identification and tamper detection using microphone signature [2, 3], anti-forensic attack detection [4, 8], and developed benchmarking dataset for audio forensic methods [7].


Related Publications:

[1] Zhao, H., Chen, Y., Wang, R., and Malik, H., “Audio Source Authentication and Splicing detection using Acoustic Environmental Signature,” in Proc. ACM Workshop on Information Hiding and Multimedia Security 2014 (IH&MMSec’14), June 11- 13, Salzburg, Austria, 2014.

[2] Kurniawan1, F., Khalil, M.S., Khan, M.K., and Malik, H., “Dual-Microphone Forensics of Identical Model Based on Signal Analysis,” in Proc. of 5th Int. Conf. on Information Systems and Technologies (ICIST 2015) Istanbul, Turkey, March 21 – 23, 2015.

[3] Kurniawan1, F., Khalil, M.S., and Malik, H, “Robust Tampered Detection Method For Digital Audio using Gabor Filterbank,” in Proc. of 2015 Int. Conf. on Image Processing, Production and Computer Science (ICIPCS 2015), Istanbul, Turkey, June 3-4, 2015.

[4] Zhao, H., Chen, Y., Wang, R., and Malik, H., “Anti-Forensics of Environmental-Signature-Based Audio Splicing Detection and Its Countermeasure via Rich-features Classification,” IEEE Transactions on Information Forensics and Security, vol. 11(7), pp. 1603 – 1617, July 2016.

[5] Zhao, H., Chen, Y., Wang, R., and Malik, H., “Audio Splicing Detection and Localization using Acoustic Channel Impulse Response,” Springer Journal Multimedia Tools and Applications, DOI: 10.1007/s11042-016-3758-7, 2016.

[6] Zakariah, Z., Khan, M.K., and Malik, H., “Digital multimedia audio forensics: Past, present and future” Springer Journal Multimedia Tools and Applications, 10.1007/s11042-016-4277-2, 2017

[7] Khan, M.K., Zakariah, M., Malik H., and Choo, K.-K. R., “A Novel Audio Forensic Dataset for Digital Multimedia Forensics,” Australian Journal of Forensic Sciences, http://dx.doi.org/10.1080/00450618.2017.1296186, 2017.

[8] Hafeez, A., Malik, H., and Mahmood, K., “Performance of Blind Microphone Recognition Algorithms in the Presence of Anti-Forensic Attacks”, in Proc. of 54th AES Conf. on Audio Forensics 2017, Arlington, VA, June 15 – 17, 2017.