EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments

Moussa D, Hirsch G, Rieß C (2025)

Publication Language: English

Publication Type: Journal article

Publication year: 2025

Journal

IEEE Transactions on Information Forensics and Security Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 20

Pages Range: 2281-2296

URI: https://arxiv.org/pdf/2405.02119

DOI: 10.1109/TIFS.2025.3541534

Abstract

Audio recordings may provide important evidence in criminal investigations. One such case is the forensic association of a recorded audio to its recording location. For example, a voice message may be the only investigative cue to narrow down the candidate sites for a crime. Up to now, several works provide supervised classification tools for closed-set recording environment identification under relatively clean recording conditions. However, in forensic investigations, the candidate locations are case-specific. Thus, supervised learning techniques are not applicable without retraining a classifier on a sufficient amount of training samples for each case and respective candidate set. In addition, a forensic tool has to deal with audio material from uncontrolled sources with variable properties and quality. In this work, we therefore attempt a major step towards practical forensic application scenarios. We propose a representation learning framework called EnvId, short for environment identification. EnvId avoids case-specific retraining by modeling the task as a few-shot classification problem. We demonstrate that EnvId can handle forensically challenging material. It provides good quality predictions even under unseen signal degradations, out-of-distribution reverberation characteristics or recording position mismatches.

Authors with CRIS profile

Denise Moussa Lehrstuhl für Informatik 1 (IT-Sicherheitsinfrastrukturen) Christian Rieß Lehrstuhl für Informatik 1 (IT-Sicherheitsinfrastrukturen)

Related research project(s)

Cybercrime and Forensic Computing -- Hardware Security (GRK 2475) GRK2475: Cyberkriminalität und Forensische Informatik Oct. 1, 2019 - Oct. 1, 2028

How to cite

APA:

Moussa, D., Hirsch, G., & Rieß, C. (2025). EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments. IEEE Transactions on Information Forensics and Security, 20, 2281-2296. https://doi.org/10.1109/TIFS.2025.3541534

MLA:

Moussa, Denise, Germans Hirsch, and Christian Rieß. "EnvId: A Metric Learning Approach for Forensic Few-Shot Identification of Unseen Environments." IEEE Transactions on Information Forensics and Security 20 (2025): 2281-2296.

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