How can journalists and researchers forensically authenticate audio recordings published on platforms like Substack?
Executive summary
Journalists and researchers can treat audio posted on platforms like Substack as user‑generated evidence requiring the same forensic rigor applied in legal contexts: preserve originals, run file‑structure and metadata examinations, use signal analyses (spectrogram, ENF, codec traces) and comparative exemplars, and when necessary engage certified forensic labs for peer‑reviewed reports [1] [2] [3]. These methods cannot always prove content truthfulness—only consistency with an asserted origin and absence or presence of detectable manipulation—so findings must be framed with technical limits and chain‑of‑custody caveats [4] [5].
1. Preserve and document the original submission and chain of custody
The first forensic step is to secure the earliest available copy and document how it was obtained, because original device‑stored files carry the strongest integrity for testing and court use, while later copies can lose evidentiary value; experts recommend obtaining the original device or the earliest file version and recording transfer methods [4] [6].
2. Start with file‑structure and metadata for “front‑door” and “back‑door” clues
Examining hex/file structure, headers and embedded metadata can reveal editing footprints, application traces, or mismatches between container and encoded audio (the so‑called front door/back door), and many forensic shops use these indicators to flag likely manipulation before deeper audio work [1] [2].
3. Use signal‑level analyses to spot edits, splices and double compression
Spectrogram inspection, waveform continuity checks and detection of abrupt sound‑floor changes or jagged transitions are standard to reveal edits; specialized tests can detect double compression signatures (useful for common smartphone codecs like AMR) and anomalies introduced by re‑encoding or post‑processing [7] [8] [1].
4. Leverage environmental and codec fingerprints (ENF, codec frames, device logs)
Electrical Network Frequency traces can sometimes date and validate continuity of recordings, while codec frame periodicity and psychoacoustic codec traces can identify prior encoding or laundering through other recorders; pairing these with device‑specific application logs (e.g., iPhone Voice Memos patterns) helps tie a file to a claimed capture method or device [9] [10].
5. Create exemplars and run comparative tests whenever possible
Forensic best practice calls for exemplars—test recordings made on the same model, app and conditions—to compare spectral, temporal and metadata features against the evidence; comparative analysis can discriminate whether a file was likely produced by the claimed device or was “laundered” through another recorder [3] [4].
6. Use automated tools and human expert judgment, and disclose limits and peer review
Modern tool suites (spectral editors, ENF analyzers, TD‑Expert‑style toolkits and deep‑learning approaches for watermarking and forgery detection) accelerate detection but require trained interpretation; labs emphasize peer review, documented methodology, and cautious language because tools can flag inconsistencies but cannot prove intent or factual truth of spoken content [9] [11] [2].
7. Frame results responsibly in reporting and expose competing interpretations
When publishing, journalists must present technical findings as assessments of consistency and integrity—not binary truth—cite the methods used, disclose uncertainties and, when possible, offer alternative explanations (e.g., benign re‑encoding, device‑specific quirks, or expert disagreement), because authentication determines whether a file matches claimed provenance rather than proving the factual accuracy of words spoken [5] [4].
8. When stakes are high, engage accredited forensic analysts and expect courtroom standards
For contested, high‑impact recordings, courts and investigators expect formal reports, documented chain of custody, exemplar tests, and peer‑reviewable methods from accredited practitioners to support admissibility; private labs and academic groups provide these services and must be cited transparently in coverage [2] [6].