Authentic Audio Recovery (AAR)

• Authentic Audio Recovery (AAR) is a multifaceted field that defines methods for reconstructing and verifying audio integrity and provenance from degraded or manipulated sources.
• It encompasses techniques such as contactless carrier recovery, spectral phase retrieval, learned inpainting via latent priors, and proactive watermark-mediated recovery for tamper localization.
• Practical implementations of AAR address challenges like measurement inconsistencies, noise robustness, and the trade-off between exact waveform reconstruction and plausible, perceptually strong restoration.

Searching arXiv for papers on “Authentic Audio Recovery” and closely related audio recovery/watermarking/provenance work to ground the article. Authentic Audio Recovery (AAR) denotes a family of methods for reconstructing, recovering, or re-establishing audio authenticity under conditions where the original signal, its provenance, or both have been degraded, obscured, or manipulated. In the literature, the term is used in several technically distinct senses: reconstructing sound encoded in fragile analogue carriers without contact, recovering a time-domain signal from incomplete or magnitude-only observations, recovering authentic speech from a tampered audiovisual stream through a pre-embedded cross-modal watermark, and recovering cryptographic authenticity evidence from the audio signal itself (Feng, 2020, Kim et al., 17 Jul 2025, Yang et al., 8 May 2026). This suggests that AAR is not a single inverse problem but a research area spanning physical media recovery, statistical restoration, watermark-mediated recovery, and provenance verification.

1. Scope, definitions, and problem classes

Across the cited work, AAR is defined operationally rather than axiomatically. In the sonorine work, authenticity means reconstructing the sound encoded in a fragile analogue carrier “as faithfully as possible,” using only images and computation and “without ever touching the carrier” (Feng, 2020). In Synthesized Audiovisual Forgeries (SAVFs), AAR is explicitly defined as recovering the “original, authentic audio signal” from a tampered audiovisual stream by decoding a watermark embedded in the visual stream before manipulation (Kim et al., 17 Jul 2025). In cryptographic watermarking, AAR means recovering a signed message and a proof of provenance from the audio itself, with successful verification interpreted as a positive authenticity signal (Yang et al., 8 May 2026). In active restoration, authenticity is tied to recovery from self-embedded latent side information prepared before loss occurs (Cheddad et al., 2021).

Formulation	Observation at recovery time	Recovery target
Contactless carrier recovery	Photographs or scans of a physical carrier	Sound encoded in grooves
Spectral or waveform restoration	Magnitude spectrograms or degraded audio	Time-domain signal
Cross-modal watermarking	Tampered visual stream	Authentic audio and tamper intervals
Cryptographic watermarking	Watermarked audio and public key	Signed payload and provenance proof
Ledger-based authentication	Query audio and ledger state	Registered origin metadata

A common misconception is that AAR always denotes waveform-identical restoration. The literature instead contains exactness-oriented formulations, such as groove-based virtual playback and signature verification, alongside plausibility-oriented formulations in which missing or band-limited regions are regenerated from learned conditional distributions (Kong et al., 20 Jan 2025). Another misconception is that authenticity recovery is equivalent to passive deepfake detection. Several works are explicitly proactive: they assume authenticity information is embedded or registered before degradation or manipulation occurs (Kim et al., 17 Jul 2025, Chenna et al., 2021).

2. Recovery from physical carriers and incomplete spectra

In contactless recovery from sonorines, the pipeline is explicitly: photos $\rightarrow$ surface normals $\rightarrow$ height map $\rightarrow$ virtual stylus $\rightarrow$ audio (Feng, 2020). Four high-resolution 16-bit TIFF photographs are captured under different light directions, together with blank paper images and mirror-sphere calibration images. Light direction vectors are estimated from the mirror spheres, normalized images are formed, and Woodham’s photometric stereo model is solved per pixel: $I = L \cdot n,$ followed by height reconstruction from the normal field and virtual groove tracing. The groove signal is converted to audio by using the derivative of groove height along the traced path, which mimics stylus velocity playback. The work emphasizes non-contact preservation, algorithmic auditability, and the fact that the recovered signal should reflect the current physical groove rather than an idealized prior state (Feng, 2020).

The same study also identifies the technical fragility of exact physical recovery. The camera-based photovisual method produced a height map in which grooves were visible but often faded, and only about two seconds of audio were recovered from one sonorine; the result was too noisy and short to recognize voice content. By contrast, the earlier flatbed-based method produced recognizable human voice from some sonorines. The reported conclusion was that, for now, flatbed scanning is preferable for sonorine AAR (Feng, 2020).

A related but more abstract reconstruction problem arises when only STFT magnitudes or power spectrograms are available. Phase retrieval is then posed as reconstruction of a signal $x^\star$ from

$r \approx |A x^\star|^d,$

with $A$ the STFT operator and $d \in \{1,2\}$. Classical formulations minimize a quadratic mismatch,

$\min_x \|r - |Ax|^d\|_2^2,$

but Bregman-divergence formulations replace this with either a right or left divergence,

$\rightarrow$0

which are no longer equivalent because Bregman divergences are generally asymmetric (Vial et al., 2020). The resulting algorithms are derived using accelerated gradient descent and ADMM, and quadratic-loss Griffin–Lim appears as a special case. The reported finding is not that non-quadratic divergences dominate universally; rather, quadratic-loss methods remain strong when the spectrogram is exact or only slightly degraded, whereas some KL or $\rightarrow$1-divergence formulations improve intelligibility under very noisy conditions (Vial et al., 2020).

Taken together, these lines of work place one branch of AAR within classical inverse problems. The object to be recovered may be a groove height trajectory or a missing STFT phase field, but in both cases the target is a signal constrained by a measured physical or spectral representation. Authenticity in this branch is tied to measurement consistency rather than provenance metadata.

3. Learned restoration, inpainting, and latent priors

A second branch of AAR treats recovery as conditional generation under a learned prior. Audio-to-Audio Schrödinger Bridges (A2SB) models restoration in a 3-channel STFT representation

$\rightarrow$2

with clean endpoint $\rightarrow$3 and degraded endpoint

$\rightarrow$4

Training uses a mask-restricted objective so that only the corrupted region is modified, and sampling starts from the observed degraded spectrogram rather than from pure Gaussian noise (Kong et al., 20 Jan 2025). The model supports both bandwidth extension and time-domain inpainting, is end-to-end without a vocoder, can restore hour-long inputs through MultiDiffusion, and was reported to achieve state-of-the-art bandwidth extension and inpainting quality on several out-of-distribution music test sets (Kong et al., 20 Jan 2025). The same source also states the central limitation for AAR: the output is “plausible rather than exactly authentic,” because high-frequency or missing content is sampled from a learned conditional distribution rather than deterministically recovered (Kong et al., 20 Jan 2025).

Active Restoration of Lost Audio Signals (ARLAS) proposes a more explicitly proactive architecture. The original waveform is scaled to $\rightarrow$5, reshaped to a 2D matrix, halftoned with Floyd–Steinberg dithering, flattened and permuted, and then embedded into the least significant bits of the original audio samples via steganography (Cheddad et al., 2021). At recovery time, the embedded binary representation is extracted, inverse-permuted, reshaped, and inverse-halftoned with a Gaussian kernel

$\rightarrow$6

then combined with wavelet-derived scalogram features. A Random Forest or LSTM is trained on intact regions of the same audio and used to predict the missing gap. The paper reports that the proposed framework outperformed D2WGAN, SG, SPAIN, TDC, and wCP in average ranking across ODG, Hansen’s QC, and SNR, with the proposed RF ranked best in SNR and among the best in ODG and QC (Cheddad et al., 2021). Here authenticity is not blind; it depends on prior self-embedding of latent side information.

Latent Vector Recovery of Audio GANs addresses a different form of recovery: given audio $\rightarrow$7, recover a latent vector $\rightarrow$8 such that $\rightarrow$9 reconstructs the signal. For synthesized audio, the model is trained with latent MSE and a perceptual loss defined by a spoken-digit ResNet-18 classifier; for real audio, only perceptual loss is available because there is no ground-truth latent (Keyes et al., 2020). On WaveGAN-generated speech, the inverse mapping model achieved near-identical reconstruction performance and high Inception Score; for real audio, it produced a “reasonable reconstruction” of the input. This does not by itself define authenticity, but it makes explicit the role of generator priors in mapping observed audio back onto a learned manifold (Keyes et al., 2020).

4. Watermark-mediated recovery and tamper localization

The most explicit formulation of AAR as a named task appears in work on SAVFs. There the original audiovisual stream is written as

$\rightarrow$0

the watermarked stream as

$\rightarrow$1

and the tampered stream as

$\rightarrow$2

The framework uses an Invertible Neural Network (INN) to embed an audio representation into the visual frame after DWT on the image and STFT-plus-reshape on the audio, with $\rightarrow$3 INN blocks (Kim et al., 17 Jul 2025). Recovery requires estimating the discarded latent audio state from the tampered frame, then running the INN backward and applying inverse STFT to obtain $\rightarrow$4. The paper also defines Tamper Localization in Audio (TLA) by comparing semantic features of $\rightarrow$5 and $\rightarrow$6 over time. Reported results include AAR at SNR $\rightarrow$7 dB and PESQ $\rightarrow$8, TLA under audio swapping at IoU $\rightarrow$9, AP $\rightarrow$0, AUC $\rightarrow$1, and TLA under voice cloning at IoU $\rightarrow$2, AP $\rightarrow$3, AUC $\rightarrow$4; the watermarked visuals retain PSNR $\rightarrow$5 dB and SSIM $\rightarrow$6 (Kim et al., 17 Jul 2025).

The cryptographic variant of watermark-mediated AAR does not aim to reconstruct a pristine waveform. Asymmetric Phase Coding (APC) embeds a payload

$\rightarrow$7

protects it with Reed–Solomon coding, and writes it into pseudo-random STFT bins through a phase channel and a redundant magnitude-QIM channel (Yang et al., 8 May 2026). Verification is blind-extractable from the received audio and the public key alone. On 1,000 LibriSpeech test-clean clips under eight attack conditions, APC achieved cryptographic verification rates between $\rightarrow$8 and $\rightarrow$9, mean PESQ $I = L \cdot n,$0, mean STOI $I = L \cdot n,$1, and tens-of-milliseconds CPU latency (Yang et al., 8 May 2026). The paper is explicit that this recovers a signed message and proof of provenance, not necessarily bit-exact integrity of the current waveform unless the message semantics encode a canonical content hash.

These two watermarking paradigms recover different objects. Cross-modal watermarking recovers the authentic audio signal itself from the visual modality (Kim et al., 17 Jul 2025). APC recovers an auditable provenance primitive from the audio signal and lets authenticity fail closed when the watermark is erased or corrupted (Yang et al., 8 May 2026). Both are proactive rather than purely forensic.

5. Provenance infrastructures and acoustic-environment authenticity

Distributed-ledger approaches shift AAR toward provenance lookup. In the IoAuT architecture, the actual audio file is stored in IPFS and metadata are recorded on a custom Proof-of-Work blockchain, including recTimestamp, recDuration, recNumChannels, deviceMaker, deviceModel, deviceMacAdd, deviceGpsInfo, ipfsHash, contentId, and recSignature, the latter being an acoustic fingerprint computed using Chromaprint/fpcalc (Chenna et al., 2021). Authentication of an unknown audio file proceeds by computing its fingerprint and duration, searching the chain for a matching recSignature and recDuration, or locating the corresponding contentId embedded in ID3v2 metadata, then verifying the fingerprint and metadata consistency. The system is therefore not a waveform restorer but a registry that “recovers” authenticity by mapping a query signal back to an immutable record of capture provenance (Chenna et al., 2021).

The same study makes clear that provenance recovery is only as strong as the capture path. It explicitly notes that “the chain of trust does not start with the blockchain, but with the IoAuT recording devices themselves,” and that MAC addresses and GPS can be spoofed (Chenna et al., 2021). It also reports that, in its robustness experiment, the acoustic fingerprint changed under all tested manipulations, including trimming, gain change, time shift, and pitch shift, which makes the prototype strong for fragile integrity checking but unsuitable for tolerant matching of edited derivatives (Chenna et al., 2021).

A related concern appears in acoustic-environment transfer. EchoMark addresses Acoustic Environment Matching (AEM), where clean audio is transferred into a target acoustic environment by generating a perceptually similar Room Impulse Response (RIR) with an embedded watermark (Huang et al., 9 Nov 2025). The stated motivation is that direct RIR recovery from reverberant speech enables misuse such as arbitrary “relocation,” advanced voice spoofing attacks, or attacks on the authenticity of recorded evidence. EchoMark operates in the latent domain to handle variable RIR durations and energy decays, jointly optimizing perceptual RIR reconstruction and watermark detection. The reported results are room acoustic parameter matching performance comparable to FiNS, a Mean Opinion Score of $I = L \cdot n,$2 out of $I = L \cdot n,$3, watermark detection accuracy exceeding $I = L \cdot n,$4, and BER below $I = L \cdot n,$5 (Huang et al., 9 Nov 2025). In this setting, authenticity is attached not only to the dry signal but also to its acoustic context.

6. Evaluation criteria, limitations, and open directions

The evaluation of AAR is heterogeneous because the target object differs across formulations. Signal-reconstruction papers use SNR, PESQ, STOI, LSD, SiSpec, ViSQOL, ODG, Hansen’s QC, and MOS; watermarking papers add BER and cryptographic verification rate; tamper-localization papers use IoU, AP, and AUC; and visual carriers are evaluated with PSNR and SSIM (Kong et al., 20 Jan 2025, Cheddad et al., 2021, Kim et al., 17 Jul 2025, Yang et al., 8 May 2026). This metric diversity is not incidental: it reflects a genuine division between fidelity to an original waveform, perceptual plausibility, provenance recoverability, and localization accuracy.

Several limitations recur. Pre-embedded protection is a hard requirement in cross-modal watermarking, APC, and active steganographic restoration; unprotected legacy media cannot be retroactively upgraded into these regimes (Kim et al., 17 Jul 2025, Yang et al., 8 May 2026, Cheddad et al., 2021). Erasure remains possible in principle for any perceptible watermark, and APC states this explicitly under its forgery-versus-erasure threat model (Yang et al., 8 May 2026). Physical reconstruction depends on calibration assumptions such as Lambertian reflectance and accurate light vectors, and the sonorine work attributes its performance gap partly to light-direction inaccuracies, non-Lambertian behavior, and solver smoothing trade-offs (Feng, 2020). Learned restoration may be perceptually strong while remaining only statistically faithful, as A2SB states directly for missing or high-frequency regions (Kong et al., 20 Jan 2025). Provenance systems depend on secure capture hardware and trustworthy registration, a condition that the DLT work identifies but does not implement through PKI, attestation, or secure enclaves (Chenna et al., 2021).

A common misunderstanding is to treat these limitations as evidence that AAR is internally inconsistent. The literature instead suggests a layered architecture. Exact physical or spectral recovery addresses what is still inferable from the signal or carrier (Feng, 2020, Vial et al., 2020). Learned generative recovery addresses perceptual restoration where information is genuinely missing (Kong et al., 20 Jan 2025). Watermarking and ledger systems address provenance and tamper evidence when authenticity must survive distribution and adversarial manipulation (Kim et al., 17 Jul 2025, Yang et al., 8 May 2026, Chenna et al., 2021). EchoMark extends the same logic to the authenticity of room acoustics rather than only waveform content (Huang et al., 9 Nov 2025). A plausible implication is that mature AAR systems will combine these layers rather than choose among them: physical measurement where possible, probabilistic completion where necessary, and explicit provenance channels wherever authenticity claims must remain auditable under attack.