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REVIEW 5 major objections 6 minor 36 references

Reject Threshold Adaptation for Open-Set Model Attribution of Deepfake Audio

T0 review · 5 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read ReTA replaces hand-tuned rejection thresholds with per-class adaptive thresholds derived from reconstruction error distributions, and reports the best open-set attribution accuracy on the SFR deepfake audio benchmark.

desk verdict A competent transplant of an existing SAR OSR method to deepfake audio, with a plausible but unvalidated proxy assumption and a missing threshold objective; worth a serious referee but needs revision. read the letter →

arxiv 2412.01425 v1 pith:7O6OZBSL submitted 2024-12-02 cs.SD cs.AIeess.AS

Xinrui Yan , Jiangyan Yi , Jianhua Tao , Yujie Chen , Hao Gu , Guanjun Li , Junzuo Zhou , Yong Ren
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Tao Xu
This is my paper · ORCID
classification cs.SDcs.AIeess.AS
keywords opensetrecognitiondeepfakeaudiomodelattributionsystemfingerprintrejectthresholdadaptationreconstructionerrorkerneldensityestimationadaptive
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the manual rejection threshold used in open-set recognition of deepfake audio is the main bottleneck, and proposes ReTA, a framework that learns each known generator class's reconstruction error distribution and then computes a per-class reject threshold automatically. On the SFR benchmark, ReTA reports the best open-set attribution results, raising average F1 to 77.35% from 75.66% for CROSR and 73.44% for OpenMax, and average total accuracy to 70.09% from 67.91%. The pitch is that thresholds should be derived from the data rather than tuned by hand, so the same method carries over to new data distributions without re-tuning.

What carries the argument

The load-bearing mechanism is the pair of reconstruction error distributions built by the reconstruction error learning module. For each training sample the decoder receives the fingerprint representation multiplied either by the conditional latent feature of its true class (matching) or by a randomly drawn latent feature from another known class (non-matching), producing two error sets per class; the adaptive threshold module then fits a Gaussian kernel density estimate to each set and chooses the class-specific reject threshold by probability minimization. This mechanism converts the open-set question into a per-class anomaly detection problem in reconstruction-error space.

What would settle it

Take a held-out deepfake generator that was never used in training, feed its audio through ReTA, and measure the reconstruction error for its predicted label; if that error distribution overlaps substantially with the matching-error distributions of the known classes, the per-class thresholds will be miscalibrated and the open-set F1 will drop close to the fixed-threshold baselines. Concretely, training only on five of the seven vendors and testing on the other two as unknowns, then varying which vendors are held out, would reveal whether the simulated non-matching errors are a faithful proxy.

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Extended reading notes

Core claim

The central claim is that a closed-set deepfake attribution classifier can be turned into an open-set one by calibrating a separate rejection threshold for each known class, where those thresholds are computed from the distributions of matching and non-matching reconstruction errors rather than chosen by hand. The paper shows that when a sample's fingerprint representation is decoded together with the correct class label the reconstruction error is small, while decoding with a randomly chosen other class label produces a larger error, and it fits a Gaussian kernel density estimate to each set of errors to locate the threshold that minimizes the probability of misclassification. Experiments on the SFR dataset, with Tencent and iFLYTEK as unknown classes, show average F1 of 77.35% and average total accuracy of 70.09%, beating the OpenMax and CROSR baselines while largely retaining closed-set ID accuracy.

Load-bearing premise

The method assumes that the reconstruction errors obtained by pairing a known class fingerprint with a different known class label behave like the reconstruction errors of genuinely unknown classes, so that calibrating thresholds on those simulated mismatches transfers to real unknown classes at test time.

Editorial extensions

If this is right

  • ReTA achieves the best average F1 and total accuracy on SFR among the compared methods, including on the compressed set where unknown detection is harder.
  • The per-class thresholds are derived without manual validation-set tuning, so deployment to a new open-set scenario does not require re-setting a global threshold.
  • The framework turns an existing closed-set fingerprint classifier into an open-set one by adding the reconstruction decoder and threshold module, so it can be attached to other ResNet-based extractors.
  • The design predicts that matching reconstruction errors cluster tightly while non-matching errors spread out, which is what the threshold calculation exploits.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the proxy assumption holds across domains, ReTA could serve as a general open-set recognition wrapper for any fingerprint-based attribution system, replacing hand-tuned confidence thresholds in production.
  • A natural testable extension is to model the unknown-class error distribution with a mixture or heavy-tailed density instead of a Gaussian, since real unknown generators may produce multi-modal errors; the probability minimization criterion could be sensitive to tail overlap.
  • The same reconstruction-error geometry could be used to detect out-of-distribution audio beyond attribution, for example by treating real human speech as an additional known class and rejecting everything else.
  • The reported gap between clean and compressed performance suggests that compression changes the error distributions substantially; an extension could condition thresholds on audio quality or estimate distribution shift during inference.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes ReTA, a framework for open-set model attribution of deepfake audio. It consists of a system fingerprint recognition module, a reconstruction error learning module that produces matching and non-matching reconstruction samples, and an adaptive reject threshold module that uses kernel density estimation and a "probability minimization criterion" to set per-class rejection thresholds. The method is evaluated on the SFR dataset, where ReTA is compared with Softmax, OpenMax, and CROSR, reporting the highest average F1 score and total accuracy.

Significance. If the method is fully specified and validated, it addresses a genuine practical problem: manual rejection thresholds in open-set audio attribution are brittle and do not transfer across data distributions. The paper has a fair experimental setup in that thresholds are fit to training data and unknown vendors (Tencent, iFLYTEK) are held out, so the central result is not circular. The reported gains, however, are modest (average F1 77.35 vs 75.66 for CROSR), and the current manuscript does not specify the probability minimization criterion, lacks error bars or significance tests, and does not validate the key proxy assumption that known-class mismatched reconstruction errors model unknown-class errors. The contribution is promising but not yet established.

major comments (5)
  1. [Section 2.1.3] The "probability minimization criterion" is the core of the adaptive threshold mechanism but is never defined mathematically or algorithmically. The text only states that "optimal reject thresholds are determined using the probability minimization criterion" and provides a flowchart in Figure 4. Please specify the objective function in a formula, including how the two KDEs are combined, what is minimized (e.g., a weighted sum of error probabilities), and any hyperparameters such as false-positive/false-negative costs or grid resolution.
  2. [Section 2.2 vs Section 3.1] The training procedure is internally inconsistent: Section 2.2 states that the modules "use stochastic gradient descent (SGD) to iteratively update parameters," while Section 3.1 states "The training was conducted with the Adam optimizer." These are different optimization algorithms. Please clarify which optimizer is actually used and provide the exact training configuration.
  3. [Table 2 and Section 3.3] The performance comparison reports only point estimates. The average F1 improvement over CROSR is 1.69 points (77.35 vs 75.66), and the clean-set F1 improvement is only 0.41 points (89.35 vs 88.94). Without repeated runs, standard deviations, or a paired significance test, the claim that ReTA "overall achieves the best" is not statistically supported. Please report mean±std over at least five random seeds and apply an appropriate significance test (e.g., paired bootstrap).
  4. [Section 2.1.2 and Section 2.1.3] The central assumption of the method is that non-matching reconstruction errors, obtained by pairing a known-class fingerprint with a randomly selected other known-class label, are a faithful proxy for the reconstruction errors of genuinely unknown classes such as Tencent and iFLYTEK. This assumption is never validated. Please add a leave-one-known-out experiment: hold out one of the five training vendors as an unknown class, calibrate thresholds using the remaining four, and compare the simulated non-matching error distribution with the held-out vendor's actual error distribution. Report a quantitative separation measure (e.g., AUROC, KL divergence, or overlap coefficient) to demonstrate that the proxy holds.
  5. [Section 3.3] The baseline threshold selection is unspecified. The paper states that Softmax, OpenMax, and CROSR use manually set thresholds and that all methods use the same LFCC features and ResNet backbone, but it does not describe how each baseline's threshold was chosen (e.g., grid search on the development set, a fixed percentile, or a single scalar). Without this information, the comparison may be biased in favor of ReTA if the baselines' thresholds are poorly tuned. Please document the threshold selection protocol for each baseline and provide threshold sensitivity curves analogous to Figure 2.
minor comments (6)
  1. [Section 3 heading] The section heading "Experiences" should be "Experiments."
  2. [Conclusion] The word "atribution" is a typo and should be "attribution."
  3. [Figure 3] The label "X mm" in Figure 3 appears to be a typo and should likely be "X^nm" (non-matching), and the caption uses "Mismatching Features" while the text uses "non-matching"; please make the terminology consistent.
  4. [Equation (1)] The notation in Eq. (1) is ambiguous: X'_i is described as "a randomly sampled sample from other target classes," but it is not clear whether the non-matching reconstruction target is the original input X_i reconstructed under a wrong class condition, or a different sample X'_i reconstructed under its own or another wrong class condition. Please clarify the exact pairing and loss computation.
  5. [Section 2.1.1] The phrase "constraining the activation of the penultimate layer [26]" is vague; please specify the constraint (e.g., ReAct or a bounded activation) and how it is applied to the ResNet-18 encoder.
  6. [Introduction and References] Reference [16] describes a similar "reject threshold adaptation" method for SAR target recognition, but the paper does not discuss the differences between ReTA and [16] or what is newly contributed beyond adapting that approach to audio model attribution. Please add a brief comparison.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: ReTA's thresholds are fit to training reconstruction errors and evaluated on held-out unknown vendors, with self-citations used only as independent benchmark and motivation.

full rationale

ReTA's rejection thresholds are computed entirely from training-time reconstruction error distributions: matching errors (same-class reconstruction) and non-matching errors (fingerprint paired with a randomly chosen other known class label), via KDE and a probability-minimization criterion (Sections 2.1.2 and 2.1.3). The test protocol holds out Tencent and iFLYTEK as unknown classes (Table 1) and never uses their labels or errors to set thresholds. Thus the headline F1 gains (77.35 average vs. CROSR's 75.66 in Table 2) are not forced by construction: thresholds are fit to known-class data and then evaluated on genuinely unseen vendors. The self-citations are to the SFR dataset [2] and to a vocoder-fingerprint study [23] used as motivation (Figure 2); these are independent artifacts or benchmarks rather than fitted parameters or unverified uniqueness theorems. The weakest point is the empirical proxy assumption that non-matching known-class reconstruction errors approximate unknown-class errors; if false, thresholds would be miscalibrated. But that is an empirical validity concern, not a circular derivation, because the unknown-class labels and errors are not inputs to threshold fitting. No circular step requiring Eq. X = Eq. Y by construction, or a renamed fitted parameter presented as a prediction, was found in the paper.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The method rests on the proxy assumption that non-matching reconstruction errors from known classes represent unknown classes, plus unstated hyperparameters (alpha, KDE bandwidth, threshold criterion). No new physical entities are introduced.

free parameters (3)
  • alpha (loss weighting) = not reported
    Overall loss L = LCE + alpha * LREC in Eq. 2; the value is never stated and controls the balance between classification and reconstruction.
  • KDE bandwidth = not reported
    Gaussian kernel density estimation in Section 2.1.3 requires a bandwidth; no value or selection method is given, and thresholds depend on it.
  • probability minimization criterion parameters = not reported
    The threshold calculation is described only as 'probability minimization criteria' in Section 2.1.3; no cost function, priors, or optimization details are provided.
assumptions (4)
  • domain assumption Non-matching reconstruction errors from randomly selected known classes approximate unknown-class reconstruction errors
    Section 2.1.2 and 2.1.3; if violated, per-class thresholds are miscalibrated.
  • domain assumption Gaussian kernel density estimation faithfully models reconstruction error distributions with the chosen bandwidth
    Section 2.1.3; bandwidth is unreported and can change thresholds.
  • domain assumption LFCC features plus ResNet-18 capture distinguishing system fingerprints
    Section 2.1.1; inherited from the SFR dataset paper [2] without re-validation.
  • domain assumption Per-class thresholds computed on training data transfer to unseen data distributions
    Section 1 claims data adaptability, but no cross-dataset experiment is run.

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Cite this review

Pith. "Pith review of Reject Threshold Adaptation for Open-Set Model Attribution of Deepfake Audio." pith.science (2026). https://pith.science/paper/7O6OZBSL

@misc{pith2026241201425,
  author       = {Pith},
  title        = {Pith review of: Reject Threshold Adaptation for Open-Set Model Attribution of Deepfake Audio},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7O6OZBSL}},
  note         = {Machine review of arXiv:2412.01425}
}
read the original abstract

Open environment oriented open set model attribution of deepfake audio is an emerging research topic, aiming to identify the generation models of deepfake audio. Most previous work requires manually setting a rejection threshold for unknown classes to compare with predicted probabilities. However, models often overfit training instances and generate overly confident predictions. Moreover, thresholds that effectively distinguish unknown categories in the current dataset may not be suitable for identifying known and unknown categories in another data distribution. To address the issues, we propose a novel framework for open set model attribution of deepfake audio with rejection threshold adaptation (ReTA). Specifically, the reconstruction error learning module trains by combining the representation of system fingerprints with labels corresponding to either the target class or a randomly chosen other class label. This process generates matching and non-matching reconstructed samples, establishing the reconstruction error distributions for each class and laying the foundation for the reject threshold calculation module. The reject threshold calculation module utilizes gaussian probability estimation to fit the distributions of matching and non-matching reconstruction errors. It then computes adaptive reject thresholds for all classes through probability minimization criteria. The experimental results demonstrate the effectiveness of ReTA in improving the open set model attributes of deepfake audio.

Figures

Figures reproduced from arXiv: 2412.01425 by the authors.

Figure 1
Figure 1. Threshold-based open-set recognition diagram. τ a τ b (a) Open-set recognition experiments on system fingerprints (b) Open-set recognition experiments on vocoder fingerprints [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. The drawback of manually set threshold-based open￾set recognition. The open-set recognition performance in the left and right figures is highly sensitive to the rejection thresh￾old, and the optimal rejection threshold varies for different model attribution tasks. In real-world open environments, new speech generation models continuously emerge, making it impossible to exhaus￾tively list all possible categories in a… view at source ↗
Figure 3
Figure 3. Overview of the network architecture of ReTA. It consists of 1) system fingerprint recognition module; 2) reconstruction error learning module; 3) adaptive rejection threshold module. Kernel Density Estimation D1 D2 。。。 DK F1 F2。。。 FK Rejection Threshold Calculation τ τ τ ... Error Distribution Gaussian Kernel Function Threshold Matching Reconstruction Error Mismatching Reconstruction Error KDE for Matching Error KD… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: The flowchart of the adaptive reject threshold calcu￾lation module. 2.2. Training The system fingerprint recognition module and the recon￾struction error learning module use stochastic gradient descent (SGD) to iteratively update parameters. The overall loss is de￾term…

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