Component-Level Audio Spoofing

• Component-Level Audio Spoofing is a fine-grained approach that decomposes audio into speech and environmental components for independent authenticity verification.
• It leverages methodologies such as separation-enhanced joint learning, temporal analysis, and pipeline tracing to improve detection accuracy and spoof attribution.
• Empirical studies show that tailored component analysis enhances performance in mixed-content and open-set scenarios by mitigating masking effects from genuine segments.

Component-Level Audio Spoofing (Comp-Spoof) denotes a class of audio manipulation attacks in which only certain components of a signal are forged or substituted while other components remain genuine. In its current benchmarked form, the dominant formulation treats audio as a mixture of foreground speech and environmental sound, each of which may independently be bona fide or spoofed; this departs from conventional anti-spoofing, which assigns a single utterance-level label to the entire waveform (Zhang et al., 19 Sep 2025). More broadly, related work shows that component analysis can also refer to temporal segments within a sentence, specific generation modules such as conversion and waveform generation, or acquisition-chain elements such as room, microphone, playback device, and codec (Zhang et al., 12 Jan 2026).

1. Definition and conceptual scope

Comp-Spoof is motivated by the observation that real-world audio rarely consists of a single homogeneous speech stream. The formulation used in recent benchmarks explicitly decomposes audio into two components: speech, defined as the linguistically meaningful foreground speech produced by the primary speaker, and environmental sound, defined as non-speech background or non-target speech. A component-level attack may therefore forge only the speech component, only the environmental component, or both simultaneously. This makes the problem qualitatively different from whole-utterance deepfake detection, because a fully natural component can mask artifacts in the manipulated one and can mislead systems trained only for global bona fide-versus-spoof discrimination (Zhang et al., 12 Jan 2026).

Earlier anti-spoofing work already contained implicit component structure, even when the task was posed as binary classification. In ASVspoof-style terminology, logical access (LA) attacks are generated in the digital domain and mainly involve speech synthesis and voice conversion, whereas physical access (PA) attacks are replay attacks mediated by room acoustics, microphones, loudspeakers, and propagation conditions. This partition corresponds to two broad subsystems: generation components in LA and channel or acquisition components in PA (P et al., 2020). Related attribute-based work makes this modularity explicit by classifying spoofing attributes such as conversion, speaker representation, and waveform generator rather than only deciding whether an utterance is fake (Zhu et al., 2022).

A second line of work extends the component notion along the time axis. Partial spoofing datasets and models study utterances in which bona fide and spoofed segments coexist, and spoof diarization further asks “what spoofed when,” combining temporal localization with clustering by spoofing method (Liu et al., 2024). This suggests that Comp-Spoof is best understood as a family of fine-grained anti-spoofing tasks defined over identifiable substructures of the audio generation, transmission, and composition process rather than over the utterance as an indivisible object (Zhang et al., 2024).

2. Formal task formulations and taxonomies

The canonical speech–environment formulation models a mixed waveform as

$x(t) = s(t) + b(t),$

where $s(t)$ is speech and $b(t)$ is environmental sound. In this setting, each component may independently be bona fide or spoofed, yielding a five-class taxonomy that distinguishes original co-recorded audio from artificial mixtures (Zhang et al., 12 Jan 2026).

ID	Class label	Description
0	original	Original audio without any manipulation or mixing
1	bonafide_bonafide	Bona fide speech with bona fide environmental sound from other audio
2	spoof_bonafide	Spoofed speech mixed with bona fide environmental sound
3	bonafide_spoof	Bona fide speech mixed with spoofed environmental sound
4	spoof_spoof	Spoofed speech mixed with spoofed environmental sound

This taxonomy operationalizes component-level authenticity as a structured label rather than a binary utterance tag. In effect, the system must determine whether the clip is an untouched original or a mixture, and, if it is a mixture, whether speech and environment are each genuine or manipulated (Zhang et al., 12 Jan 2026).

Temporal formulations introduce a different but complementary label space. Spoof diarization defines a function

$$f_\text{dia}: \boldsymbol{x}_{1:T} \mapsto \boldsymbol{c}_{1:M}, \quad c_m \in \{\text{bona fide},\, A_1, \dots, A_N, \text{[ConP]}\},$$

where the output is a sequence of frame- or segment-level labels identifying bona fide regions, specific spoofing methods, and optionally concatenated parts. This formulation turns partial spoofing into a sequence labeling problem and makes explicit that Comp-Spoof may involve both localization and attribution (Zhang et al., 2024).

A third formulation is pipeline tracing. “Source Tracing: Detecting Voice Spoofing” models logical-access spoofing systems as compositions of input processing, conversion, speaker representation, and waveform generation modules, then focuses on three spoofing attributes—conversion, speaker representation, and waveform generator. Each utterance is assigned a triplet of labels over these module families, and spoof detection is obtained by combining attribute-specific spoof probabilities (Zhu et al., 2022). A plausible implication is that Comp-Spoof admits multiple orthogonal ontologies: speech-versus-environment, temporal segment structure, and generation-pipeline attributes.

3. Datasets, benchmarks, and evaluation settings

The first dataset explicitly designed for component-level audio anti-spoofing is CompSpoof. It contains 2,500 audio samples, five classes with 500 samples each, durations from 5 to 21 seconds, and a 70%/10%/20% train/dev/eval split. Bona fide speech is drawn from ASVspoof 5 and CommonVoice, spoofed speech from ASVspoof 5 and SSTC, bona fide environmental sound from VGGSound, spoofed environmental sound from VCapAV, and the original class from authentic VGGSound recordings containing co-recorded speech and environment. All files are resampled to 16 kHz, and the separation-based framework processes 4-second chunks with 2-second hop (Zhang et al., 19 Sep 2025).

CompSpoofV2 scales this formulation substantially. It is described as a large-scale curated dataset for component-level audio anti-spoofing with over 250k audio samples and approximately 283 hours. It is used in the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2), where evaluation and test sets include “New Generated” speech and environmental sound absent from training, thereby stressing generalization to unseen generators and manipulations (Zhang et al., 12 Jan 2026).

Component-level benchmarks now span several granularities:

Resource	Component scope	Key characteristics
CompSpoof	Speech + environment	2,500 samples, five balanced classes, 5–21 s (Zhang et al., 19 Sep 2025)
CompSpoofV2	Speech + environment	Over 250k samples, about 283 hours, unseen “New Generated” attacks in eval/test (Zhang et al., 12 Jan 2026)
PartialSpoof / HAD / Psynd / ADD PF	Temporal segments	Partial replacement and segment localization benchmarks (Li et al., 2024)
MultiAPI Spoof	Generation source	About 230 hours of spoofed speech from 30 APIs plus equal bona fide speech (Zhang et al., 8 Dec 2025)

The evaluation protocols reflect these differing scopes. ESDD2 uses Macro-F1 over the five classes as the primary ranking metric and supplements it with $\mathrm{EER}_{\text{original}}$, $\mathrm{EER}_{\text{speech}}$, and $\mathrm{EER}_{\text{env}}$ to disentangle original detection from speech- and environment-specific spoof detection (Zhang et al., 12 Jan 2026). Partial spoofing studies additionally use utterance-level EER, frame- or segment-level EER, and range-based metrics for manipulated-region localization (Li et al., 2024). Generator-attribution benchmarks such as MultiAPI Spoof define an API tracing task over 21 seen APIs plus an unseen class, thereby converting anti-spoofing into open-set source attribution (Zhang et al., 8 Dec 2025).

4. Methodological paradigms

The dominant Comp-Spoof architecture is separation-enhanced joint learning. In this design, a mixture detector first decides whether an input is an original recording or a mixed-content clip; if mixed, a separation model estimates speech and environmental components; dedicated anti-spoofing heads then classify each separated component. In the CompSpoof framework, the overall objective is

$$L_\mathrm{joint} = \kappa \, L_\mathrm{sepa} + L_\mathrm{cls}^\mathrm{mixed} + L_\mathrm{cls}^\mathrm{speech} + L_\mathrm{cls}^\mathrm{env} + L_\mathrm{cons},$$

with $\kappa = 10$, and the consistency term is

$$L_\mathrm{cons} = \mathrm{KL}(p_\mathrm{ref}^\mathrm{env} \,\|\, p_\mathrm{sepa}^\mathrm{env}) + \mathrm{KL}(p_\mathrm{ref}^\mathrm{speech} \,\|\, p_\mathrm{sepa}^\mathrm{speech}).$$

The purpose of joint learning is to prevent the separator from discarding spoof-relevant cues while still producing component estimates usable by speech- and environment-specific detectors (Zhang et al., 19 Sep 2025).

Temporal component analysis follows a different strategy. Partial spoofing work shows that countermeasures trained on partially spoofed data can be interpreted frame by frame, and that they often attend strongly to transition regions created when bona fide and spoofed segments are concatenated. “How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?” uses Grad-CAM and Relative Contribution Quantification to show that partially spoof-trained systems prioritize transition-region artifacts, whereas systems trained only on fully spoofed audio focus more on pattern differences between bona fide and spoofed segments (Liu et al., 2024). Boundary-centric systems make this explicit: “Waveform Boundary Detection for Partially Spoofed Audio” formulates detection as frame-level boundary localization and reports the best partially spoofed audio detection performance among systems that can locate manipulated clips on the ADD2022 challenge test set (Cai et al., 2022).

Spoof diarization extends localization to method attribution. The 3C model, short for Countermeasure-Condition Clustering, uses one countermeasure branch to extract frame-level embeddings for clustering and another to localize bona fide versus spoof regions; a label-based countermeasure constraint then forces frames predicted as bona fide to remain bona fide in the diarization output. This creates a pipeline in which clustering is conditioned by anti-spoofing evidence rather than performed on generic acoustic embeddings (Zhang et al., 2024).

A separate methodological strand treats components as generator or pipeline modules. Source tracing uses a shared front-end with three attribute classifiers for conversion, speaker representation, and waveform generator, and fuses their spoof probabilities into a global spoof score (Zhu et al., 2022). MultiAPI Spoof similarly introduces API tracing and a local-attention variant of Nes2Net, called Nes2Net-LA, to capture fine-grained source-specific artifacts under seen and unseen API conditions (Zhang et al., 8 Dec 2025). These approaches are not speech–environment decomposition methods, but they instantiate the same general principle: anti-spoofing is improved when authenticity is resolved into interpretable subcomponents.

Component-aware work also builds on earlier global anti-spoofing systems. A Mel-spectrogram ResNet-34 system on ASVspoof 2019 treated spoofing as a single bona fide-versus-spoof problem and achieved EERs of 5.32% on logical-access evaluation and 5.74% on physical-access evaluation (P et al., 2020). Unified raw-waveform systems such as the Parallel Stacked Aggregation network narrowed the LA–PA “spoof gap” by decomposing signals into multiple temporal, spectral, and channel components even though the task remained binary (Khan et al., 2023). This suggests that modern Comp-Spoof methods inherit both their representations and their limitations from the global anti-spoofing literature.

5. Empirical findings

The central empirical result of Comp-Spoof research is that explicit component modeling improves difficult mixed-content classes, but only when separation and anti-spoofing are jointly optimized. On CompSpoof, a direct 5-class XLSR-AASIST baseline achieved overall dev/eval F1 scores of 0.840 and 0.827. A separation-enhanced framework without joint learning degraded markedly to 0.705 and 0.668, showing that naïve separation can remove or distort spoof cues. The full separation-enhanced joint learning system raised overall dev/eval F1 to 0.912 and 0.908 and produced large class-level gains, including dev F1 of 0.916 for spoof_bonafide, 0.874 for bonafide_spoof, and 0.903 for spoof_spoof (Zhang et al., 19 Sep 2025).

The larger CompSpoofV2 benchmark shows both the promise and difficulty of the problem. The ESDD2 baseline reports, on the validation set, Original EER 0.0031, Speech EER 0.0172, Env EER 0.3766, and F1-score 0.9462. On the evaluation set these become 0.0174, 0.1993, 0.4336, and 0.6224, and on the test set 0.0173, 0.1978, 0.4279, and 0.6327. Original-versus-mixed detection is therefore comparatively easy, speech-component spoof detection generalizes imperfectly to unseen generators, and environmental sound spoof detection is consistently the hardest subproblem (Zhang et al., 12 Jan 2026).

Temporal component studies reinforce this picture. On PartialSpoof, countermeasures trained on partially spoofed data reach approximately 0.35–0.86% EER, whereas systems trained only on fully spoofed ASVspoof 2019 LA audio yield approximately 3.6–4.7% EER on the same task, indicating a large gain from matched component-level supervision (Liu et al., 2024). Boundary-based localization remains challenging but effective: the ADD2022 waveform boundary detector reports 6.58% EER on the challenge test set while retaining the ability to locate manipulated clips (Cai et al., 2022). Spoof diarization is harder still. With multi-class countermeasure embeddings alone, the 3C line reports dev $s(t)$0 and $s(t)$1, but on the evaluation set these rise to 19.66% and 28.05%, underscoring the difficulty of open-set spoof-type diarization even under oracle cluster count and single-speaker assumptions (Zhang et al., 2024).

Fine-grained source attribution shows similar patterns. On MultiAPI Spoof, training with the new dataset reduces anti-spoofing EER on the benchmark itself to 0.56% for XLSR+Nes2Net-LA, and API tracing reaches Precision 0.950, Recall 0.923, and F1 0.936 on seen APIs in the evaluation set; the unseen class has Precision 0.972, Recall 0.520, and F1 0.678, revealing strong seen-class attribution but incomplete open-set recognition (Zhang et al., 8 Dec 2025). In logical-access source tracing, multi-task attribute classification achieved conversion accuracy 88.41%, speaker representation accuracy 51.46%, waveform generator accuracy 84.47%, and reduced ResNet34 spoof-detection EER from 0.066 to 0.012 in the reconstructed evaluation protocol (Zhu et al., 2022). Together these findings suggest that component labels can improve both detection and attribution, but that the gain is uneven across component types.

6. Open problems, controversies, and future directions

Several unresolved issues define the current frontier of Comp-Spoof. The first is generalization. CompSpoofV2 explicitly inserts unseen “New Generated” speech and environmental sound into evaluation and test, MultiAPI Spoof separates seen and unseen APIs, and spoof diarization reports a large gap between known and unknown spoof methods. A consistent cross-paper pattern is that open-set component attribution remains substantially harder than closed-set discrimination (Zhang et al., 12 Jan 2026).

The second is cue reliance. Partial spoofing analyses show that strong countermeasures often focus on transition-region artifacts rather than on the internal content of manipulated segments. Correct predictions correlate with high attention on transition regions, while misclassifications correlate with attention shifting back toward ordinary speech segments. This implies a specific vulnerability: smoother editing or better transition concealment could degrade current component-level detectors even if the manipulated content remains synthetic (Liu et al., 2024).

A third issue is label ambiguity. Neural audio codecs have a dual role: the same codec can be used benignly for compression and maliciously in generation pipelines. The codec study on an ASVspoof 5 extension argues that compression-oriented codecs should generally be treated as bona fide during training, whereas synthesis-oriented codecs may require different handling; it also concludes that more research is needed to disentangle codec artifacts from spoofness when a component excels at both compression and synthesis (Xiao et al., 18 Feb 2026). This is a fundamental Comp-Spoof controversy because it shows that “component manipulated” and “component malicious” are not always identical.

Finally, component-aware systems must be evaluated against active adversaries and deployment constraints. Practical attacks on integrated ASV+CM platforms achieve black-box success rates up to 93.57% and include targeted over-telephony-network attacks, demonstrating that joint optimization over authentication and countermeasure components is already feasible (Kassis et al., 2021). Broader surveys identify cross-dataset evaluation, adversarial defense, fairness, privacy, and deployment on resource-constrained devices as unresolved issues for audio anti-spoofing as a whole (Li et al., 2024). A plausible implication is that mature Comp-Spoof systems will need to combine component-specific supervision, open-set calibration, adversarial robustness, and deployment-aware evaluation rather than treating component decomposition as a stand-alone improvement (Ranjan et al., 2023).