Zero-Day Audio Deepfake Detection

• Zero-day audio deepfake detection is a technique that distinguishes synthetic speech generated by unseen algorithms using open-set and anomaly detection methods.
• It leverages diverse feature representations such as MFCC, LFCC, and phase cues to capture subtle synthesis artifacts and enhance robustness.
• The field emphasizes multi-domain feature fusion and retrieval-based strategies to improve detection resilience against novel voice synthesis attacks.

Zero-day audio deepfake detection is the discipline of distinguishing synthetic, manipulated, or generated audio—especially speech—produced by previously unseen algorithms or pipelines. Unlike closed-set detection, which assumes the generative process is known and represented in the training data, zero-day detection must robustly generalize to arbitrary and novel attacks, including those launched shortly after the release of new voice synthesis, conversion, or reconstruction methods. Key strategies leverage generalized feature representations, robust modeling of real audio, multi-domain data, and anomaly or open-set learning paradigms.

1. Theoretical Foundations and Detection Paradigms

Zero-day detection in audio deepfakes fundamentally differs from conventional supervised classification. Conventional binary classifiers often overfit to artifacts specific to the training set of known generative models, resulting in poor generalization when the underlying synthesis technique changes. Several paradigms address this:

• Open-set and One-class Modeling: Methods such as OC-Softmax and speaker verification–based approaches explicitly model the manifold of real (or authentic) audio, treating all deviations as suspect (Pianese et al., 2022, Xie et al., 5 Jun 2024).
• Identity-Mismatch and Consistency Approaches: Detection by verifying consistency across biometric identity (e.g., speaker embeddings), or across modalities (audio-visual), are robust to unseen attacks (Cozzolino et al., 2022, Li et al., 12 Jun 2024). If audio is manipulated, its representation is inconsistent with the known identity or the co-occurring video.
• Feature Fusion and Ensemble Strategies: Combining representations from multiple domains (timbral, spectral, phonetic) or several model predictions enables resilience to new artifacts introduced by novel deepfake methods (Krishnan et al., 2023, Pham et al., 1 Jul 2024).
• Retrieval-Augmented Anomaly Detection: Training-free architectures employ feature space retrieval against knowledge bases of real and observed-fake samples, relying on similarity metrics and ensemble voting to offer prompt zero-day response (Liu et al., 26 Sep 2025).

2. Feature Representations for Robustness

The discriminative power of an audio deepfake detector under zero-day conditions is tightly linked to the richness, diversity, and invariance of its feature sets.

• Cepstral and Spectro-Temporal Features: Mel-frequency cepstral coefficients (MFCC), linear-frequency cepstral coefficients (LFCC), Chroma-STFT, and delta features provide multi-faceted descriptions of time-frequency structure (Frank et al., 2021, Krishnan et al., 2023). LFCCs, in particular, preserve high-frequency details characteristic of synthesis artifacts (Frank et al., 2021).
• Phase and Fundamental Frequency (F0) Cues: Real and imaginary parts of the STFT, along with F0 energy, capture aspects of natural voice production that are challenging for generative models to imitate. A two-stage fusion of F0, phase, and magnitude yields low EERs across known and unknown attacks (Xue et al., 2022).
• Self-Supervised and Pretrained Encoders: Models like Whisper encode audio using representations robustly learned from hundreds of thousands of hours of diverse speech, offering strong baseline invariance to data distribution shifts (Kawa et al., 2023, Pham et al., 1 Jul 2024).
• Segmental and Articulatory Features: Vowel formant trajectories and segmental phonetic features are closely tied to physical speech mechanisms, making them difficult for deepfake models to authentically recreate, as evidenced in forensic scenarios (Yang et al., 20 May 2025).
• Voice Profile Attributes: Metadata and embeddings capturing age, gender, emotion, and voice quality help to identify inconsistencies in speaker traits, which are often overlooked in synthetic audio (Liu et al., 26 Sep 2025).

3. Benchmarks, Datasets, and Evaluation Protocols

The efficacy of zero-day detection approaches is determined by evaluation on appropriate, diverse, and up-to-date datasets.

Dataset	Composition/Scope	Notable Utility
WaveFake	117,985 clips, 2 languages, 5 TTS pipelines	Out-of-distribution, cross-lingual paper
AUDETER	3M clips from 21 synthesis models, 4,500+ hours	Largest, high diversity, open-world eval
Cross-Domain ADD	>300h, 5 zero-shot TTS models, 9 attack augmentations	Cross-model & few-shot analysis
FakeSound	General-audio deepfakes with localization labels	Region-level detection, human benchmarking

Large, multi-domain datasets such as AUDETER facilitate the training of "generalist" detectors. These datasets intentionally include multiple TTS/vocoder systems and real-world voices, making them suitable testbeds for open-world and zero-day evaluation (Wang et al., 4 Sep 2025). The inclusion of paired real/fake audio (identical text, different synthesis) exposes nuanced synthesis artifacts.

4. Modeling Strategies for Zero-Day Resilience

Recent models adopt various strategies to explicitly promote generalization:

• Multipath Feature Networks: MFAAN (Krishnan et al., 2023) employs parallel CNNs, each ingesting a different feature representation (MFCC, LFCC, Chroma), and merges their outputs before a dense classifier layer. This redundancy helps detect new artifact types introduced by novel synthesis.
• Raw End-to-End Models: RawNetLite (Pierno et al., 29 Apr 2025) forgoes handcrafted features, using convolutional and bidirectional GRU layers to process raw waveform input. Domain-mixed and augmentative training strengthens OOD generalization.
• Contrastive or One-class Learning: POI-Forensics (Cozzolino et al., 2022) and Real Emphasis/Fake Dispersion (REFD) (Xie et al., 5 Jun 2024) train exclusively on bona fide data or employ contrastive objectives to learn identity or class-localized embeddings, flagging any mismatch or dispersion as indicative of manipulation.
• Retrieval and Profile Matching: A training-free framework (Liu et al., 26 Sep 2025) retrieves k-nearest feature neighbors from a pre-built knowledge base, using ensemble votes across CM and profile features to approximate zero-day detection without retraining.
• Attack-Augmented and Codec Robustness Training: Models are trained with a diverse suite of attacks—including codec compression, noise, and reverberation—to force learning of invariant features and downweight overfitting to surface artifacts (Li et al., 7 Apr 2024, Li et al., 14 Sep 2024, Xie et al., 20 Aug 2024).
• Audio-Visual Consistency: For fake video/audio detection, content (text), semantic, and temporal consistencies between the ASR and VSR outputs flag inconsistencies without requiring fake training examples (Li et al., 12 Jun 2024).

5. Limitations and Practical Challenges

Zero-day deepfake detection is subject to a range of ongoing technical and practical challenges:

• Overfitting and Domain Shift: Neural architectures, although performant in-distribution, risk catastrophic performance drops on new synthesis techniques or under environmental variations. Combining multiple models/representations, augmentative strategies, and domain-mixed training are invaluable but not panaceas (Pierno et al., 29 Apr 2025, Wang et al., 4 Sep 2025).
• Adversarial and Codec-Based Evasions: DNN-based compression (e.g., Encodec, MP3) severely impairs detection, erasing artifact cues. Techniques must be evolved to maintain invariance under such transformations (Li et al., 7 Apr 2024, Xie et al., 20 Aug 2024, Li et al., 14 Sep 2024). Codec-augmented models show promise here.
• Feature Limitations: Some classical features (e.g., global long-term formant distributions) lack discriminative power for fine-grained or highly naturalistic deepfakes, necessitating further focus on segmental, dynamic, or multi-modal cues (Yang et al., 20 May 2025).
• Human Limitations: Human listeners perform near chance in detecting general-audio deepfakes in challenging scenarios, emphasizing the need for automated, explainable systems (Xie et al., 12 Jun 2024).
• Privacy Requirements: Privacy-preserving detection (e.g., SafeEar (Li et al., 14 Sep 2024)), which avoids access to semantic speech content by exclusively leveraging acoustic tokens, is an emerging paradigm—balancing detection performance with content confidentiality.

6. Open Research Problems and Future Directions

Several active areas of research are repeatedly highlighted:

• Dataset Expansion and Diversity: Continued development and open release of broad, current datasets (e.g., AUDETER, FakeSound) is paramount for both model development and evaluation (Wang et al., 4 Sep 2025, Xie et al., 12 Jun 2024).
• Few-Shot and Continual Learning: Adapting generalist models to unseen domains or synthesis methods with minimal supervision (as in cross-domain ADD fine-tuning for one minute) is critical for real-world utility (Li et al., 7 Apr 2024).
• Hybrid, Adaptive, and Fusion Models: Systems combining segmental features, global statistics, deep embeddings, voice profiles, and multi-modal cues appear especially robust (Pham et al., 1 Jul 2024, Krishnan et al., 2023, Liu et al., 26 Sep 2025).
• Explainability and Forensic Utility: Segmental, interpretable features enhance scientific and legal rigor (Yang et al., 20 May 2025). Future detection systems must strike a balance between high discrimination, transparency, and operational feasibility.
• Anomaly and Open-Set Detection: Future solutions are likely to more directly embrace open-set learning, verification, and anomaly detection—rather than closed-set classification—so as to natively accommodate the zero-day threat (Wang et al., 10 Sep 2025, Xie et al., 5 Jun 2024).
• Integration with Privacy: Frameworks such as SafeEar demonstrate the feasibility of masking semantic information, pointing to broader adoption as privacy becomes a key constraint (Li et al., 14 Sep 2024).

7. Real-World Impact and Deployment Considerations

Zero-day audio deepfake detection underpins critical applications: protecting voice authentication systems, countering fraud and misinformation, law enforcement forensics, media authenticity assessment, and privacy-focused deployments where semantic content must remain obscured. Achieving reliable, generalist, and explainable detection in the presence of rapidly advancing and diversifying synthesis technologies remains a central objective for the field.

In summary, the zero-day audio deepfake detection landscape is driven by representational diversity, open-set learning, domain robustness, privacy respect, and the continuous evolution of synthesis and detection algorithms. Collaborative benchmark development, adaptive and hybrid model architectures, and scientific explainability are central to countering the growing and shifting threat of synthetic audio forgeries.