# A Survey on Speech Deepfake Detection

arXiv: https://arxiv.org/abs/2404.13914 · HTML: https://mlsanity.com/paper/2404.13914/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2024.

## Built on by (most cited first, 27 of 27)

- [DFADD: The Diffusion and Flow-Matching Based Audio Deepfake Dataset](https://mlsanity.com/paper/2409.08731/) (2024)
- [Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing](https://mlsanity.com/paper/2504.05657/) (2025)
- [SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detection](https://mlsanity.com/paper/2407.18517/) (2024)
- [CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation](https://mlsanity.com/paper/2505.15233/) (2025)
- [TRACE: Training-Free Partial Audio Deepfake Detection via Embedding Trajectory Analysis of Speech Foundation Models](https://mlsanity.com/paper/2604.01083/) (2026)
- [Analyzing the Impact of Splicing Artifacts in Partially Fake Speech Signals](https://mlsanity.com/paper/2408.13784/) (2024)
- [A Data-Driven Diffusion-based Approach for Audio Deepfake Explanations](https://mlsanity.com/paper/2506.03425/) (2025)
- [Attention-based Mixture of Experts for Robust Speech Deepfake Detection](https://mlsanity.com/paper/2509.17585/) (2025)
- [SpMis: An Investigation of Synthetic Spoken Misinformation Detection](https://mlsanity.com/paper/2409.11308/) (2024)
- [Generalizable Speech Deepfake Detection via Information Bottleneck Enhanced Adversarial Alignment](https://mlsanity.com/paper/2509.23618/) (2025)
- [XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection](https://mlsanity.com/paper/2601.02944/) (2026)
- [Disentangling Speaker Traits for Deepfake Source Verification via Chebyshev Polynomial and Riemannian Metric Learning](https://mlsanity.com/paper/2603.21875/) (2026)
- [Leave No Stone Unturned: Uncovering Holistic Audio-Visual Intrinsic Coherence for Deepfake Detection](https://mlsanity.com/paper/2603.23960/) (2026)
- [Alethia: A Foundational Encoder for Voice Deepfakes](https://mlsanity.com/paper/2605.00251/) (2026)
- [Teffic-Audio: Tell Fact from Fiction](https://mlsanity.com/paper/2607.28351/) (2026)
- [REIMU: Efficient Heterogeneous Hierarchical Reasoning for SSL-Based Speech Deepfake Detection](https://mlsanity.com/paper/2608.00857/) (2026)
- [Towards Generalized Source Tracing for Codec-Based Deepfake Speech](https://mlsanity.com/paper/2506.07294/) (2025)
- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)
- [Can Current Detectors Catch Face-to-Voice Deepfake Attacks?](https://mlsanity.com/paper/2510.21004/) (2025)
- [Don't Guess, Escalate: Towards Explainable Uncertainty-Calibrated AI Forensic Agents](https://mlsanity.com/paper/2512.16614/) (2025)
- [Performance and Complexity Trade-off Optimization of Speech Models During Training](https://mlsanity.com/paper/2601.13704/) (2026)
- [AI Security Research Should Better Incentivize Defense Research](https://mlsanity.com/paper/2605.23448/) (2026)
- [Traceback Translators Against Forgetting in Continual Fake Speech Detection](https://mlsanity.com/paper/2607.12569/) (2026)
- [Explainable-by-Design Audio Deepfake Detection via Wiener-Hopf Linear Prediction](https://mlsanity.com/paper/2607.12584/) (2026)
- [Optimal transport meets speech: a tutorial review](https://mlsanity.com/paper/2609.31787/) (2026)
- [SE-ADD: Self-Evolving Audio Deepfake Detection with Mistake-Driven Supervision](https://mlsanity.com/paper/2609.39679/) (2026)
- [Adaptive Compression and Targeted Perturbation: A Unified Framework for Generalized Audio Deepfake Detection](https://mlsanity.com/paper/2610.16049/) (2026)

## Abstract (arXiv)

The availability of smart devices leads to an exponential increase in multimedia content. However, advancements in deep learning have also enabled the creation of highly sophisticated Deepfake content, including speech Deepfakes, which pose a serious threat by generating realistic voices and spreading misinformation. To combat this, numerous challenges have been organized to advance speech Deepfake detection techniques. In this survey, we systematically analyze more than 200 papers published up to March 2024. We provide a comprehensive review of each component in the detection pipeline, including model architectures, optimization techniques, generalizability, evaluation metrics, performance comparisons, available datasets, and open source availability. For each aspect, we assess recent progress and discuss ongoing challenges. In addition, we explore emerging topics such as partial Deepfake detection, cross-dataset evaluation, and defences against adversarial attacks, while suggesting promising research directions. This survey not only identifies the current state of the art to establish strong baselines for future experiments but also offers clear guidance for researchers aiming to enhance speech Deepfake detection systems.