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A Survey on Speech Deepfake Detection
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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.
Forward citations
Cited by 6 Pith papers
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Performance and Complexity Trade-off Optimization of Speech Models During Training
By turning each layer's width into a continuous, noise-smoothed parameter, the authors train speech models whose sizes shrink during training, reducing FLOPs and size by roughly 80–90% in their case studies.
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Fake-Mamba: Real-Time Speech Deepfake Detection Using Bidirectional Mamba as Self-Attention's Alternative
Fake-Mamba reports EERs of 0.97%, 1.74%, and 5.85% on three speech deepfake benchmarks, but the provided full text is an unrelated paper, so the claims cannot be verified.
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CAD: A General Multimodal Framework for Video Deepfake Detection via Cross-Modal Alignment and Distillation
CAD combines cross-modal lip-speech alignment with per-modality artifact distillation and reports 99.96% AUC on IDForge-v2, with strong cross-dataset results.
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Hybrid Audio Detection Using Fine-Tuned Audio Spectrogram Transformers: A Dataset-Driven Evaluation of Mixed AI-Human Speech
Fine-tuned Audio Spectrogram Transformers achieve 97% accuracy on a new, unreleased hybrid human-AI speech dataset, but the evaluation is in-domain and internally inconsistent.
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Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection
Across six self-supervised speech models and ten deepfake datasets, the first 4-12 transformer layers match full-model fake audio detection performance, reducing parameters by at least half.
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When Fine-Tuning is Not Enough: Lessons from HSAD on Hybrid and Adversarial Audio Spoof Detection
A new hybrid spoofed-audio benchmark is claimed to show that fine-tuning on it reaches 97%+ accuracy, but the reported numbers are internally inconsistent.
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