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Cited by 101Speech Recognition and SynthesisDigital Media Forensic DetectionSpeech and Audio Processing

A Survey on Speech Deepfake Detection

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Li, Menglu, Ahmadiadli, Yasaman, Zhang, Xiao-Ping·2025·ACM Computing Surveys
DOI 10.1145/3714458
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Abstract

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 defenses 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.

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  • While existing surveys have extensively reviewed deepfake detection methods from theoretical and architectural perspectives, there remains a critical gap in systematic empirical evaluations of these methods under diverse evaluation protocols.

    “While existing surveys have extensively reviewed deepfake detection methods from theoretical and architectural perspectives [5-7, 15, 15, 25], there remains a critical gap in systematic empirical evaluations of these methods under diverse evaluation protocols.”

    From Deepfake detection across image, video, and audio: a comprehensive survey with empirical evaluation of generalization and robustness (2026) · cites this paper as [25]

    The Introduction and Conclusion say that existing speech-deepfake detection surveys primarily focus on model architectures, supporting the architectural part of the cited clause. They do not characterize those surveys as extensively reviewing methods from a theoretical perspective. The statement about a gap in empirical evaluations follows the citation and is not assessed as a claim attributed to this source. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Li, M., Ahmadiadli, Y., & Zhang, X.-P.
Affiliations
  • Toronto Metropolitan University (CA)
  • Tsinghua Shenzhen International Graduate School (CN)
ORCID
Venue
ACM Computing Surveys
Publisher
Association for Computing Machinery (ACM)
Published
2025-02-08
Volume
57
Issue
7
Pages
1-38
ISSN
  • 0360-0300 (ISSN-L)
  • 1557-7341
DOI
10.1145/3714458
OpenAlex
W4406803348
Cited by
101
References
19
Citations by year
2026: 50 · 2025: 45 · 2024: 6
FWCI
30.80
Type
journal article
Language
en
Open access
Licence
Locations
  • ACM Computing Surveys (publishedVersion, public-domain)
  • arXiv (Cornell University) (submittedVersion, public-domain)
  • arXiv (Cornell University)
Funders
  • Shenzhen Key Laboratory of Ubiquitous Data Enabling (ZDSYS20220527171406015)
  • Tsinghua Shenzhen International Graduate School–Shenzhen Pengrui Endowed Professorship Scheme of Shenzhen Pengrui Foundation
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
  • Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Keywords
speech deepfake detection, deepfakes, speech synthesis, misinformation, adversarial attacks, cross-dataset evaluation, evaluation metrics, deepfake datasets, open source tools

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