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Half-Truth: A Partially Fake Audio Detection Dataset

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arxiv 2104.03617 v2 pith:U6QOHTHE submitted 2021-04-08 cs.SD cs.AIcs.CLeess.AS

Jiangyan Yi , Ye Bai , Jianhua Tao , Haoxin Ma , Zhengkun Tian , Chenglong Wang , Tao Wang , Ruibo Fu This is my paper
classification cs.SDcs.AIcs.CLeess.AS
keywords audiofakedatasetdetectionspeechpartiallydatasetshalf-truth
verification ladder T0 review T1 audit T2 compute T3 formal
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Diverse promising datasets have been designed to hold back the development of fake audio detection, such as ASVspoof databases. However, previous datasets ignore an attacking situation, in which the hacker hides some small fake clips in real speech audio. This poses a serious threat since that it is difficult to distinguish the small fake clip from the whole speech utterance. Therefore, this paper develops such a dataset for half-truth audio detection (HAD). Partially fake audio in the HAD dataset involves only changing a few words in an utterance.The audio of the words is generated with the very latest state-of-the-art speech synthesis technology. We can not only detect fake uttrances but also localize manipulated regions in a speech using this dataset. Some benchmark results are presented on this dataset. The results show that partially fake audio presents much more challenging than fully fake audio for fake audio detection. The HAD dataset is publicly available: https://zenodo.org/records/10377492.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ThinkOmni: A Reasoning-Driven Omni-Modal LLM Framework for Audio Forgery Detection and Localization

    cs.SD 2026-07 conditional novelty 6.0 of 10

    ThinkOmni uses an omni-modal LLM with explicit forensic chain-of-thought to detect and localize audio forgery, and reports strong cross-dataset results.

  2. NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation

    cs.CL 2025-09 conditional novelty 6.0 of 10

    NE-PADD combines SpeechNER attention with a deepfake detector via fusion or transfer, reaching 7.89% EER on the new PartialSpoof-NER benchmark.

  3. Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization

    cs.CV 2025-06 conditional novelty 6.0 of 10

    UniCaCLF trains temporal instant features to separate real from forged moments relative to each sample's global context, achieving state-of-the-art temporal forgery localization on five public datasets.

  4. When Fine-Tuning is Not Enough: Lessons from HSAD on Hybrid and Adversarial Audio Spoof Detection

    cs.SD 2025-09 reject novelty 4.0 of 10

    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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