ICLAD: In-Context Learning with Comparison-Guidance for Audio Deepfake Detection (2026.findings-acl)
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| Challenge: | Audio deepfake detection systems do not generalize well to realistic in-the-wild deepfakkes. |
| Approach: | They propose a novel In-Context Learning paradigm with comparison-guidance for Audio Deepfake detection framework that uses audio language models for training-free generalization to unseen deepfakes. |
| Outcome: | The proposed framework improves macro F1 over specialized detectors on in-the-wild datasets with up to 2 relative improvement over existing models. |
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| Challenge: | Existing algorithms for audio deepfake detection are based on layer-wise analysis of self-supervised learning (SSL) models. |
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Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake (2024.findings-naacl)
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| Challenge: | a recent study has focused on audio deepfake detection (ADD) due to its ability to impersonate and share false, often malicious information. |
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Jun Xue, Zhuolin Yi, Yihuan Huang, Yanzhen Ren, Yujie Chen, Cunhang Fan, Zicheng Su, Yongcheng Zhang, Bo Cai
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| Challenge: | In-context learning has shown high efficacy in several NLP tasks, especially in few-shot settings. |
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XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark (2026.findings-eacl)
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Ioan-Paul Ciobanu, Andrei-Iulian Hîji, Nicolae Catalin Ristea, Paul Irofti, Cristian Rusu, Radu Tudor Ionescu
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A Survey to Recent Progress Towards Understanding In-Context Learning (2025.findings-naacl)
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| Challenge: | Existing speech deepfake datasets are limited in scale and diversity, making it challenging to train models that can generalize well to unseen deepfakkes. |
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Deepfake Defense: Constructing and Evaluating a Specialized Urdu Deepfake Audio Dataset (2024.findings-acl)
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Sheza Munir, Wassay Sajjad, Mukeet Raza, Emaan Abbas, Abdul Hameed Azeemi, Ihsan Ayyub Qazi, Agha Ali Raza
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Indic-CodecFake meets SATYAM: Towards Detecting Neural Audio Codec Synthesized Speech Deepfakes in Indic Languages (2026.findings-acl)
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| Challenge: | Speech deepfakes are highly realistic and can generate a few seconds of recorded speech. |
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