Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection (2025.findings-naacl)
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| Challenge: | Existing algorithms for audio deepfake detection are based on layer-wise analysis of self-supervised learning (SSL) models. |
| Approach: | They conduct a layer-wise analysis of self-supervised learning (SSL) models for audio deepfake detection across diverse contexts. |
| Outcome: | The proposed models achieve competitive equal error rate (EER) scores even when employing a reduced number of layers. |
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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. |
| Approach: | They propose to use multilingual speech Pre-Trained models for Audio deepfake detection (ADD) they propose to combine models with existing models to achieve better ADD detection . |
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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. |
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Revealing the Truth with ConLLM for Detecting Multi-Modal Deepfakes (2026.findings-eacl)
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Gautam Siddharth Kashyap, Harsh Joshi, Niharika Jain, Ebad Shabbir, Jiechao Gao, Nipun Joshi, Usman Naseem
| Challenge: | Existing methods for deepfake detection suffer from two limitations: modality fragmentation and shallow inter-modal reasoning. |
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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
| Challenge: | Automatic speaker verification systems are facing escalating challenges due to deepfake attacks. |
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SpeechFake: A Large-Scale Multilingual Speech Deepfake Dataset Incorporating Cutting-Edge Generation Methods (2025.acl-long)
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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. |
| Approach: | They propose a large-scale speech deepfake dataset that includes over 3 million deepfak samples, totaling more than 3,000 hours of audio, generated using 40 different speech synthesis tools. |
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Self-supervised Representation Learning for Speech Processing (2022.naacl-tutorials)
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Hung-yi Lee, Abdelrahman Mohamed, Shinji Watanabe, Tara Sainath, Karen Livescu, Shang-Wen Li, Shu-wen Yang, Katrin Kirchhoff
| Challenge: | Self-supervised representation learning (SSL) uses proxy supervised learning tasks to obtain training data from unlabeled corpora. |
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Cross-Domain Audio Deepfake Detection: Dataset and Analysis (2024.emnlp-main)
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| Challenge: | Existing audio deepfake detection datasets are outdated and lack generalization capabilities. |
| Approach: | They construct a new cross-domain audio deepfake detection dataset comprising over 300 hours of speech data that is generated by five advanced zero-shot TTS models. |
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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
| Challenge: | Recent advances in audio generation led to an increasing number of deepfakes . however, these methods are typically tested in an in-domain setup . |
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When depth is redundant: Efficient transformer-based speech anti-spoofing (2026.findings-acl)
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| Challenge: | Existing anti-spoofing countermeasures exhibit limited generalization to unseen spoof attacks, especially in out-of-domain evaluation settings. |
| Approach: | They propose a training strategy that aligns shallow and intermediate representations with those of the final transformer layer for speech deepfake detection. |
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Detecting deepfakes and false ads through analysis of text and social engineering techniques (2025.coling-main)
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| Challenge: | Existing deepfake detection algorithms focus on technical analysis of video and audio . authors examine stylistic inconsistencies and manipulative language patterns . |
| Approach: | They propose a method that emphasizes the analysis of text-based transcripts . they examine stylistic inconsistencies and manipulative language patterns . |
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