Papers with deepfake detection
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. |
SpeechLLM-as-Judges: Towards General and Interpretable Speech Quality Evaluation (2026.acl-long)
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Hui Wang, Jinghua Zhao, Yifan Yang, Shujie Liu, Junyang Chen, Yanzhe Zhang, Shiwan Zhao, Jinyu Li, Jiaming Zhou, Haoqin Sun, Yan Lu, Yong Qin
| Challenge: | Existing methods for evaluating the perceptual quality of synthetic speech are limited due to the complexity of perceptual quality factors and the diversity of speech generation tasks. |
| Approach: | They propose a new paradigm for enabling large language models to conduct structured speech quality evaluation using a large-scale dataset. |
| Outcome: | The proposed model performs well across tasks and languages. |
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 . |
| Outcome: | The proposed method improves the accuracy of distinguishing between fake and real materials. |
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. |
| Approach: | They propose a Urdu deepfake audio dataset for deepfak detection focusing on two spoofing attacks – Tacotron and VITS TTS. |
| Outcome: | The proposed dataset evaluates two spoofing attacks in Urdu with a human evaluation to gauge whether people are able to distinguish deepfake audios from real (bonafide) audios. |