Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911339169972224 |
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| author | Samu, Most. Sharmin Sultana Islam, Md. Rakibul Hossain, Md. Zahid Bhuiyan, Md. Kamrozzaman Zaman, Farhad Uz |
| author_facet | Samu, Most. Sharmin Sultana Islam, Md. Rakibul Hossain, Md. Zahid Bhuiyan, Md. Kamrozzaman Zaman, Farhad Uz |
| contents | The rapid growth of speech synthesis and voice conversion systems has made deepfake audio a major security concern. Bengali deepfake detection remains largely unexplored. In this work, we study automatic detection of Bengali audio deepfakes using the BanglaFake dataset. We evaluate zeroshot inference with several pretrained models. These include Wav2Vec2-XLSR-53, Whisper, PANNsCNN14, WavLM and Audio Spectrogram Transformer. Zero-shot results show limited detection ability. The best model, Wav2Vec2-XLSR-53, achieves 53.80% accuracy, 56.60% AUC and 46.20% EER. We then f ine-tune multiple architectures for Bengali deepfake detection. These include Wav2Vec2-Base, LCNN, LCNN-Attention, ResNet18, ViT-B16 and CNN-BiLSTM. Fine-tuned models show strong performance gains. ResNet18 achieves the highest accuracy of 79.17%, F1 score of 79.12%, AUC of 84.37% and EER of 24.35%. Experimental results confirm that fine-tuning significantly improves performance over zero-shot inference. This study provides the first systematic benchmark of Bengali deepfake audio detection. It highlights the effectiveness of f ine-tuned deep learning models for this low-resource language. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_21702 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning Samu, Most. Sharmin Sultana Islam, Md. Rakibul Hossain, Md. Zahid Bhuiyan, Md. Kamrozzaman Zaman, Farhad Uz Sound Artificial Intelligence The rapid growth of speech synthesis and voice conversion systems has made deepfake audio a major security concern. Bengali deepfake detection remains largely unexplored. In this work, we study automatic detection of Bengali audio deepfakes using the BanglaFake dataset. We evaluate zeroshot inference with several pretrained models. These include Wav2Vec2-XLSR-53, Whisper, PANNsCNN14, WavLM and Audio Spectrogram Transformer. Zero-shot results show limited detection ability. The best model, Wav2Vec2-XLSR-53, achieves 53.80% accuracy, 56.60% AUC and 46.20% EER. We then f ine-tune multiple architectures for Bengali deepfake detection. These include Wav2Vec2-Base, LCNN, LCNN-Attention, ResNet18, ViT-B16 and CNN-BiLSTM. Fine-tuned models show strong performance gains. ResNet18 achieves the highest accuracy of 79.17%, F1 score of 79.12%, AUC of 84.37% and EER of 24.35%. Experimental results confirm that fine-tuning significantly improves performance over zero-shot inference. This study provides the first systematic benchmark of Bengali deepfake audio detection. It highlights the effectiveness of f ine-tuned deep learning models for this low-resource language. |
| title | Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2512.21702 |