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license: apache-2.0 |
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# ARFAKE: A Multi-Dialect Benchmark and Baselines for Arabic Spoof-Speech Detection |
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## Overview |
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ARFAKE is the **first multi-dialect Arabic spoof-speech benchmark**, designed to evaluate and advance anti-spoofing systems for Arabic audio. With the rapid progress of generative text-to-speech (TTS) and voice-cloning models, distinguishing between real and synthetic speech has become increasingly challenging, especially for Arabic and its diverse dialects — a language family that has been underrepresented in previous deepfake detection . |
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This repository provides: |
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- The **ARFAKE dataset**, built on top of the **Casablanca speech corpus** (8 dialects, ~6 hours each). |
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- **Spoofed versions** generated using state-of-the-art TTS systems: |
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- XTTS-v2 |
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- FishSpeech |
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- ArTST |
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- VITS |
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- **Baselines and evaluation pipeline** for detecting spoofed speech using both traditional ML and modern embedding-based models. |
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--- |
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## Key Features |
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- 📀 **Multi-dialect coverage**: Eight Arabic dialects, balanced across bonafide and spoofed samples. |
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- 🎙️ **Spoofed data generation**: Using large-scale multilingual and Arabic-specific TTS models. |
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- 🧪 **Detection baselines**: |
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- MFCC + classical ML classifiers (SVM, Random Forest, etc.) |
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- Embedding-based models using **HuBERT**, **Whisper**, and **Wav2Vec 2.0** |
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- **RawNet2**, the ASVspoof benchmark system |
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- 🔍 **Evaluation metrics**: |
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- **Equal Error Rate (EER)** |
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- **Accuracy** |
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- **Mean Opinion Score (MOS)** (via human ratings) |
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- **Word Error Rate (WER)** (via Whisper-Large ASR) |
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--- |
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## Dataset |
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- **Source corpus**: [Casablanca dataset (2024)] |
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- **Size**: 54,413 utterances (~23k test samples, ~31k train samples) |
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- **Composition**: |
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- Bonafide (genuine) speech |
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- Spoofed speech from FishSpeech, XTTS-v2, and ArTST |
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- **Dialectal coverage**: DZ, EG, JO, MA, MR, PS, AE, YE (ISO 3166-1 alpha-2 codes) |
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- **Distribution**: (*see Figure 1 in paper*). |
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--- |
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## Baselines & Results |
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- **Embedding-based models** outperform traditional MFCC-based ML classifiers. |
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- **Whisper-large** achieved the best detection performance (EER 6.92% on FishSpeech-generated data). |
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- **FishSpeech** produced the most challenging spoofed samples, with the highest MOS (3.72/5) and lowest WER, making it harder to detect than XTTS-v2, ArTST, or VITS. |
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- Classifiers trained on the combined dataset generalized well even to unseen TTS models like VITS. |
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**Summary of Findings**: |
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- FishSpeech is the most realistic and difficult TTS system for Arabic spoofing. |
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- Combining spoofed data from multiple TTS models improves generalizability of detectors. |
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- Whisper-based detectors outperform MFCC-based baselines by a wide margin. |
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--- |
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## Usage |
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1. **Dataset Access** |
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We uploaded the dataset, you can find use merge_training_set to train your model and merge_test_set (in-domain) ,Vits-spoofed (out-domain). |
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2. **Training Baseline Models** |
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- Classical ML: Train SVM, Random Forest, etc. on MFCC features. |
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- Embedding-based: Use pre-trained HuBERT / Whisper / Wav2Vec encoders with classifier heads. |
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- Benchmark comparison with RawNet2. |
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3. **Evaluation** |
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- Run detection and report **EER**, **Accuracy**, **MOS**, and **WER**. |
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- Use Whisper-Large for ASR-based evaluation. |
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--- |
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## Citation |
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```bibtex |
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@misc{maged2025arfakemultidialectbenchmarkbaselines, |
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title={ArFake: A Multi-Dialect Benchmark and Baselines for Arabic Spoof-Speech Detection}, |
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author={Mohamed Maged and Alhassan Ehab and Ali Mekky and Besher Hassan and Shady Shehata}, |
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year={2025}, |
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eprint={2509.22808}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2509.22808}, |
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} |
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