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license: apache-2.0
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# ARFAKE: A Multi-Dialect Benchmark and Baselines for Arabic Spoof-Speech Detection

## Overview
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 .

This repository provides:
- The **ARFAKE dataset**, built on top of the **Casablanca speech corpus** (8 dialects, ~6 hours each).
- **Spoofed versions** generated using state-of-the-art TTS systems:
  - XTTS-v2
  - FishSpeech
  - ArTST
  - VITS
- **Baselines and evaluation pipeline** for detecting spoofed speech using both traditional ML and modern embedding-based models.

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## Key Features
- 📀 **Multi-dialect coverage**: Eight Arabic dialects, balanced across bonafide and spoofed samples.  
- 🎙️ **Spoofed data generation**: Using large-scale multilingual and Arabic-specific TTS models.  
- 🧪 **Detection baselines**:
  - MFCC + classical ML classifiers (SVM, Random Forest, etc.)
  - Embedding-based models using **HuBERT**, **Whisper**, and **Wav2Vec 2.0**
  - **RawNet2**, the ASVspoof benchmark system
- 🔍 **Evaluation metrics**:
  - **Equal Error Rate (EER)**
  - **Accuracy**
  - **Mean Opinion Score (MOS)** (via human ratings)
  - **Word Error Rate (WER)** (via Whisper-Large ASR)

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## Dataset
- **Source corpus**: [Casablanca dataset (2024)]
- **Size**: 54,413 utterances (~23k test samples, ~31k train samples)  
- **Composition**:
  - Bonafide (genuine) speech  
  - Spoofed speech from FishSpeech, XTTS-v2, and ArTST  
- **Dialectal coverage**: DZ, EG, JO, MA, MR, PS, AE, YE (ISO 3166-1 alpha-2 codes)  
- **Distribution**: (*see Figure 1 in paper*).

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## Baselines & Results
- **Embedding-based models** outperform traditional MFCC-based ML classifiers.  
- **Whisper-large** achieved the best detection performance (EER 6.92% on FishSpeech-generated data).  
- **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.  
- Classifiers trained on the combined dataset generalized well even to unseen TTS models like VITS.  

**Summary of Findings**:
- FishSpeech is the most realistic and difficult TTS system for Arabic spoofing.
- Combining spoofed data from multiple TTS models improves generalizability of detectors.
- Whisper-based detectors outperform MFCC-based baselines by a wide margin.

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## Usage
1. **Dataset Access**  
   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).  

2. **Training Baseline Models**  
   - Classical ML: Train SVM, Random Forest, etc. on MFCC features.  
   - Embedding-based: Use pre-trained HuBERT / Whisper / Wav2Vec encoders with classifier heads.  
   - Benchmark comparison with RawNet2.  

3. **Evaluation**  
   - Run detection and report **EER**, **Accuracy**, **MOS**, and **WER**.  
   - Use Whisper-Large for ASR-based evaluation.  

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## Citation

```bibtex
@misc{maged2025arfakemultidialectbenchmarkbaselines,
      title={ArFake: A Multi-Dialect Benchmark and Baselines for Arabic Spoof-Speech Detection}, 
      author={Mohamed Maged and Alhassan Ehab and Ali Mekky and Besher Hassan and Shady Shehata},
      year={2025},
      eprint={2509.22808},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2509.22808}, 
}