TeleAntiFraud-28k 📞🛡️

🎉 News

🎊 Our paper has been accepted by ACM MM 2025!

TeleAntiFraud-28k is the first open-source audio-text slow-thinking dataset specifically designed for automated telecom fraud analysis. This dataset integrates audio signals with reasoning-oriented textual analysis, providing high-quality multimodal training data for telecom fraud detection research. 🔍💡

📊 Dataset Overview

• Total Samples: 28,511 rigorously processed speech-text pairs 📋
• Total Audio Duration: 307 hours ⏱️
• Unique Feature: Detailed annotations for fraud reasoning 🧠
• Task Categories: Scenario classification, fraud detection, fraud type classification 🎯

🏗️ Dataset Construction Strategies

1. 🔒 Privacy-preserved Text-Truth Sample Generation

• Using ASR-transcribed call recordings (with anonymized original audio)
• Ensuring real-world consistency through TTS model regeneration
• Strict adherence to privacy protection standards

2. 🚀 Semantic Enhancement

• LLM-based self-instruction sampling on authentic ASR outputs
• Expanding scenario coverage to improve model generalization
• Enriching the diversity of conversational contexts

3. 🤖 Multi-agent Adversarial Synthesis

• Simulation of emerging fraud tactics
• Generation through predefined communication scenarios and fraud typologies
• Enhancing dataset adaptability to new fraud techniques

🎯 TeleAntiFraud-Bench

We have constructed TeleAntiFraud-Bench, a standardized evaluation benchmark comprising proportionally sampled instances from TeleAntiFraud-28k, to facilitate systematic testing of model performance and reasoning capabilities on telecom fraud detection tasks. 📐✅

🤖 Model Contribution

We contribute a production-optimized supervised fine-tuning (SFT) model based on Qwen2-Audio, trained on the TeleAntiFraud training set. 🎨⚡

📝 Examples

Explore our dataset examples to better understand the telecom fraud detection capabilities: 👀

• Case 1: Normal Conversation Analysis - Detailed analysis of a legitimate phone conversation ✅
• Case 2: Fraud Conversation Analysis - Step-by-step reasoning for detecting a fraudulent call ⚠️
• Evaluation Sample - Representative sample from our evaluation benchmark 📊
• Model Output: Normal Conversation - Our model's reasoning process on a legitimate call 🤖✅
• Model Output: Fraud Detection - Model's analysis and detection of a fraudulent call 🤖⚠️

🛠️ Multi-Agent Data Collection

To collect fraudulent conversation data: 💼

1. Insert your API key in multi-agents-tools/AntiFraudMatrix/main.py (uses SiliconFlow API key) 🔑
2. Run the following command to generate fraudulent dialog text:

   python multi-agents-tools/AntiFraudMatrix/main.py

3. Results will be saved in the result directory 📁

For normal conversation data: 💬

• Use multi-agents-tools/AntiFraudMatrix-normal/main.py following the same process

🎙️ Voice Synthesis with ChatTTS

To synthesize speech from the collected text: 🔊

1. Install the necessary dependencies 📦

2. Run the API server:

   fastapi dev ChatTTS/examples/api/main_new_new.py --host 0.0.0.0 --port 8006

3. Use any of the scripts in ChatTTS/examples/api/normal_run*.sh or ChatTTS/examples/api/run*.sh 🚀

   Modify the port in these scripts if needed, then run:

   bash ChatTTS/examples/api/run*.sh

🌟 Open-Source Resources

• TeleAntiFraud-28k dataset 📚
• TeleAntiFraud-Bench evaluation benchmark 🏆
• Data processing framework (supporting community-driven dataset expansion) 🔧
• TeleAntiFraud-Qwen2-Audio SFT model 🤖

🎯 Key Contributions

1. Establishing a foundational framework for multimodal anti-fraud research 🏗️
2. Addressing critical challenges in data privacy and scenario diversity 🔐
3. Providing high-quality training data for telecom fraud detection 📈
4. Open-sourcing data processing tools to enable community collaboration 🤝

🙏 Acknowledgements

We would like to express our sincere gratitude to all the organizations and individuals who have provided invaluable support throughout this project: ❤️

• China Mobile Internet Company (中移互联网) - For their industry expertise and technical guidance 🏢
• Intern Community (书生社区) - For their open-source ecosystem support and collaboration 🌍
• ModelScope Community (魔搭社区) - For their platform support and community resources 🎪
• SmartFlowAI Community (机智流社区) - For their technical contributions and collaborative efforts 💡
• Control-derek - For his technical expertise and valuable contributions 👨‍💻
• vansin - For his dedicated support and assistance 🤝
• Jintao-Huang - For his valuable suggestions and contributions 💭

Their contributions have been instrumental in making this project a success and advancing the field of telecom fraud detection research. 🚀

📄 Citation

@inproceedings{Ma2025TeleAntiFraud28kAA,
  title={TeleAntiFraud-28k: An Audio-Text Slow-Thinking Dataset for Telecom Fraud Detection},
  author={Zhiming Ma and Peidong Wang and Minhua Huang and Jingpeng Wang and Kai Wu and Xiangzhao Lv and Yachun Pang and Yin Yang and Wenjie Tang and Yuchen Kang},
  year={2025},
  url={https://api.semanticscholar.org/CorpusID:277467703}
}