TeleAntiFraud-28k: A Audio-Text Slow-Thinking Dataset for Telecom Fraud
  Detection
The detection of telecom fraud faces significant challenges due to the lack
of high-quality multimodal training data that integrates audio signals with
reasoning-oriented textual analysis. To address this gap, we present
TeleAntiFraud-28k, the first open-source audio-text slow-thinking dataset
specifically designed for automated telecom fraud analysis. Our dataset is
constructed through three strategies: (1) Privacy-preserved text-truth sample
generation using automatically speech recognition (ASR)-transcribed call
recordings (with anonymized original audio), ensuring real-world consistency
through text-to-speech (TTS) model regeneration; (2) Semantic enhancement via
large language model (LLM)-based self-instruction sampling on authentic ASR
outputs to expand scenario coverage; (3) Multi-agent adversarial synthesis that
simulates emerging fraud tactics through predefined communication scenarios and
fraud typologies. The generated dataset contains 28,511 rigorously processed
speech-text pairs, complete with detailed annotations for fraud reasoning. The
dataset is divided into three tasks: scenario classification, fraud detection,
fraud type classification. Furthermore, we construct TeleAntiFraud-Bench, a
standardized evaluation benchmark comprising proportionally sampled instances
from the dataset, to facilitate systematic testing of model performance on
telecom fraud detection tasks. We also contribute a production-optimized
supervised fine-tuning (SFT) model trained on hybrid real/synthetic data, while
open-sourcing the data processing framework to enable community-driven dataset
expansion. This work establishes a foundational framework for multimodal
anti-fraud research while addressing critical challenges in data privacy and
scenario diversity. The project will be released at
https://github.com/JimmyMa99/TeleAntiFraud.
67eb5116d3a707c0a5b02bd1
Zhiming Ma
extracted_pending
2025-04-01T02:36:07.612Z
64a0ed5ed5374ca472cfb0ac
https://cdn-avatars.huggingface.co/v1/production/uploads/64a0ed5ed5374ca472cfb0ac/n_wXamXfR_PPn0hRbnR1X.jpeg
ZhimingMa
user
JimmyMa99
67eb5116d3a707c0a5b02bd2
Peidong Wang
extracted_pending
2025-04-01T02:36:07.612Z
6385f7b969634850f8ddd541
https://cdn-avatars.huggingface.co/v1/production/uploads/1669723465271-noauth.png
Peidong Wang
user
WDong
67eb5116d3a707c0a5b02bd3
Minhua Huang
admin_assigned
2025-04-01T08:02:15.224Z
6466d57bf3e78d1d6be0505c
/avatars/9659b7d0f6fa51efc127afb7a1ba14b1.svg
HuangMinhua
user
HuangMinhua
67eb5116d3a707c0a5b02bd4
Jingpeng Wang
67eb5116d3a707c0a5b02bd5
Kai Wu
67eb5116d3a707c0a5b02bd6
Xiangzhao Lv
67eb5116d3a707c0a5b02bd7
Yachun Pang
67eb5116d3a707c0a5b02bd8
Yin Yang
67eb5116d3a707c0a5b02bd9
Wenjie Tang
67eb5116d3a707c0a5b02bda
Yuchen Kang
TeleAntiFraud-28k: A Audio-Text Slow-Thinking Dataset for Telecom Fraud
  Detection
The detection of telecom fraud faces significant challenges due to the lack
of high-quality multimodal training data that integrates audio signals with
reasoning-oriented textual analysis. To address this gap, we present
TeleAntiFraud-28k, the first open-source audio-text slow-thinking dataset
specifically designed for automated telecom fraud analysis. Our dataset is
constructed through three strategies: (1) Privacy-preserved text-truth sample
generation using automatically speech recognition (ASR)-transcribed call
recordings (with anonymized original audio), ensuring real-world consistency
through text-to-speech (TTS) model regeneration; (2) Semantic enhancement via
large language model (LLM)-based self-instruction sampling on authentic ASR
outputs to expand scenario coverage; (3) Multi-agent adversarial synthesis that
simulates emerging fraud tactics through predefined communication scenarios and
fraud typologies. The generated dataset contains 28,511 rigorously processed
speech-text pairs, complete with detailed annotations for fraud reasoning. The
dataset is divided into three tasks: scenario classification, fraud detection,
fraud type classification. Furthermore, we construct TeleAntiFraud-Bench, a
standardized evaluation benchmark comprising proportionally sampled instances
from the dataset, to facilitate systematic testing of model performance on
telecom fraud detection tasks. We also contribute a production-optimized
supervised fine-tuning (SFT) model trained on hybrid real/synthetic data, while
open-sourcing the data processing framework to enable community-driven dataset
expansion. This work establishes a foundational framework for multimodal
anti-fraud research while addressing critical challenges in data privacy and
scenario diversity. The project will be released at
https://github.com/JimmyMa99/TeleAntiFraud.
TeleAntiFraud-28k is an open-source dataset combining audio and text for telecom fraud detection, featuring privacy-preserving text generation and adversarial synthesis.
automatically speech recognition ASR text-to-speech TTS large language model LLM multi-agent adversarial synthesis scenario classification fraud detection supervised fine-tuning SFT