基于跨模态语义对齐的电信诈骗图文一致性检测方法

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基于跨模态语义对齐的电信诈骗图文一致性检测方法

更新时间：2026-04-21

- 基于跨模态语义对齐的电信诈骗图文一致性检测方法
- Cross-Modal Semantic Alignment-Based Method for Detecting Text-Image Consistency in Telecom Fraud
- 电信科学 2026年
- 作者机构：
- 作者简介：
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  中图分类号： TP393
- 收稿：2026-02-26，
  
  修回：2026-04-18，
  
  录用：2026-04-20，
- 稿件说明：
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基于跨模态语义对齐的电信诈骗图文一致性检测方法[J/OL]. 电信科学, 2026.

Cross-Modal Semantic Alignment-Based Method for Detecting Text-Image Consistency in Telecom Fraud[J/OL]. Telecommunications Science, 2026.
基于跨模态语义对齐的电信诈骗图文一致性检测方法[J/OL]. 电信科学, 2026. DOI：

Cross-Modal Semantic Alignment-Based Method for Detecting Text-Image Consistency in Telecom Fraud[J/OL]. Telecommunications Science, 2026. DOI：

摘要

随着生成式AI发展，电信诈骗日益呈现视觉化与复合化趋势，诈骗分子通过伪造转账截图、公文等图像搭配误导文本，构成“图文并悖”欺诈，传统检测系统面临挑战。为此，本文提出一种基于深度跨模态语义对齐的图文一致性检测方法：先利用微调的YOLOv8定位图像中关键视觉元素，并解析文本实体与欺诈意图；再借助预训练CLIP模型将视觉与文本信息编码至同一语义空间；最后通过计算特征向量余弦相似度并结合动态阈值评估语义矛盾。在自建电信诈骗图文数据集上的实验表明，该方法F1值达92.7%，较特征拼接基线提升19.6%，且对噪声、裁剪等干扰具有良好鲁棒性，为自动化、高精度的混合型诈骗识别提供了可解释解决方案。

Abstract

With the advancement of generative AI

telecom fraud is increasingly exhibiting visual and composite trends. Fraudsters combine forged images

such as transfer screenshots and official documents

with misleading text to form "text-image contradiction" scams

posing significant challenges to traditional detection systems. To address this issue

this paper proposes a deep cross-modal semantic alignment-based method for detecting text-image consistency. The method first employs a fine-tuned YOLOv8 model to localize and extract key visual elements

such as logos and seals

from images

while simultaneously parsing key entities and fraudulent intents from the text. Subsequently

a pre-trained CLIP model is utilized to encode the extracted visual regions and text information into the same semantic space

obtaining comparable feature vectors. Finally

the semantic contradiction between text and images is quantitatively assessed by computing the cosine similarity of the visual and textual feature vectors combined with an adaptive dynamic threshold. Experimental results on a self-built dataset of telecom fraud text-image pairs demonstrate that the proposed method achieves an F1-score of 92.7%

representing a 19.6% improvement over the feature concatenation baseline. Furthermore

it exhibits good robustness against common adversarial interferences such as noise addition and image cropping. This study provides an efficient and interpretable solution for automated

high-precision detection of hybrid telecom fraud involving both text and images.

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