混合优化驱动的假新闻检测使用增强型变压器模型.
Ganesh Karthik M1, Khadri Syed Faizz Ahmad2, Sai Geetha Pamidimukkala3
1Department of Computer Science and Engineering, GITAM School of Technology, GITAM University- Bengaluru Campus, Bengaluru, India.
Scientific reports
|April 28, 2025
概括
本研究介绍了一种先进的修改变压器 (MT) 模型,用于检测多式联运虚假新闻,通过整合混合优化来提高准确性. 这种新的方法有效地识别了复杂的假冒,增强了错误信息检测系统.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 信息安全 信息安全
背景情况:
- 多模式假新闻 (文本和图像) 由于分布差异,造成了检测挑战.
- 现有的探测器和大型视觉语言模型 (LVLM) 在开放世界场景和识别本地伪造方面存在局限性.
- 目前的方法往往无法在早期阶段有效制错误信息.
研究的目的:
- 开发一个强大而准确的系统来检测大规模的多式联运假新闻.
- 解决传统探测器和LVLM在识别复杂伪造品方面的局限性.
- 通过优化模型性能来提高假新闻检测的效率和有效性.
主要方法:
- 介绍一个改造变压器 (MT) 模型,在制造的新闻文章上在三个阶段进行微调.
- 使用PSODO优化MT模型,PSODO是一种混合粒子群优化和花优化算法.
- 在PSODO中整合全球和本地搜索策略,以提高搜索效率并克服本地最佳.
主要成果:
- 拟议的方法显著提高了对基准数据集的假新闻检测准确度.
- MT模型有效地捕捉了分布不一致性和多式联运伪造细节.
- 实验结果表明,与传统探测器和LVLM相比,其性能优越.
结论:
- 变压器的集成和混合优化对于开发通用,可扩展和准确的假新闻检测系统至关重要.
- 拟议的方法为打击复杂的多式联运虚假新闻的传播提供了一个有希望的解决方案.
- 这项研究有助于通过更有效和高效的方法推进错误信息检测领域.
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