混合卷积神经网络和生成对立网络框架,用于强大的深度假冒检测:提高准确性,可扩展性和多模式集成
Pradeep Gupta1, Sonam Gupta2, Lipika Goel3
1Department of Computer Science and Engineering, Ajay Kumar Garg Engineering College, Ghaziabad, India.
Journal of visualized experiments : JoVE
|September 8, 2025
概括
本研究介绍了使用卷积神经网络 (CNN) 和生成对抗网络 (GAN) 的混合深度假冒检测框架. 先进的模型在识别操纵的媒体方面实现了最先进的准确性,增强了数字信任.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 网络安全 网络安全
背景情况:
- 深度假冒对数字媒体的完整性和公众信任构成重大威胁.
- 现有的深度假冒检测方法在可扩展性,通用性和对抗攻击的稳定性方面面临挑战.
研究的目的:
- 开发和评估一个混合深度假冒检测框架,整合CNN和GAN.
- 为了提高深度假冒检测的可扩展性,通用性和对抗性稳定性.
- 解决深度假冒传播和错误信息对社会技术的影响.
主要方法:
- 实施了一个混合框架,将卷积神经网络 (CNN) 和生成对抗网络 (GAN) 结合起来.
- 综合对抗训练,时间衰变分析和多模式检测 (音频,视频,文本).
- 在FaceForensics++基准数据集上评估性能.
主要成果:
- 实现了最先进的性能,准确度为98.5%,精度为99.1%,回忆率为97.9%,AUC为0.995.5.
- 时间分析模拟了可检测性衰变,指导主动的再培训.
- 多模式检测发现了97.8%的跨模式不一致性.
结论:
- 混合CNN-GAN框架为深度假冒检测提供了一个强大的,可扩展的解决方案.
- 跨学科的方法对于开发有效的AI驱动的防护措施来防止错误信息至关重要.
- 强调需要早期检测系统和公众宣传活动.
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