使用生成对抗网络改进合成媒体的生成和检测.
Rabbia Zia1, Mariam Rehman1, Afzaal Hussain1
1Department of Information Technology, Government College University Faisalabad, Punjab, Pakistan.
PeerJ. Computer science
|September 24, 2024
概括
这项研究引入了改进的生成对抗网络 (GAN) 来检测深度假冒,提高了分辨真实和合成图像的准确性. 优化的模型显著降低了与人工智能生成的内容相关的风险.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 使用生成模型和深度学习创建的合成图像或深度假冒通过错误信息和违反社交媒体法规构成风险.
- 目前用于深度假冒检测的方法需要提高准确性和稳定性.
研究的目的:
- 提出一个改进的生成对抗网络 (GAN) 模型,以提高分辨真实和合成图像的准确性.
- 通过在GAN培训期间专注于数据增强和标签平滑策略来应对深度假冒带来的挑战.
主要方法:
- 使用深度卷积生成对抗网络 (DCGAN) 作为基础模型.
- 实施数据增强和标签平滑技术用于GAN培训.
- 优化模型参数以提高性能.
主要成果:
- 与传统的GAN相比,拟议的GAN模型表现出优越的性能.
- 在基准数据集上获得了Fréchet Inception Distance (FID) 55.67分和98.82%的准确性.
- 在合成图像检测中获得0.99的F1得分.
结论:
- 开发的GAN框架对于合成图像生成和检测都有效.
- 优化的模型显著降低了与深度假冒相关的风险.
- 这项研究为通过合成媒体打击虚假信息传播提供了有效的解决方案.
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