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人工智能与人工智能:人工智能可以检测人工智能生成的图像吗?
Samah S Baraheem1,2, Tam V Nguyen2
1Department of Computer Science, Umm Al-Qura University, Prince Sultan Bin Abdulaziz Road, Mecca 21421, Makkah, Saudi Arabia.
Journal of imaging
|October 27, 2023
概括
本研究引入了一个卷积神经网络 (CNN) 框架来检测人工智能生成的图像,实现100%的准确性. 这种可靠的检测方法对于在生成对抗网络 (GAN) 时代验证图像真实性至关重要.
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:
- 生成对抗网络 (GAN) 擅长创建真实的合成图像,对真实性和安全性构成挑战.
- 人工智能产生的图像在互联网上的广泛传播需要强大的检测方法.
- 自动检测系统对于评估图像合成模型和确保内容完整性至关重要.
研究的目的:
- 开发一个可靠的框架来区分人工智能生成的图像和真实的图像.
- 为图像合成模型创建一个有效的评估工具.
- 加强数字媒体的安全性和真实性验证.
主要方法:
- 利用卷积神经网络 (CNN) 来进行图像分类.
- 在各种任务和架构中收集各种GAN生成的图像,以实现通用检测.
- 应用转移学习和集成的类激活地图 (CAM) 以确定分类的歧视性区域.
- 使用Adam优化器对预先训练的EfficientNetB4模型进行了微调,具有特定的学习速率,批量大小和时代,包含数据增强和学习速率降低.
主要成果:
- 在真实或合成图像 (RSI) 数据集上实现了100%的准确性.
- 在各种其他数据集和配置上展示了卓越的性能和准确性.
- 开发的CNN框架有效地识别了GAN生成的图像.
结论:
- 拟议的CNN框架可靠地检测AI生成的图像,为图像合成提供了有价值的评估工具.
- 该方法显示了高精度和概括能力,这对于打击合成媒体传播至关重要.
- 以特定参数进行微调的EfficientNetB4对于GAN图像检测非常有效.
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