对基于GAN的融合深度神经模型进行比较分析,用于假面部检测
Musiri Kailasanathan Nallakaruppan1, Chiranji Lal Chowdhary1, SivaramaKrishnan Somayaji1
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamilnadu 632014, India.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
这项研究介绍了一种基于人工智能 (AI) 的系统,用于假面部检测,这对整个行业的安全至关重要. 模型-3实现了最高的准确性 (0.9797),在识别假身份方面表现出卓越的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 伪造面部身份在各个领域构成重大安全风险,包括金融和军事应用.
- 有效检测合成或操纵的面部数据对于保持安全和信任至关重要.
研究的目的:
- 开发和评估基于人工智能 (AI) 的系统,用于假面部检测.
- 为了比较假面部识别的三种不同融合模型的性能,评估它们与生成对抗网络 (GAN) 集成的适用性.
主要方法:
- 在广泛的真实和假面部图像数据集上训练人工智能模型.
- 实施预处理步骤,包括采样,聚合,规范化和矢量化.
- 对三个融合模型 (模型-1,模型-2,模型-3) 的比较分析,涉及DenseNet-201,ResNet (50和102),Xception和Inception V3架构.
主要成果:
- 模型-3 (DenseNet-201+ResNet-102+Xception) 在初级数据集上达到0.9797的最高准确度.
- 模型-2 (DenseNet-201+ResNet-50+Inception V3) 显示了最小的损失值为0.1146. 这是一个很好的例子.
- 模型-3还实现了86.42%的准确性,在二级数据集上损失了0.4054,表明了稳定性.
结论:
- 拟议的基于人工智能的假面部检测系统有效地识别了被操纵的面部数据.
- 融合模型,特别是Model-3,显示了与GAN集成的高潜力,以提高假面部检测能力.
- 开发的模型提供了一个有希望的解决方案,以减轻与虚假面部身份相关的风险.
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