Bi2O2基于Se的双模噪声发生器用于生成对抗网络的应用
Bo Liu1, XingYi Zheng2, Dharmendra Verma3
1Faculty of Information Technology, College of Microelectronics, Beijing University of Technology, Beijing 100124, People's Republic of China.
ACS applied materials & interfaces
|October 12, 2023
概括
使用 bismuth oxy-selenide (Bi2O2Se) 的硬件噪声生成器为生成对抗网络 (GAN) 提供了独特的不可预测性. 这项研究证明了它们在增强图像生成多样性和安全性的有效性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机科学 计算机科学
- 电气工程 电气工程
背景情况:
- 生成对抗网络 (GAN) 依赖于噪音来产生独特而强大的图像.
- 基于硬件的噪声产生提供了更大的不可预测性比软件方法通过结合物理源.
- 氧化物 (Bi2O2Se) 是一个有前途的材料,由于其电子和光电子特性.
研究的目的:
- 为GAN应用展示基于Bi2O2Se的双模式噪声发生器.
- 探索Bi2O2Se在创建多样化和安全生成图像方面的潜力.
- 分析与Bi2O2Se.Se集成的不同物理噪声源.
主要方法:
- 基于Bi2O2Se的双模噪声发生器的制造.
- 在光探测器 (黑电流) 和memristor (随机电报噪声) 模式下对噪声的描述.
- 应用马尔科夫链与K-means集群来分析噪声状态和过渡.
- 使用初始分数和Fréchet初始距离评估GAN性能.
主要成果:
- 成功展示了Bi2O2Se作为硬件噪音生成的材料平台.
- 识别和描述不同的噪声模式 (黑电流和随机电报噪声).
- 使用先进的统计方法量化噪声状态和过渡概率.
- 使用标准GAN评估指标验证生成的图像属性.
结论:
- Bi2O2Se是一种通用材料,用于开发用于GAN的硬件噪声发生器.
- 使用Bi2O2Se的硬件噪声生成提高了GAN生成图像的不可预测性和质量.
- 这种方法为更安全和多样化的AI驱动图像合成提供了途径.
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