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基于深度学习的蛋白质自我组装的数字化,为设备安全打印可生物降解,物理不可克隆的标签

Sayantan Pradhan¹, Abhi D Rajagopala², Emma Meno³

¹Department of Chemical and Life Science Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA.

|September 28, 2023

概括

这项研究介绍了一种新的生物物理不可克隆功能 (PUF),使用蛋白质自组装来安全防伪. 该方法从蛋白质图像中生成独特的加密密钥,提供可扩展和低成本的解决方案.

科学领域:

生物技术是生物技术.
材料科学材料科学材料科学
密码学密码学密码学

背景情况:

假冒对公共卫生和医疗器械真实性构成重大威胁.
物理非克隆功能 (PUF) 为安全认证提供了强大的解决方案.
现有的PUF技术需要进一步开发以获得广泛的应用.

研究的目的:

开发一种利用蛋白质自我组装的新型生物PUF.
创建一个安全和可扩展的防伪技术.
验证生成的加密密钥的随机性和适用性.

主要方法:

简单的蛋白质自我组装过程被用作生物PUF的源.
一个深度学习模型将自我组装图像数字化,以提取特征向量.
生成的密钥经过二进制化,无序化,并使用NIST SP 800-22进行随机性测试.
图像被打印在可生物降解的丝纤维素标签上,使用蛋白质生物墨水进行物理部署.

主要成果:

蛋白质自我组装过程成功生成了独特的指纹.
深度学习有效地提取特征向量,产生足够随机的加密密钥.
NIST SP 800-22测试证实了生成的密钥的高度随机性.
与源图像相比,手机对印刷标签的成像显示出较低的错误率.

结论:

基于深度学习的生物PUF提供了一个有希望的低成本,可扩展和高度随机化的反假冒策略.
蛋白质自我组装为生物PUF提供了一个独特而可行的源.
可生物降解的,用蛋白质生物墨水打印的标签证明了认证的实际部署潜力.

关键词:

可生物降解的标签深度学习是一种深度学习.设备安全的安全装置.扩散有限聚合的聚合.物理上无法克隆的功能.丝蛋白质是一种丝蛋白质.

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