Physical unclonable function (PUF) is a cornerstone of anticounterfeiting. However, conventional PUF key-based secure tags encounter several bottlenecks, such as complicated manufacturing, specialized and tedious readout, long authentication time, and insufficient stability. Here, we utilize various two-dimensional materials (2DMs), including Ti₃C₂T_x MXene and graphene oxide, to construct multigenerational microstructures as PUF patterns. Two intermediate treatments, cation intercalation and moisture-induced lubrication, are introduced in between sequential contractions to engineer the multiscale patterns in a transfer-free and scalable fashion. A deep learning (DL)-facilitated software is developed to pre-categorize the hierarchical topographies with classifiable features. Thereafter, the search-and-compare is conducted within a smaller database to shorten the overall authentication time. The synergy between 2DM tags and DL-facilitated software enables a reliable and environmentally stable anticounterfeiting technology, DeepKey, showing superior encoding capacity (>10^144,494) and short authentication time (∼3.5 min). Our 2DM anticounterfeiting tag is finally integrated with QR codes to provide two-layer information security.

物理不可克隆功能（PUF）是防伪的基石。但是，常规的基于PUF密钥的安全标签遇到了多个瓶颈，例如复杂的制造，专门且乏味的读取，较长的身份验证时间以及不足的稳定性。在这里，我们利用各种二维材料（2DM），包括Ti ₃ C ₂ T _xMXene和氧化石墨烯以PUF模式构建多代微观结构。在顺序收缩之间引入了两种中间处理，即阳离子插层和水分诱导润滑，以无转移和可扩展的方式设计多尺度模式。开发了深度学习（DL）辅助软件，以对具有可分类特征的分层地形进行预分类。此后，在较小的数据库中进行搜索和比较，以缩短总体身份验证时间。2DM标签和DL辅助软件之间的协同作用实现了可靠且对环境稳定的防伪技术DeepKey，显示出出众的编码能力（> 10 ^144,494）和较短的身份验证时间（约3.5分钟）。我们的2DM防伪标签最终与QR码集成在一起，以提供两层信息安全性。