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Anticounterfeiting technologies meet challenges in the Internet of Things era due to the rapidly growing volume of objects, their frequent connection with humans, and the accelerated advance of counterfeiting/cracking techniques. Here, we, inspired by biological fingerprints, present a simple anticounterfeiting system based on perovskite quantum dot (PQD) fingerprint physical unclonable function (FPUF) by cooperatively utilizing the spontaneous-phase separation of polymers and selective in situ synthesis PQDs as an entropy source. The FPUFs offer red, green, and blue full-color fingerprint identifiers and random three-dimensional (3D) morphology, which extends binary to multivalued encoding by tuning the perovskite and polymer components, enabling a high encoding capacity (about 108570000, far surpassing that of biometric fingerprints). The strategy is compatible with mainstream production techniques that are widely used in traditional low-cost printed anticounterfeiting labels including spray printing, stamping, writing, and laser printing, avoiding complicated fabrication. Macrographical patterns and micro/nanofingerprint patterns with multiscale-tailorable inter-ridge sizes can be fused into a single FPUF label, satisfying different levels of anticounterfeiting requirements. Furthermore, a smart fused scheme of enhanced deep learning and fingerprint characteristic comparison is leveraged, by which high-efficiency, high-accuracy authentication of our FPUFs is achieved even for the increasingly huge FPUF databases and imperfectly captured images from users.