A Facile Microwave-Assisted Green Synthetic Approach of Solid-State Fluorescent Carbon-Dot Nanopowders Derived from Biowaste for Potential Latent-Fingerprint Enhancement

Latent fingerprint detection and visualization remains a challenge especially where problems of poor contrast, auto-fluorescent surfaces and patterned backgrounds are encountered. As a result there is an increasing interest in the development of simple, cost effective, rapid and yet accurate methods for latent fingerprint detection and recovery. Herein, this paper reports the synthesis of bright blue photoluminescent carbon dots (C-dots) via an eco-friendly and simple one-step microwave-assisted carbonization of potato peels’ biomass. The C-dots were prepared in only 3 min and ground into powder and used without any further treatment. The as-prepared C-dots were characterized using atomic force microscope, Fourier transform infra-red spectroscopy and X-ray diffraction with an average size of 1.0[Formula: see text]nm. The optical properties of the as-prepared C-dots were studied by UV-Vis spectroscopy and spectrofluorometer which established an excitation and emission wavelengths of 390[Formula: see text]nm and 480[Formula: see text]nm, respectively. Owing to their strong solid state fluorescence, the as-prepared C-dots’ powder was successfully used in latent fingerprint detection and imaging on porous and nonporous surfaces. Latent fingerprints were recovered with high resolution and excellent quality providing sufficient details for individual identification. These findings demonstrate that C-dots derived from biomass have a great potential in latent fingerprint analysis for forensic applications.

Web Service Access Control Based on Browser Fingerprint Detection

Web services have covered all areas of social life, and various browsers have become necessary software on computers and mobile phones, and they are also the entrances to Web services. All kinds of threats to web data security continue to appear, so web services and browsers have become the focus of security. In response to the requirements of Web service for access entity identification and data access control, this paper proposes a multi-dimensional browser fingerprint detection method based on adversarial learning, and designs a Web service access control framework combined with browser fingerprint detection. Through the joint use of multi-dimensional browser features, adversarial learning is used to improve the accuracy and robustness of browser fingerprint detection; a cross-server and browser-side Web service access control framework is established by creating tags for Web data resources and access entities. Based on the mapping relationship between browser fingerprint detection entities and data resources, fine-grained hierarchical data access control is realized. Through experiments and analysis, the browser fingerprint detection method proposed in this paper is superior to existing machine learning detection methods in terms of accuracy and robustness. Based on the adversarial learning method, good detection results can be obtained in the case of a small number of user samples. At the same time, the open source data set is further used to verify the advantages of the method in this paper. The Web service access control framework can satisfy the requirements of Web data security control, is an effective supplement to user identification technology, and is implementable.