arXiv subjects

Xuefei Yin

Publications and source records attributed to Xuefei Yin.

21 records · Page 2Linked to original sources

arXiv2022

FingerGAN: A Constrained Fingerprint Generation Scheme for Latent Fingerprint Enhancement

Latent fingerprint enhancement is an essential pre-processing step for latent fingerprint identification. Most latent fingerprint enhancement methods try to restore corrupted gray ridges/valleys. In this paper, we propose a new method that formulates the latent fingerprint enhancement as a constrained fingerprint generation problem within a generative adversarial network (GAN) framework. We name the proposed network as FingerGAN. It can enforce its generated fingerprint (i.e, enhanced latent fingerprint) indistinguishable from the corresponding ground-truth instance in terms of the fingerprint skeleton map weighted by minutia locations and the orientation field regularized by the FOMFE model. Because minutia is the primary feature for fingerprint recognition and minutia can be retrieved directly from the fingerprint skeleton map, we offer a holistic framework which can perform latent fingerprint enhancement in the context of directly optimizing minutia information. This will help improve latent fingerprint identification performance significantly. Experimental results on two public latent fingerprint databases demonstrate that our method outperforms the state of the arts significantly. The codes will be available for non-commercial purposes from \url{https://github.com/HubYZ/LatentEnhancement}.

cs.CV↗

arXiv2022

A Novel Length-Flexible Lightweight Cancelable Fingerprint Template for Privacy-Preserving Authentication Systems in Resource-Constrained IoT Applications

Fingerprint authentication techniques have been employed in various Internet of Things (IoT) applications for access control to protect private data, but raw fingerprint template leakage in unprotected IoT applications may render the authentication system insecure. Cancelable fingerprint templates can effectively prevent privacy breaches and provide strong protection to the original templates. However, to suit resource-constrained IoT devices, oversimplified templates would compromise authentication performance significantly. In addition, the length of existing cancelable fingerprint templates is usually fixed, making them difficult to be deployed in various memory-limited IoT devices. To address these issues, we propose a novel length-flexible lightweight cancelable fingerprint template for privacy-preserving authentication systems in various resource-constrained IoT applications. The proposed cancelable template design primarily consists of two components: 1) length-flexible partial-cancelable feature generation based on the designed re-indexing scheme; and 2) lightweight cancelable feature generation based on the designed encoding-nested-difference-XOR scheme. Comprehensive experimental results on public databases~FVC2002 DB1-DB4 and FVC2004 DB1-DB4 demonstrate that the proposed cancelable fingerprint template achieves equivalent authentication performance to state-of-the-art methods in IoT environments, but our design substantially reduces template storage space and computational cost. More importantly, the proposed length-flexible lightweight cancelable template is suitable for a variety of commercial smart cards (e.g., C5-M.O.S.T. Card Contact Microprocessor Smart Cards CLXSU064KC5). To the best of our knowledge, the proposed method is the first length-flexible lightweight, high-performing cancelable fingerprint template design for resource-constrained IoT applications.

cs.CR↗

arXiv2020

Designing False Data Injection attacks penetrating AC-based Bad Data Detection System and FDI Dataset generation

The evolution of the traditional power system towards the modern smart grid has posed many new cybersecurity challenges to this critical infrastructure. One of the most dangerous cybersecurity threats is the False Data Injection (FDI) attack, especially when it is capable of completely bypassing the widely deployed Bad Data Detector of State Estimation and interrupting the normal operation of the power system. Most of the simulated FDI attacks are designed using simplified linearized DC model while most industry standard State Estimation systems are based on the nonlinear AC model. In this paper, a comprehensive FDI attack scheme is presented based on the nonlinear AC model. A case study of the nine-bus Western System Coordinated Council (WSCC)'s power system is provided, using an industry standard package to assess the outcomes of the proposed design scheme. A public FDI dataset is generated as a test set for the community to develop and evaluate new detection algorithms, which are lacking in the field. The FDI's stealthy quality of the dataset is assessed and proven through a preliminary analysis based on both physical power law and statistical analysis.

cs.CR↗