The ISC International Journal of Information Security

Abstract Advanced fingerprint spoofing attacks often threaten biometric authentication systems, such as the presence of non-contiguous minutiae to form a double-identity fingerprint. This research paper offers a deep learning system based on MobileNet to differentiate between authentic and anomalous fingerprints. The SOCOFing dataset (in the public domain) with real fingerprints and synthetically deformed samples at three levels of difficulty (easy, medium, and hard) was experimented with, which is used as a reference point in assessing the performance of spoof detection. In order to enhance the generalization, data augmentation and transfer learning were utilized, which helped in increasing the resilience of the model to different fingerprint modifications. The overall test accuracy of the MobileNet-based model was 88%, which declined with the case of medium and hard alterations, which suggests the inability to detect extremely complicated spoofing patterns. However, the model is efficient and has low computational expense, thus it can form a viable yet lightweight real-time biometric verification architecture. The future research ought to cover more sophisticated architectures, cross-dataset testing, and feature-level analysis in order to enhance the detection of difficult forms of spoofing.

Abstract Internet of Things (IoT) and cloud computing technologies have connected the infrastructure of the city to make the context-aware and more intelligent city for utility its major resources. These technologies have much potential to solve the
challenges of urban areas around the globe to facilitate the citizens. A framework model that enables the integration of sensor’s data and analysis of the data in the context of smart parking is proposed. These technologies use sensors and
devices deployed around the city parking areas sending real time data through the edge computers to the main cloud servers. Mobil-Apps are developed that used real time data, set from servers of the parking facilities in the city. Fuzzification is shown to be a capable mathematical approach for modeling city parking issues. To solve the city parking problems in cities a detailed analysis of fuzzy logic proposed systems is developed. This paper presents the results
achieved using Mamdani Fuzzy Inference System to model complex smart parking system. These results are verified using MATLAB simulation.