Related papers: Secure Fingerprint Alignment and Matching Protocol…

This paper presents an effective fingerprint classification method designed based on a hierarchical agglomerative clustering technique. The performance of the technique was evaluated in terms of several real-life datasets and a significant…

We propose a machine learning pipeline for forensic shoeprint pattern matching that improves on the accuracy and generalisability of existing methods. We extract 2D coordinates from shoeprint scans using edge detection and align the two…

The localization speed and accuracy in the indoor scenario can greatly impact the Quality of Experience of the user. While many individual machine learning models can achieve comparable positioning performance, their prediction mechanisms…

Electronic information is increasingly often shared among entities without complete mutual trust. To address related security and privacy issues, a few cryptographic techniques have emerged that support privacy-preserving information…

We present a simple but effective method for automatic latent fingerprint segmentation, called SegFinNet. SegFinNet takes a latent image as an input and outputs a binary mask highlighting the friction ridge pattern. Our algorithm combines…

As a promising non-password authentication technology, radio frequency (RF) fingerprinting can greatly improve wireless security. Recent work has shown that RF fingerprinting based on deep learning can significantly outperform conventional…

We propose a security verification framework for cryptographic protocols using machine learning. In recent years, as cryptographic protocols have become more complex, research on automatic verification techniques has been focused on. The…

Fingerprint recognition systems have been deployed globally in numerous applications including personal devices, forensics, law enforcement, banking, and national identity systems. For these systems to be socially acceptable and…

This paper presents a novel approach to the digital signing of electronic documents through the use of a camera-based interaction system, single-finger tracking for sign recognition, and multi commands executing hand gestures. The proposed…

Private data generated by edge devices -- from smart phones to automotive electronics -- are highly informative when aggregated but can be damaging when mishandled. A variety of solutions are being explored but have not yet won the public's…

Fairness monitoring is critical for detecting algorithmic bias, as mandated by the EU AI Act. Since such monitoring requires sensitive user data (e.g., ethnicity), the AI Act permits its processing only with strict privacy measures, such as…

Fingerprint liveness detection systems have been affected by spoofing, which is a severe threat for fingerprint-based biometric systems. Therefore, it is crucial to develop some techniques to distinguish the fake fingerprints from the real…

A plethora of contact tracing apps have been developed and deployed in several countries around the world in the battle against Covid-19. However, people are rightfully concerned about the security and privacy risks of such applications. To…

DNA fingerprinting and matching for identifying suspects has been a common practice in criminal investigation. Such proceedings involve multiple parties such as investigating agencies, suspects and forensic labs. A major challenge in such…

We are introducing Aligned, a platform for global governance and alignment of frontier models, and eventually superintelligence. While previous efforts at the major AI labs have attempted to gather inputs for alignment, these are often…

Fingerprint recognition systems stand as pillars in the realm of biometric authentication, providing indispensable security measures across various domains. This study investigates integrating Convolutional Neural Networks (CNNs) with Gabor…

Fingerprints have grown to be the most robust and efficient means of biometric identification. Latent fingerprints are commonly found at crime scenes. They are also of the overlapped kind making it harder for identification and thus the…

Federated Learning enables one to jointly train a machine learning model across distributed clients holding sensitive datasets. In real-world settings, this approach is hindered by expensive communication and privacy concerns. Both of these…

Quantum metrology and cryptography can be combined in a distributed and/or remote sensing setting, where distant end-users with limited quantum capabilities can employ quantum states, transmitted by a quantum-powerful provider via a quantum…

Radio Frequency Identification (RFID) is a technology aimed at eficiently identifying and tracking goods and assets. Such identification may be performed without requiring line-of-sight alignment or physical contact between the RFID tag and…