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With the increasing integration of smartphones into our daily lives, fingerphotos are becoming a potential contactless authentication method. While it offers convenience, it is also more vulnerable to spoofing using various presentation attack instruments (PAI). The contactless fingerprint is an emerging biometric authentication but has not yet been heavily investigated for anti-spoofing. While existing anti-spoofing approaches demonstrated fair results, they have encountered challenges in terms of universality and scalability to detect any unseen/unknown spoofed samples. To address this issue, we propose a universal presentation attack detection method for contactless fingerprints, despite having limited knowledge of presentation attack samples. We generated synthetic contactless fingerprints using StyleGAN from live finger photos and integrating them to train a semi-supervised ResNet-18 model. A novel joint loss function, combining the Arcface and Center loss, is introduced with a regularization to balance between the two loss functions and minimize the variations within the live samples while enhancing the inter-class variations between the deepfake and live samples. We also conducted a comprehensive comparison of different regularizations’ impact on the joint loss function for presentation attack detection (PAD) and explored the performance of a modified ResNet-18 architecture with different activation functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center loss. Finally, we evaluate the performance of the model using unseen types of spoof attacks and live data. Our proposed method achieves a Bona Fide Classification Error Rate (BPCER) of 0.12%, an Attack Presentation Classification Error Rate (APCER) of 0.63%, and an Average Classification Error Rate (ACER) of 0.37%. 

Implantable medical devices (IMD) such as pacemakers, and cardiac defibrillators are becoming increasingly interconnected to networks for remote patient monitoring. However, networked devices are vulnerable to external attacks that could allow adversaries to gain unauthorized access to devices/ data and break patient privacy. To design a lightweight computational trustworthy of IMD, we propose novel ECG-based biometric authentication using lift and shift method based on post-processing data from the noise generated in an ECG signal recording. The lift and shift method is an ideal addition to this system because it is a quick, lightweight process that produces enough random bits for encrypted communication. ECG is a signal that is already being measured by the IMD, so this ECG biometric could utilize the data that is already being actively recorded. We provide a comprehensive evaluation across multiple NIST tests, as well as ENT and Dieharder statistical suites test