Electrocardiography (ECG) is a widely used technique for recording the electrical activity of the heart. ECG measures voltage changes via electrodes on the body to reveal heart rhythms and potential cardiovascular issues. Yet, traditional ECG monitoring needs skin contact, specialized devices, and clinical oversight, limiting its use for daily or long-term monitoring. Remote electrocardiography is an emerging method for estimating ECG signals without any physical contact, using only visual information such as facial videos. Unlike traditional ECG systems that rely on adhesive electrodes and specialized equipment, rECG allows for contactless cardiac monitoring, making it well-suited for telemedicine, long-term health tracking, and unobtrusive assessments. In this work, we introduce the Guided ECG Diffusion Model (GEDM), a new framework that reconstructs accurate ECG signals from facial videos. GEDM combines a modified PhysNet to extract strong rPPG signals with a multi-stage diffusion process, guided by important ECG landmarks like the P wave, QRS complex, and T wave. This guidance ensures that the generated signals maintain high physiological accuracy. We also present the PhysioFace Diverse Dataset (PFDD), a large dataset containing synchronized facial videos and ECG signals from 100 subjects with diverse skin tones. Extensive experiments on both PFDD and the public MAHNOB-HCI dataset show that GEDM outperforms baseline models across multiple metrics, including MAE, RMSE, correlation, and SNR. These results set a new benchmark for contactless ECG generation and demonstrate the potential of rECG as an ECG-based biometric system or liveness detection technique for anti-spoofing biometric applications.

Contactless fingerprint recognition offers a higher level of user comfort and addresses hygiene concerns more effectively. However, it is also more vulnerable to presentation attacks, such as photo-paper, paper printout, and various display attacks, making it more challenging to implement in biometric systems compared to contact-based modalities. Limited research has been conducted on presentation attacks in contactless fingerprint systems, and these studies have encountered challenges in terms of generalization and scalability since both bonafide samples and presentation attacks are utilized during the training model. Although this approach appears promising, it lacks the ability to handle unseen attacks, which is a crucial factor for developing PAD methods that can generalize effectively. We introduced an innovative anti-spoofing approach that combines an unsupervised autoencoder with a convolutional block attention module to address the limitations of existing methods. Our model is trained exclusively on bonafide images without exposure to any spoofed samples during the training phase. It is then evaluated against various types of presentation attack images in the testing phase. The scheme we proposed has achieved an average BPCER of 0.96% with an APCER of 1.6% for presentation attacks involving various types of spoofed samples.

Imagine a world where a copy of your face could trick the most advanced security systems. This isn’t science fiction; it’s a real challenge today. LivDet-Face is a competition that aims to advance the detection of attacks at the biometric sensor, known as Presentation Attack Detection (PAD). This international contest is a key benchmark in biometric security, offering an unbiased look at the latest innovations in face PAD and demonstrating progress over time in detecting and preventing sophisticated attacks. Through the International Joint Conference on Biometrics (IJCB) platform, LivDet-Face 2024 provides a standardized evaluation process, access to advanced Presentation Attack Instruments (PAI), and a comprehensive dataset of bona fide face images. The competition had two main categories: algorithms and systems. A total of sixteen algorithms and one system were submitted for this year’s competition. Anonymous submissions topped both image and video subcategories with an ACER of 4.93% and 4.13%, respectively. In the systems category, Team Dermalog, despite being the sole submission, achieved an impressive ACER of 3.12%.

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%.

Electrocardiogram (ECG) biometric authentication (EBA) is a promising approach for human identification, particularly in consumer devices, due to the individualized, ubiquitous, and easily identifiable nature of ECG signals. Thus, computing architectures for EBA must be accurate, fast, energy efficient, and secure. In this article, first, we implement an EBA algorithm to achieve 100% accuracy in user authentication. Thereafter, we extensively analyze the algorithm to show the distinct variance in execution requirements and reveal the latency bottleneck across the algorithm's different steps. Based on our analysis, we propose a domain-specific architecture (DSA) to satisfy the execution requirements of the algorithm's different steps and minimize the latency bottleneck. We explore different variations of the DSA, including one that features the added benefit of ensuring constant timing across the different EBA steps, in order to mitigate the vulnerability to timing-based side-channel attacks. Our DSA improves the latency compared to a base ARM-based processor by up to 4.24×, while the constant timing DSA improves the latency by up to 19%. Also, our DSA improves the energy by up to 5.59×, as compared to the base processor.

Security primitives based on Dynamic Random Access Memory (DRAM) can provide cost-efficient and practical security solutions, especially for resource-constrained devices, such as hardware used in the Internet of Things (IoT), as DRAMs are an intrinsic part of most contemporary computer systems [1]. Over the past few years, DRAM-based physical unclonable functions became very popular among researchers in this field. However, similar to other types of PUFs, DRAM PUF reliability for authentication and key generation is highly dependent on its resistance against the environmental noises such as Temperature variation, Voltage variations, and Device aging. This paper addresses the challenges related to the reliability and robustness of DRAM PUFs under noisy environments. In this paper we apply a new approach (Quantization) that extracts keys from DRAM startup values with a high reliability and stability rate. This quantization technique identifies suitable features from power-up values of DRAM memories and quantize them into binary bits using tunable parameters that control and predict the environmental noises. Our experimental result shows a high reliability and min-entropy rate for relatively large number of DRAM key bits.

Logic encryption obfuscation has been used for thwarting counterfeiting, overproduction, and reverse engineering but vulnerable to attacks. However, it was recently shown that satisfiability - checking (SAT) can potentially compromise hardware obfuscation circuits. In this paper, we develop a novel attack called BOCANet that can be beneficial from deep learning architecture to compromise hardware obfuscation circuits's key. Our approach involves exploiting deep recurrent neural network (D-RNN) model, and developing attack model to compromise the obfuscated hardware at least an order-of magnitude more efficiently and under resource-constrained scenarios. In our experiments, the BOCANet approach achieves an average success rate of 100% for 32 bit key size, 93.4% for 64 bit key size, 92.2% and 91.7% for 128 and 256 bit key size, respectively.

Photoplethysmography (PPG) signals have unique identity properties for human recognition, and are becoming easier to capture by emerging IoT sensors. Existing research on PPG-based biometric systems rely on fiducial methods that extract landmarks from the PPG signal as features. This paper investigates non-fiducial methods that operating in a holistic manner that is less sensitive to noise in landmarks. We compare PPG-based human verification of 42 subjects with fiducial and non-fiducial methods (specifically, discrete wavelet transform) and classification using a neural network and support vector machine. The experimental results demonstrate higher test recognition rates for wavelet transform feature extraction. We further improve our results by selecting a subset of features via the genetic algorithm.

Electrocardiogram (ECG) has long been regarded as a biometric modality which is impractical to copy, clone, or spoof. However, it was recently shown that an ECG signal can be replayed from arbitrary waveform generators, computer sound cards, or off-the-shelf audio players. In this paper, we develop a novel presentation attack where a short template of the victim's ECG is captured by an attacker and used to map the attacker's ECG into the victim's, which can then be provided to the sensor using one of the above sources. Our approach involves exploiting ECG models, characterizing the differences between ECG signals, and developing mapping functions that transform any ECG into one that closely matches an authentic user's ECG. Our proposed approach, which can operate online or on-the-fly, is compared with a more ideal offline scenario where the attacker has more time and resources. In our experiments, the offline approach achieves average success rates of 97.43% and 94.17% for non-fiducial and fiducial based ECG authentication. In the online scenario, the performance is de-graded by 5.65% for non-fiducial based authentication, but is nearly unaffected for fiducial authentication.

Physical Unclonable Functions are promising candidates for lightweight authentication applications as they are hard to predict and clone. PUFs are dependent on process variations that occurs during silicon chip fabrication. As the CMOS technology scales down towards nanoscale dimensions, there are increasing transistor reliability challenges which impact the lifetime of integrated circuits. These issues are known as aging effects, which result in degradation of the performance of circuits. In this paper, we analyze the effects of aging on the reliability of intrinsic DRAM PUFs. We present accelerated aging experimental results over 18 months (from Sep. 2014 to Feb. 2016) on 3 DRAM PUFs. Based on our observations, DRAM PUFs maintain their reliability over time, and thus, validate the use of DRAM PUFs in a number of applications such as system authentications.

A physically unclonable function (PUF) is an irreversible probabilistic function that produces a random bit string. It is simple to implement but hard to predict and emulate. PUFs have been widely proposed as security primitives to provide device identification and authentication. In this paper, we propose a novel dynamic-memory-based PUF [dynamic RAM PUF (DRAM PUF)] for the authentication of electronic hardware systems. The DRAM PUF relies on the fact that the capacitor in the DRAM initializes to random values at startup time. Most PUF designs require custom circuits to convert unique analog characteristics into digital bits, but using our method, no extra circuitry is required to achieve a reliable 128-bit PUF. The results show that the proposed DRAM PUF provides a large number of input patterns (challenges) compared with other memory-based PUF circuits such as static RAM PUFs. Our DRAM PUFs provide highly unique PUFs with a 0.4937 average interdie Hamming distance. We also propose an enrollment algorithm to achieve highly reliable results to generate PUF identifications for system-level security. This algorithm has been validated on real DRAMs with an experimental setup to test different operating conditions.

The Internet of Things (IoT) is a concept that involves connecting endpoint devices and physical objects to the Internet. While IoT is envisioned to dramatically increase convenience in our daily lives, it could also result in catastrophic economic and safety issues. Considering the applications envisioned for IoT (smart cities, homes, retail, etc.), security must be handled with great care and should start from the bottom up (i.e., from the hardware level). As a good deal of IoT devices require interaction between devices and humans, biometrics provide an interesting opportunity for improving both the convenience and security in IoT applications. In this paper, we consider the potential benefits and challenges associated with incorporating biometrics into IoT. We combine novel biometrics, such as ECG and PPG, and system-level obfuscation approaches to prevent reverse engineering, tampering and unauthorized access of IoT devices and other electronic systems. Our preliminary results are promising and motivate future work in this area.

Physical Unclonable Functions (PUF) are the result of random uncontrollable variables in the manufacturing process. A PUF can be used as a source of random but reliable data for applications such as generating chip identification and encryption keys. Among various types of PUFs, an intrinsic PUF is the result of a preexisting manufacturing process, does not require any additional circuitry, and is cost effective. In this paper, we introduce an intrinsic PUF based on dynamic random access memories (DRAM). DRAM PUFs can be used in low cost identification applications and also have several advantages over other PUFs such as large input patterns. The DRAM PUF relies on the fact that the capacitor in the DRAM initializes to random values at startup. We demonstrate real DRAM PUFs and describe an experimental setup to test different operating conditions on three DRAMs to achieve the highest reliable results. Finally, we select the most stable bits to use as chip ID using our enrollment algorithm.