arXiv subjects

Nima Karimian

Publications and source records attributed to Nima Karimian.

14 recordsLinked to original sources

arXiv2026

Cross-Layer Roots of Trust: Integrating Biometrics, PUFs, and Hardware Obfuscation

Modern cyber--physical, Internet-of-Things (IoT), wearable, and edge systems increasingly require trust in three distinct entities: the human requesting access, the physical device executing the computation, and the hardware function that is permitted to operate. These requirements are usually studied in separate communities. Biometrics establish human identity but remain vulnerable to presentation attacks, intra-user variability, template leakage, and limited revocability. Physical unclonable functions (PUFs) provide device-specific physical identity and on-demand secret derivation, yet must address environmental instability, helper-data exposure, side channels, and modeling attacks. Hardware obfuscation and logic locking condition correct circuit behavior on an activation secret, but face oracle-guided, approximate, structural, removal, and physical attacks. This survey develops a unified human--device--function view of trust. We first decompose each primitive into its complete processing chain and identify the corresponding security assumptions, implementation mechanisms, and evaluation metrics. We then formalize pairwise compositions---biometric--PUF, PUF--obfuscation, and biometric--obfuscation---and a three-way architecture in which correct functionality is bound jointly to an authorized user and a genuine device. Particular attention is given to biometric key reconstruction, PUF stabilization and modeling resistance, logic-locking attack evaluation, cross-layer error propagation, enrollment trust, key lifecycle, and interface leakage. The survey concludes with a taxonomy and research agenda for revocable human--device credentials, compositional security, leakage-aware integration, reconfigurable activation, and standardized end-to-end evaluation.

cs.CR↗

arXiv2026

DiffAttack: Evasion Attacks Against Face Recognition via Latent Diffusion Models

Facial biometric identification relies on the distinctiveness of user attributes within a high-dimensional embedding space. However, the decision boundaries of deep face recognition (FR) systems are often sufficiently narrow that they can be conflated, rendering the models vulnerable to adversarial attacks. In such scenarios, the FR system fails to distinguish between an authentic source and a meticulously crafted adversarial face. Existing adversarial methods targeting facial biometrics are limited in both performance and their ability to generate high-quality images that are imperceptible to humans. Moreover, these methods often fail when the source and target images belong to different demographic groups or genders. To address these limitations, we present a novel approach for adversarial face generation via latent-space optimization. We leverage latent diffusion models directly to guide generation toward target identity embeddings, as measured by a face recognition model. Our proposed \textbf{DiffAttack} framework has been evaluated on standard benchmarks, such as the FFHQ and CelebA-HQ datasets. DiffAttack significantly outperforms existing adversarial techniques, achieving a high average attack success rate of 84.86% across multiple face recognition models (e.g., FaceNet). Notably, DiffAttack demonstrates superior transferability, surpassing traditional noise-based methods by over 15.28% and semantic-based approaches by approximately 5.21% on benchmark datasets like FFHQ and CelebA-HQ.

cs.CV↗

arXiv2026

Robust Spoofed Speech Detection via Temporal Pyramid Modeling

Spoofed speech detection is increasingly challenged by realistic synthesis, voice conversion, and replay attacks, with cross-dataset generalization remaining a major limitation. This work we propose a Temporal Pyramid Adapter that utilize parallel temporal convolutions with varying receptive fields to capture multi-scale spoofing cues, ranging from local artifacts to global prosodic irregularities. We also integrated self-supervised XLS-R representations combined with front-end adapters, including Mel, Sinc, and a Temporal Pyramid design for multi-scale temporal modeling. The proposed model is evaluated cross multiple benchmark including ASVspoof 2017, ASVspoof 2021 (DF/LA), PartialSpoof, DiffSSD, and multilingual HQ-MPSD datasets. Experimental results demonstrate that Temporal Pyramid model obtained AUC of 99.24% and a EER of 3.87% on the PartialSpoof database, which is significantly outperforming the base model and several SOTA baseline such as LCNN-BLSTM (9.87% EER) and TRACE (8.08% EER). Additionally, multilingual evaluations confirm that while spoofing artifact are independent from language. While self-supervised representations improve robustness, performance degrades under domain and language shifts, highlighting the need for better adaptation and calibration strategies.

cs.CV↗

arXiv2026

Adv-TGD: Adversarial Text-Guided Diffusion for Face Recognition Impersonation Attacks

The widespread adoption of face recognition (FR) technologies raises serious privacy concerns, as facial data can be exploited without consent. To address this challenge, we propose Adv-TGD, a generative adversarial attack framework that synthesizes photorealistic faces capable of impersonating target identities and deceiving face recognition systems. Built upon Stable Diffusion v2.1, Adv-TGD performs per-sample LoRA fine-tuning conditioned on concise textual prompts to generate natural yet adversarially manipulated identities. Unlike conventional identity attack approaches, our method optimizes lightweight cross-attention adapters for each source-target pair within a fixed-timestep denoising process. Latent blending is constrained by a face-local heatmap mask to ensure spatially precise identity manipulation while preserving non-sensitive regions. We introduce a composite objective that integrates masked epsilon-MSE reconstruction, thresholded identity divergence in FR embedding space, directional feature alignment, and source-similarity suppression to balance adversarial attack and visual realism. Optionally, LLaVA-generated attribute prompts enhance fine-grained semantic details without reintroducing identity cues. Under the black-box evaluation protocol, Adv-TGD attains an average attack success rate (ASR) of 85.90% across IR152, IRSE50, MobileFace, and FaceNet, surpassing the semantic SOTA baseline Adv-CPG by 6.25 points, the diffusion-based makeup method DiffAIM by 3 points, and the noise-based P3-Mask by 16 points. Despite its strong attack efficacy, Adv-TGD preserves high visual fidelity (PSNR = 28.18 dB, SSIM = 0.981). Furthermore, we demonstrate the flexibility of our framework by successfully extending it to in-the-wild datasets (LADN), general object classification (ImageNet), and transformer-based diffusion models (FLUX.1).

cs.CV↗

arXiv2025

GRU-AUNet: A Domain Adaptation Framework for Contactless Fingerprint Presentation Attack Detection

Although contactless fingerprints offer user comfort, they are more vulnerable to spoofing. The current solution for anti-spoofing in the area of contactless fingerprints relies on domain adaptation learning, limiting their generalization and scalability. To address these limitations, we introduce GRU-AUNet, a domain adaptation approach that integrates a Swin Transformer-based UNet architecture with GRU-enhanced attention mechanisms, a Dynamic Filter Network in the bottleneck, and a combined Focal and Contrastive Loss function. Trained in both genuine and spoof fingerprint images, GRU-AUNet demonstrates robust resilience against presentation attacks, achieving an average BPCER of 0.09\% and APCER of 1.2\% in the CLARKSON, COLFISPOOF, and IIITD datasets, outperforming state-of-the-art domain adaptation methods.

cs.CV↗

arXiv2025

rPPG-SysDiaGAN: Systolic-Diastolic Feature Localization in rPPG Using Generative Adversarial Network with Multi-Domain Discriminator

Remote photoplethysmography (rPPG) offers a novel approach to noninvasive monitoring of vital signs, such as respiratory rate, utilizing a camera. Although several supervised and self-supervised methods have been proposed, they often fail to accurately reconstruct the PPG signal, particularly in distinguishing between systolic and diastolic components. Their primary focus tends to be solely on extracting heart rate, which may not accurately represent the complete PPG signal. To address this limitation, this paper proposes a novel deep learning architecture using Generative Adversarial Networks by introducing multi-discriminators to extract rPPG signals from facial videos. These discriminators focus on the time domain, the frequency domain, and the second derivative of the original time domain signal. The discriminator integrates four loss functions: variance loss to mitigate local minima caused by noise; dynamic time warping loss to address local minima induced by alignment and sequences of variable lengths; Sparsity Loss for heart rate adjustment, and Variance Loss to ensure a uniform distribution across the desired frequency domain and time interval between systolic and diastolic phases of the PPG signal.

cs.CV↗

arXiv2024

Advancements in Continuous Glucose Monitoring: Integrating Deep Learning and ECG Signal

This paper presents a novel approach to noninvasive hyperglycemia monitoring utilizing electrocardiograms (ECG) from an extensive database comprising 1119 subjects. Previous research on hyperglycemia or glucose detection using ECG has been constrained by challenges related to generalization and scalability, primarily due to using all subjects' ECG in training without considering unseen subjects as a critical factor for developing methods with effective generalization. We designed a deep neural network model capable of identifying significant features across various spatial locations and examining the interdependencies among different features within each convolutional layer. To expedite processing speed, we segment the ECG of each user to isolate one heartbeat or one cycle of the ECG. Our model was trained using data from 727 subjects, while 168 were used for validation. The testing phase involved 224 unseen subjects, with a dataset consisting of 9,000 segments. The result indicates that the proposed algorithm effectively detects hyperglycemia with a 91.60% area under the curve (AUC), 81.05% sensitivity, and 85.54% specificity.

eess.SP↗

arXiv2023

Contactless Fingerprint Biometric Anti-Spoofing: An Unsupervised Deep Learning Approach

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, which makes 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 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 exclusively trained 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.

cs.CV↗

arXiv2023

A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems

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\%.

cs.CV↗

arXiv2021

Intrinsic PUF Instance on Non-Volatile NAND Flash Memory

Embedded systems or micro controller based modules have become increasingly prevalent in our daily lives. However, the security of embedded devices as well as the authenticity of hardware has become an increasing concern within the growing Internet of Things (IoT) space. In this paper we setup an experiment environment where SLC flash program disturbance is observed. We discovered that intra-page disturbance is easier to be produced than inter-page disturbance. We also observed that adjacent pages are paired in (2n, 2n+1) manner, and disturbance only occurs within a pair. Lastly, we found that as page number increases from 0 to 63, it becomes more difficult to observe the first bit flip within a page, and thus more difficult to achieve the disturbance stable state.

cs.CR↗

arXiv2020

When Physical Unclonable Function Meets Biometrics

As the Covid-19 pandemic grips the world, healthcare systems are being reshaped, where the e-health concepts become more likely to be accepted. Wearable devices often carry sensitive information from users which are exposed to security and privacy risks. Moreover, users have always had the concern of being counterfeited between the fabrication process and vendors' storage. Hence, not only securing personal data is becoming a crucial obligation, but also device verification is another challenge. To address biometrics authentication and physically unclonable functions (PUFs) need to be put in place to mitigate the security and privacy of the users. Among biometrics modalities, Electrocardiogram (ECG) based biometric has become popular as it can authenticate patients and monitor the patient's vital signs. However, researchers have recently started to study the vulnerabilities of the ECG biometric systems and tried to address the issues of spoofing. Moreover, most of the wearable is enabled with CPU and memories. Thus, volatile memory-based (NVM) PUF can be easily placed in the device to avoid counterfeit. However, many research challenged the unclonability characteristics of PUFs. Thus, a careful study on these attacks should be sufficient to address the need. In this paper, our aim is to provide a comprehensive study on the state-of-the-art developments papers based on biometrics enabled hardware security.

cs.CR↗

arXiv2019

DRAMNet: Authentication based on Physical Unique Features of DRAM Using Deep Convolutional Neural Networks

Nowadays, there is an increasing interest in the development of Autonomous Vehicles (AV). However, there are two types of attack challenges that can affect AVs and are yet to be resolved, i.e., sensor attacks and vehicle access attacks. This paper, to the best of our knowledge, is the first work that proposes a novel authentication scheme involving DRAM power-up unique features using deep Convolutional Neural Network (CNN), which can be used to implement secure access control of autonomous vehicles. Our approach consists of two parts. First, we convert raw power-up sequence data from DRAM cells into a two-dimensional (2D) format to generate a DRAM image structure. Second, we apply deep CNN to DRAM images, in order to extract unique features from each memory to classify them for authentication. To evaluate our proposed approach, we utilize data from three Commercial-Off-The-Shelf (COTS) DRAMs taken under various environmental and other conditions (high/low temperature, high/low supply voltage and aging effects). Based on our results, our proposed authentication method ``DRAMNet'' achieves 98.63% accuracy and 98.49% precision. In comparison to other state-of-the-art CNN architectures, such as the AlexNet and VGGNet models, our DRAMNet approach fares equally well or better than them.

cs.CR↗

arXiv2018

Secure and Reliable Biometric Access Control for Resource-Constrained Systems and IoT

With the emergence of the Internet-of-Things (IoT), there is a growing need for access control and data protection on low-power, pervasive devices. Biometric-based authentication is promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with biometric processing and template protection are nontrivial for smart cards, key fobs, and so forth. In this paper, we discuss the security, cost, and utility of biometric systems and develop two major frameworks for improving them. First, we introduce a new framework for implementing biometric systems based on physical unclonable functions (PUFs) and hardware obfuscation that, unlike traditional software approaches, does not require nonvolatile storage of a biometric template/key. Aside from reducing the risk of compromising the biometric, the nature of obfuscation also provides protection against access control circumvention via malware and fault injection. The PUF provides non-invertibility and non-linkability. Second, a major requirement of the proposed PUF/obfuscation approach is that a reliable (robust) key be generated from the users input biometric. We propose a noiseaware biometric quantization framework capable of generating unique, reliable keys with reduced enrollment time and denoising costs. Finally, we conduct several case studies. In the first, the proposed noise-aware approach is compared to our previous approach for multiple biometric modalities, including popular ones (fingerprint and iris) and emerging cardiovascular ones (ECG and PPG). The results show that ECG provides the best tradeoff between reliability, key length, entropy, and cost. In the second and third case studies, we demonstrate how reliability, denoising costs, and enrollment times can be simultaneously improved by modeling subject intra-variations for ECG.

cs.CR↗

arXiv2018

Deep RNN-Oriented Paradigm Shift through BOCANet: Broken Obfuscated Circuit Attack

This is the first work augmenting hardware attacks mounted on obfuscated circuits by incorporating deep recurrent neural network (D-RNN). Logic encryption obfuscation has been used for thwarting counterfeiting, overproduction, and reverse engineering but vulnerable to attacks. There have been efficient schemes, e.g., satisfiability-checking (SAT) based attack, which can potentially compromise hardware obfuscation circuits. Nevertheless, not only there exist countermeasures against such attacks in the state-of-the-art (including the recent delay+logic locking (DLL) scheme in DAC'17), but the sheer amount of time/resources to mount the attack could hinder its efficacy. In this paper, we propose a deep RNN-oriented approach, called BOCANet, to (i) compromise the obfuscated hardware at least an order-of magnitude more efficiently (>20X faster with relatively high success rate) compared to existing attacks; (ii) attack such locked hardware even when the resources to the attacker are only limited to insignificant number of I/O pairs (< 0.5\%) to reconstruct the secret key; and (iii) break a number of experimented benchmarks (ISCAS-85 c423, c1355, c1908, and c7552) successfully.

cs.CR↗