In recent years, sensor-based continuous authentication on mobile devices has emerged as a promising solution for enhancing personal information security. However, many existing approaches depend on both legitimate and illegitimate user data for supervised training, which is time-consuming and often impractical in real-world deployments. To address these limitations, we propose CRAFTAuth, a sensor-based continuous Authentication system built upon Context Reconstruction and Adaptive Fusion Transformer. CRAFTAuth leverages built-in smartphone sensors of the accelerometer and gyroscope to capture behavioral biometrics in a non-intrusive manner. The system employs a self-supervised Transformer-based autoencoder to reconstruct long-term behavioral contexts from historical data, enabling the extraction of stable and discriminative features. To enhance adaptability, an iterative attention adaptive fusion mechanism dynamically integrates real-time behavioral inputs with long-term contextual features through progressive spatial-temporal refinement. In addition, a channel merging strategy is incorporated to compress feature representations and reduce computational complexity while preserving temporal dependencies, facilitating efficient deployment on resource-constrained mobile devices. Extensive experiments on our dataset demonstrate that CRAFTAuth achieves state-of-the-art performance, attaining 99.28% accuracy and 0.79% EER, while significantly reducing model size and inference latency compared with existing methods.

Deep neural networks have recently achieved promising performance in the vein recognition task and have shown an increasing application trend. However, they are prone to adversarial attacks by adding imperceptible perturbations to the input, resulting in incorrect recognition. To address this issue, we propose a novel defense model named MsMemoryGAN, which aims to filter the perturbations from adversarial samples before recognition. First, we design a multiscale memory autoencoder (MsMemoryAE) to achieve high-quality reconstruction, where the memory module (MM) within it is capable of learning the detailed patterns of normal samples at different scales. Second, to overcome the limitations of handcrafted similarity metrics, we propose an MM with learnable similarity (LSMM), which retrieves the most relevant memory items to purify the input feature. Finally, the perceptual loss and adversarial loss are integrated with the pixel loss to further enhance the quality of the reconstructed image. During the training phase, the MsMemoryGAN learns to reconstruct the input by merely using fewer prototypical elements of the normal patterns recorded in the memory. At the testing stage, given an adversarial sample, the MsMemoryGAN retrieves its most relevant normal patterns in MMs for reconstruction. Perturbations in the adversarial sample are usually not reconstructed well, resulting in adversarial purification. We conduct extensive experiments on two public vein datasets under different adversarial attack methods to evaluate the performance of the proposed approach. The experimental results show that our approach removes a wide variety of adversarial perturbations, allowing vein classifiers to achieve the highest recognition accuracy.

Owing to its inherent attributes of high security, privacy preservation, and liveness detection, vein recognition has garnered significant attention, with deep learning (DL) models prevailing in the field. In particular, Mamba, a recent DL architecture showing robust feature representation with linear computational complexity, has been applied successfully for visual tasks. However, Vision Mamba captures long-distance feature dependencies but deteriorates local feature details. Besides, manually designing Mamba architecture based on human prior knowledge is very time-consuming and error-prone. To address these limitations, we propose a hybrid network structure named Global-local Vision Mamba (GLVM) to learn both local correlations and global dependencies within images for comprehensive vein feature representation. Second, we design a Multi-head Mamba to learn the dependencies along different directions, so as to improve the feature representation of Vision Mamba. Third, to learn complementary features, we propose a ConvMamba block consisting of three branches: Multi-head Mamba branch (MHMamba), Feature Iteration Unit branch (FIU), and Convolutional Neural Network (CNN) branch, with FIU aiming to fuse convolutional local features with Mamba global representations. Finally, we propose a Global-local Alternate Neural Architecture Search (GLNAS) method, which alternately searches for the optimal architecture of GLVM through weight entanglement strategy and evolutionary algorithm. We have carried out rigorous experiments on five public vein datasets to assess performance. Our approach achieves the highest 96.84%, 99.63%, 95.73%, 99.72%, 99.14% accuracies and the lowest 0.27%, 0.07%, 0.48%, 0.07%, 0.12% EER among all existing approaches on five public vein datasets, which demonstrates that our approach is capable of learning more complete features than existing approaches. In addition, the visual assessment experiments also show that our approach extracts more global vein architecture and local vein detail for recognition.

Palmprint recognition has been extensively studied as an effective biometric technique for personal identification. With the rapid development of deep neural networks (DNNs), palmprint recognition methods have achieved remarkable progress. However, their performance often deteriorates significantly under domain shifts. Moreover, existing unsupervised domain adaptation approaches for palmprint recognition typically suffer from unstable training and imprecise feature alignment, thereby limiting their effectiveness. To address these challenges, we propose SPA, a Stable and Precise Alignment framework for cross-domain palmprint recognition. Specifically, we design a lightweight yet robust Style Transformation Module (STM) to mitigate variations in style, color, and illumination. With the aid of STM, we further align joint feature distributions across all high-level layers, achieving more accurate feature alignment and enhancing recognition robustness. We conduct extensive experiments on two public multi-domain palmprint databases encompassing 42 cross-domain scenarios. The results demonstrate that SPA consistently delivers superior performance across both databases, achieving higher recognition accuracy with lower computational overhead compared to existing methods. In particular, SPA improves the average identification accuracies to 94.21% and 81.93%, while reducing the average equal error rates (EER) to 1.36% and 3.62% on the two databases, respectively.

Real-world datasets often exhibit long-tailed distributions, raising important questions about how privacy risks evolve when machine learning (ML) models are applied to such data. In this work, we present a comprehensive analysis of membership inference attacks in long-tailed scenarios, revealing significant privacy vulnerabilities in tail data. We begin by examining standard ML models trained on long-tailed datasets and identify three key privacy risk effects: amplification, convergence, and polarization. Building on these insights, we extend our analysis to state-of-the-art long-tailed learning methods, such as foundation model-based approaches, offering new perspectives on how these models respond to membership inference attacks across head to tail classes. Finally, we investigate the privacy risks of ML models trained with differential privacy in long-tailed scenarios. Our findings corroborate that, even when ML models are designed to improve tail class performance to match head classes and are protected by differential privacy, tail class data remain particularly vulnerable to membership inference attacks.

Palm-vein recognition is gaining significant attention as a high-security biometric recognition technology. However, the vein image acquisition process is easily affected by several factors, making vein texture segmentation a challenging task. Recently, foundation models such as Segment Anything Model (SAM) have shown remarkable potential in image segmentation without requiring prior retraining. Nevertheless, due to the large domain discrepancy between the resource and target domains, as well as limited datasets, existing solutions that rely heavily on abundant training images often struggle to extract robust vein texture patterns. To address this challenge, we propose AdVeinSAM, an adversarial learning-based large model for palm-vein texture extraction, which leverages rich knowledge of large models to enhance vein pattern segmentation. Specifically, by alternately optimizing the vein segmentation model and the image generator, AdVeinSAM generates diverse training samples, effectively transferring knowledge from the large model to enhance feature extraction robustness. First, we incorporate the wavelet transform into xLSTM-UNet to design Wavelet-xLSTM-UNet, which generates diverse and realistic vein images for data augmentation. Then, we improve the NOLA model to fine-tune the segmentation anything model (SAM) and develop a specialized vein segmentation model (VeinSAM), which effectively extracts palm-vein texture features. Finally, the image generator (Wavelet-xLSTM-UNet) and the vein segmentation model (VeinSAM) are combined to form AdVeinSAM, where the generator and the VeinSAM are alternatively updated through adversarial training. Concretely, the image generator generates challenging samples to increase the segmentation difficulty for VeinSAM, while VeinSAM learns more robust feature representations from these challenging samples to improve the generalization and segmentation accuracy. We conduct extensive experiments on three public palm-vein databases and experimental results demonstrate that the proposed AdVeinSAM model outperforms state-of-the-art solutions, achieving the lowest equal error rates (EERs) of 1.48%, 4.76%, and 0.72%, respectively. These results confirm the effectiveness and robustness of AdVeinSAM in palm-vein texture extraction.

Vein recognition technology offers high security and privacy as an advanced biometric identification method. While deep learning techniques have achieved state-of-the-art performance in vein recognition due to their powerful pattern recognition capabilities, the Gated Recurrent Unit (GRU), a simplified version of LSTM, still faces limitations: 1) inability to process sequence information in parallel, leading to inefficient training; 2) loss of sensitivity to local features crucial for pattern recognition, despite excelling at modeling long-distance dependencies. To address these issues, we propose WTxGRN, a Wavelet Transform-based extended Gated Recurrent Network, which simultaneously extracts global and local features and supports parallel sequence processing. Specifically, we modify the GRU memory structure to enable parallel training and enhance feature representation through exponential gating and stabilization techniques, resulting in an extended GRU architecture called xGRU. We integrate xGRU into a wavelet transform-based residual backbone to form the xGRU Block. By incorporating a wavelet convolution branch and two Mixer Modules, we facilitate multi-scale feature extraction and fusion, enhancing vein recognition robustness and yielding the WTxGRU Block. Stacking these blocks constructs the WTxGRN. Furthermore, we present Spiking WTxGRN, an energy-efficient spiking version of WTxGRN, pioneering the application of spiking neural networks in vein recognition. Spiking WTxGRN offers high energy efficiency while maintaining excellent recognition performance, making it suitable for real-time vein recognition tasks. Extensive experiments on three public palm vein datasets demonstrate that our methods outperform state-of-the-art models across multiple benchmarks, achieving superior performance.

Alzheimer's Disease (AD) is a prevalent neurodegenerative condition where early detection is vital. Handwriting, often affected early in AD, offers a non-invasive and cost-effective way to capture subtle motor changes. State-of-the-art research on handwriting, mostly online, based AD detection has predominantly relied on manually extracted features, fed as input to shallow machine learning models. Some recent works have proposed deep learning (DL)-based models, either 1D-CNN or 2D-CNN architectures, with performance comparing favorably to handcrafted schemes. These approaches, however, overlook the intrinsic relationship between the 2D spatial patterns of handwriting strokes and their 1D dynamic characteristics, thus limiting their capacity to capture the multimodal nature of handwriting data. Moreover, the application of Transformer models remains basically unexplored. To address these limitations, we propose a novel approach for AD detection, consisting of a learnable multimodal hybrid attention model that integrates simultaneously 2D handwriting images with 1D dynamic handwriting signals. Our model leverages a gated mechanism to combine similarity and difference attention, blending the two modalities and learning robust features by incorporating information at different scales. Our model achieved state-of-the-art performance on the DARWIN dataset, with an F1-score of 90.32% and accuracy of 90.91% in Task 8 ('L' writing), surpassing the previous best by 4.61% and 6.06% respectively.

For data augmentation (DA), Generative Adversarial Networks (GANs) are typically integrated with CNNs or MLPs to generate samples in classification and segmentation tasks. For classification, categorical ground truth is leveraged in conditional GANs to generate samples for each class. For regression, data generation becomes complex as the aim now is to generate both the samples (images) and their continuous ground truth vectors. GANs for classification can no longer, therefore, be leveraged for DA on regression. To address this issue, we propose GT&I_GAN, a novel GAN-based DA model that generates jointly image samples and their ground truth continuous vectors by learning their conjoint distribution. The main idea behind GT$\mathbf{\& I-GAN}$is to add, to the RGB sample image, an additional (fourth) channel associated with the ground vector. GT&I_GAN offers the great advantage of generating conjointly the samples and their ground truths by a single model without needing an additional network. We assess our approach on an image dataset where the ground truth consists of a high dimensional vector of continuous values. The results show that the synthetic data consisting of the image & ground truth vector pairs are realistic and allow improving the CNN regressor performance. Moreover, we show that our GT&I_GAN can be leveraged seamlessly for segmentation tasks by adding, in a similar way, the ground truth segmentation mask as an additional channel to the input RGB image.

Palm vein recognition has attracted recently wide attention thanks to its robust feature representation and high accuracy. Despite advancements in the literature, however, existing solutions suffer from the following issues: 1) Insufficient large-scale data for deep learning-based recognition of vein biometrics, resulting in decreased generalization performance and model accuracy. 2) Lack of methods based on machine learning convolutional neural networks capable of capturing the global receptive field for vein biometric recognition. In addressing these issues, this paper proposes a method to acquire the global receptive field, termed G AN et, which extracts vein features using Gabor filters and computes an attention mechanism to capture the global receptive field for downstream palm vein recognition models. Initially, vein features are extracted using multi-scale fixed Gabor filters and multi-scale adaptive Gabor filters. Subsequently, self-attention mechanisms are employed to compute relationships between blocks to obtain the global receptive field. To perform recognition, the Euclidean distance between feature vectors is then computed. Our experiments on three datasets show that our approach outperforms existing palm vein recognition methods.

Data mixing augmentation has been widely applied to improve the generalization ability of deep neural networks. Recently, offline data mixing augmentation, e.g. handcrafted and saliency information-based mixup, has been gradually replaced by automatic mixing approaches. Through minimizing two sub-tasks, namely, mixed sample generation and mixup classification in an end-to-end way, AutoMix significantly improves accuracy on image classification tasks. However, as the optimization objective is consistent for the two sub-tasks, this approach is prone to generating consistent instead of diverse mixed samples, which results in overfitting for target task training. In this paper, we propose AdAutomixup, an adversarial automatic mixup augmentation approach that generates challenging samples to train a robust classifier for image classification, by alternatively optimizing the classifier and the mixup sample generator. AdAutomixup comprises two modules, a mixed example generator, and a target classifier. The mixed sample generator aims to produce hard mixed examples to challenge the target classifier, while the target classifier's aim is to learn robust features from hard mixed examples to improve generalization. To prevent the collapse of the inherent meanings of images, we further introduce an exponential moving average (EMA) teacher and cosine similarity to train AdAutomixup in an end-to-end way. Extensive experiments on seven image benchmarks consistently prove that our approach outperforms the state of the art in various classification scenarios. The source code is available at https://github.com/JinXins/Adversarial-AutoMixup.

Palm-vein identification is a highly secure pattern biometrics that has become an active research area in recent years. Despite the recent progress in deep neural networks (DNNs) for vein identification, existing solutions for feature representation continue to lack robustness due to the limited training samples. To address this limitation, data augmentation approaches, including Generative Adversarial Networks (GANs), have been investigated, but these schemes suffer from the following issues. First, it is practically unfeasible to use all the generated samples for classifier training due to the limited storage space and computation resources. Further, some of these generated samples may be non-representative or ineffective, seriously compromising models’ generalization capabilities. Second, the augmented dataset is fed to the target classifier repeatedly, resulting in overfitting after substantial training epochs. To tackle the above problems, we propose AdveinAU, an Adversarial vein AUtomatic AUgmentation approach that generates challenging samples to train a more robust vein classifier for palm-vein identification by alternatively optimizing the vein classifier and a set of latent variables. First, we consider a conditional deep convolution generative adversarial net (cDCGAN) to learn the distribution of real data and the generated data, and then a latent variable from the latent variable space is mapped to the sample space. Second, we combine the trained generator with the vein classifier to constitute AdveinAU, where the input sets of the generator and the classifier are alternatively updated by adversarial training. Specifically, a latent variable set is learned to increase the training loss of a target network through generating adversarial samples, while the classifier learns more robust features from harder examples to improve the generalization. To avoid collapsing inherent meanings of images, an exponential moving average (EMA) teacher andcosinesimilarity are employed for regularization to reduce the search space. Unlike previous works where GANs synthesize new realistic images, our model aims to search a latent variable set, based on which the generator can produce challenging samples along with the training process to improve the classifier’s performance. Finally, we conduct extensive experiments on three public palm-vein datasets to evaluate the performance of AdveinAU, and the experimental results demonstrate that the proposed AdveinAU is capable of generating harder samples to improve the performance of the vein classifier.

Finger-vein recognition has attracted extensive attention due to its exceptional level of security and privacy. Recently, deep neural networks (DNNs), such as convolutional neural networks (CNNs) showing robust capacity for feature representation, have been proposed for vein recognition. The architectures of these DNNs, however, have primarily been manually designed based on human prior knowledge, which is both time-consuming and error-prone. To overcome these problems, we propose AG-NAS, an Attention Gated recurrent unit-based Neural Architecture Search to automatically search for the optimal network architecture, thereby improving the recognition performance for different finger-vein recognition tasks. First, we combine the self-attention mechanism and gated recurrent unit (GRU) to propose an attention GRU module employed as a controller to generate the architectural hyperparameters of candidate neural networks automatically. Second, we investigate a parameter-sharing supernet policy to reduce the search space, computation, and time costs. Finally, we conduct rigorous experiments on our finger-vein database and two public finger-vein databases. The experimental results demonstrate that the proposed AG-NAS outperforms the representative approaches and achieves state-of-the-art recognition accuracy.

Finger-vein biometrics has recently gained significant attention due to its robust privacy and high security features. Despite notable advancements, most existing methods focus on extracting features from a 2-dimensional (2D) image projected from 3D vein vessels with a single view. However, recognition based on a single view is prone to errors due to variations in finger positioning, especially those caused by finger roll movements, which can degrade recognition performance. To address this challenge, we propose ABLSTM-TSVT, an Attention Bidirectional LSTM-based Temporal-Spatial Vein Transformer for multi-view finger-vein recognition. First, we enhance LSTM with an attention mechanism to create an attention LSTM for extracting temporal features. We further improve this by introducing a local attention module, which learns temporal dependencies between a patch (token) and its adjacent patches across multiple views, integrating it with the attention LSTM to form a temporal attention module. Second, we develop a spatial attention module that captures the spatial dependencies of patches within an image. Finally, merging the temporal and the spatial attention modules, we create our temporal-spatial transformer model, which effectively represents features from multi-view images. Experimental results on two multi-view datasets demonstrate that our approach outperforms state-of-the-art approaches in enhancing identification accuracy and reducing verification errors in vein classifiers.

The exceptional feature extraction capabilities of deep neural networks (DNNs) have significantly advanced palmprint recognition. However, DNNs typically require training and testing data originate from the same distribution, which limits their practical applications. Moreover, existing unsupervised domain adaptation methods struggle to achieve high accuracy with efficiency. To address these challenges, we propose LSFM, an efficient Light Style and Feature Matching method that enhances palmprint recognition performance in cross-domain scenarios with fewer resources. Specifically, we develop an efficient style transfer model to mitigate domain shifts at the pixel level. We then align features across multiple task-specific layers in high dimensional space to reduce domain discrepancies, further improving cross-domain performance. Finally, we evaluate the effectiveness of the proposed LSFM through extensive experiments on two public multi-domain palmprint databases. The experimental results demonstrate that LSFM achieves superior performance with significantly reduced resource consumption, improving average accuracy to 94.87% and lowering the average equal error rate to 1.46%, while saving over 80% of resources.

Over the last years, sensor-based continuous authentication on mobile devices has achieved great success on personal information protection. These proposed mechanisms, however, require both legal and illegal users’ data for authentication model training, which takes time and is impractical. In this paper, we present MAuGANs, a lightweight and practical Memory-Augmented Autoencoder-based continuous Authentication system on smartphones with conditional transformer Generative Adversarial Networks (GANs), where the conditional transformer GANs (CTGANs) are used for data augmentation and the memory-augmented autoencoder (MAu) is utilized to identify users. Specifically, MAuGANs exploits the smartphone built-in accelerometer and gyroscope sensors to implicitly collect users’ behavioral patterns. With the normalized legitimate user's sensor data, MAuGANs uses a CTGAN composed of a conditional transformer-based generator and a conditional transformer-based discriminator to create additional training data for the MAu. Then, the MAu is trained on the augmented legitimate user's data. The trained MAu reconstructs the current user data and then calculates the reconstruction error between the reconstructed data and current user data. To carry out user authentication, MAuGANs compares the reconstruction error with a predefined authentication threshold. We evaluate the performance of MAuGANs on our dataset, where our extensive experiments demonstrate that MAuGANs reaches the best authentication performance, when comparing with the representative state-of-the-art methods, by 0.33% EER and 99.65% accuracy on 10 unseen users.

Deep neural networks can be easily fooled by the adversarial example, which is a specially crafted example with subtle and intentional perturbations. A plethora of papers have proposed to use filters to effectively defend against adversarial example attacks. However, we demonstrate that the automatic filter-based defenses may not be reliable. In this article, we present URL2AED, Using a Reinforcement Learning scheme TO escape the automatic filter-based Adversarial Example Defenses. Specifically, URL2AED uses a specially crafted policy gradient reinforcement learning (RL) algorithm to generate adversarial examples (AEs) that can escape automatic filter-based AE defenses. In particular, we properly design reward functions in policy-gradient RL for targeted attacks and non-targeted attacks, respectively. Furthermore, we customize training algorithms to reduce the possible action space in policy-gradient RL to accelerate URL2AED training while still ensuring that URL2AED generates successful AEs. To demonstrate the performance of the proposed URL2AED, we conduct extensive experiments on three public datasets in terms of different perturbation degrees of parameter, different filter parameters, transferability, and time consumption. The experimental results show that URL2AED achieves high attack success rates for automatic filter-based defenses and good cross-model transferability.

Graph embedding converts a graph data into a low dimensional space to preserve the original graph information. However, graph data can be reconstructed by malicious adversaries to train machine learning models from graph embeddings. This paper studies to what extent an adversary (without the original graph data) can recover the original graph data from graph embeddings. To quantify the original graph information leakage from graph embeddings, we develop a deep neural network model InferNet that can be used by adversaries to infer the original graph information from an adversary-accessible graph embedding database. More specifically, we propose the data-free reversed knowledge distillation technique to support the InferNet training even if the original graph dataset is absent. To ensure the performance of InferNet, we design two cycle-consistency loss functions to have an interactive training of InferNet over three series of datasets. To further enhance the performance of InferNet, we provide a joint training algorithm that simultaneously trains the pseudo-sample generator and InferNet, which significantly reduces the storage space. We evaluate the performance of InferNet on three datasets, and the intensive experiments demonstrate that InferNet can infer the original graph information from the graph embedding dataset with high accuracy.

This paper studies to what extent an adversary (without the original graph data) can recover the original raw graph data from graph embeddings. To quantify the original graph data information leakage from graph embeddings, we develop a deep neural network model InferNet that can be used by adversaries to infer the original graph data information from an adversary-accessible graph embedding database. Specifically, we propose the data-free reversed knowledge distillation (KD) technique to support InferNet training even if the original graph dataset is absent. To improve the performance of InferNet, we design two cycle-consistency loss functions to have an interactive training of InferNet over three series of datasets. Our intensive experiments demonstrate that InferNet can infer the original graph data information from the graph embedding dataset with high accuracy.

Multi-view finger-vein recognition technology has attracted increasing attentions in recent years. Despite recent advances in the multi-view finger-vein identification, existing solutions employ multiple monocular cameras from different views to record two-dimensional (2D) projections of 3D vein vessels, which causes the following problems: 1) 2D images collected from limited views (two or three views) are insufficient for robust 3D vein vessel feature representation. Furthermore, image sequences of the same finger acquired from different views usually show significant differences. As a result, the existing works are still sensitive to positional variations of the fingers, specifically those caused by finger roll movements. 2) Using multiple cameras can lead to increased costs. Moreover, it is impossible to employ several cameras to acquire full-view images because of the limited space on capturing devices. To address the above issues, we present$\mathbb {FV}$-LT, a Full-View Finger-Vein identification system based on a Local attention Transformer, by implementing an image acquisition device with a single camera. First, we design and implement a finger-vein acquisition prototype device that utilizes a single camera and a LED group to rotate along a finger for full-view image collection. This allows capturing all vein patterns concealed beneath human skin to form a complete representation of finger features. Second, given the full-view vein images, we propose a local attention transformer-based approach to extract dependency features of a token (a patch or an image) on its neighborhood’s tokens among image patches and among full-view images, respectively. These dependency features are shown to be robust to positional variations induced by finger rolls. Based on the public database of full-view finger-vein images captured by our designed device and a single-view database, we verify the performance of the proposed$\mathbb {FV}$-LT. The experimental results show that$\mathbb {FV}$-LT significantly outperforms existing 2D/multi-view based approaches with respect to improving the tolerance against finger roll and achieving the state-of-the-art identification accuracy.

Vein biometrics is a high security and privacy preserving identification technology that has attracted increasing attention over the last decade. Deep neural networks (DNNs), such as convolutional neural networks (CNN), have shown strong capabilities for robust feature representation, and have achieved, as a result, state-of-the-art performance on various vision tasks. Inspired by their success, deep learning models have been widely investigated for vein recognition and have shown significant improvement of identification accuracy compared to handcrafted models. Existing deep learning models, however, are vulnerable to adversarial perturbation attacks, where thoughtfully crafted small perturbations can cause misclassification of legitimate images, degrading, thereby, the efficiency of vein recognition systems. To address this problem, we propose, in this paper, VeinGuard, a novel defense framework to defend deep learning classifiers against adversarial palm-vein image attacks, composed of a local transformer-based GAN and a purifier. VeinGuard comprises two components: a local transformer-based GAN (LTGAN) that learns the distribution of unperturbed vein images and generates high-quality palm-vein images, and a purifier consisting of a trainable residual network and of a pre-trained generator from LTGAN that automatically removes a wide variety of adversarial perturbations. The resulting clean images are fed to vein classifiers for identification, thereby avoiding adversarial attacks. We evaluate VeinGuard on three public vein datasets in terms of white-box attacks, black-box attacks, ablation experiments, and computation time. The experimental results show that VeinGuard allows filtering the perturbations and enables the classifiers to achieve state-of-the-art recognition results for different adversarial attacks.

Mobile devices are becoming increasingly popular and are playing significant roles in our daily lives. Insufficient security and weak protection mechanisms, however, cause serious privacy leakage of the unattended devices. To fully protect mobile device privacy, we propose ADFFDA, a novel mobile continuous authentication system using an Adaptive Deep Feature Fusion scheme for effective feature representation, and a transformer-based GAN for Data Augmentation, by leveraging smartphone built-in sensors of the accelerometer, gyroscope and magnetometer. Given the normalized sensor data, ADFFDA utilizes the transformer-based GAN consisting of a transformer-based generator and a CNN-based discriminator to augment the training data for CNN training. With the augmented data and the especially-designed CNN based on the ghost module and ghost bottleneck, ADFFDA extracts deep features from the three sensors by the trained CNN, and exploits an adaptive-weighted concatenation method to adaptively fuse the CNN-extracted features. Based on the fused features, ADFFDA authenticates users by using the one-class SVM (OC-SVM) classifier. We evaluate the authentication performance of ADFFDA in terms of the efficiency of the transformer-based GAN, GAN-based data augmentation, CNN architecture, adaptive-weighted feature fusion, OC-SVM classifier, and security analysis. The experimental results show that ADFFDA obtains the best authentication performance w.r.t representative approaches, by achieving a mean equal error rate of 0.01%.

Power iteration has been applied to compute the eigenvectors of the similarity matrix in spectral clustering tasks . However, these power iteration based clustering methods usually suffer from the following two problems: (1) the power iteration usually converges very slowly; (2) the singular value decomposition method adopted to obtain the eigenvectors of the similarity matrix is time-consuming. To solve these problems, we propose a novel clustering method named Ada ptive P ower I teration C lustering (AdaPIC). Specifically, AdaPIC employs a sequence of rank-one matrices to approximate the normalized similarity matrix. Then, the first K + 1 eigenvectors can be computed in parallel, and the stopping condition of power iteration can be automatically yielded based on the target clustering error. We performed extensive experiments on public datasets to demonstrate the effectiveness of the proposed AdaPIC method, comparing with leading baseline methods . The experimental results indicate that the proposed AdaPIC algorithm has a competitive advantage in running time. The running time taken by spectral clustering baseline methods is usually more than 2.52 times of that taken by AdaPIC. For clustering accuracy, AdaPIC outperforms classic PIC by 97% on average, over all experimental datasets . Moreover, AdaPIC achieves comparable clustering accuracy with other 3 baseline methods, and achieves 6%–15% better clustering accuracy than the remaining 6 state-of-the-art baseline methods.

Despite recent advances of deep neural networks in hand vein identification, the existing solutions assume the availability of a large and rich set of training image samples. These solutions, therefore, still lack the capability to extract robust and discriminative hand-vein features from a single training image sample. To overcome this problem, we propose a single-sample-per-person (SSPP) palm-vein identification approach, where only a single sample per class is enrolled in the gallery set for training. Our approach, named MSMDGAN + CNN, consists of a multi-scale and multi-direction generative adversarial network (MSMDGAN) for data augmentation and a convolutional neural network (CNN) for palm-vein identification. First, a novel data augmentation approach, MSMDGAN, is developed to learn the internal distribution of patches in a single image. The proposed MSMDGAN consists of multiple fully convolutional GANs, each of which is responsible for learning the patch distribution within an image at a different scale and at a different direction. Second, given the resulting augmented data by MSMDGAN, we design a CNN for single sample palm-vein recognition. The experimental results on two public hand-vein databases demonstrate that MSMDGAN is able to generate realistic and diverse samples, which, in turn, improves the stability of the CNN. In terms of accuracy, MSMDGAN + CNN outperforms other representative approaches and achieves state-of-the-art recognition results.

Finger-vein verification is a highly secure biometric authentication that has been widely investigated over the last years. One of its challenges, however, is the possible degradation of image quality, that results in spurious and missing vein patterns, which increases the verification error. Despite recent advances in finger-vein quality assessment, the proposed solutions are limited as they depend on human expertise and domain knowledge to extract handcrafted features for assessing quality. We have proposed, recently, the first deep neural network (DNN) framework for assessing finger-vein quality, that does not require manual labeling of high and low quality images, as is the case for state of the art methods, but infers such annotations automatically based on an objective indicator, the biometric verification decision. This framework has significantly outperformed the existing methods, whether the input image is in grayscale or is binary. Motivated by these performances, we propose, in this work, a representation learning of finger vein image quality, where a DNN takes as input conjointly the grayscale and binary versions of the input image to predict vein quality. Our model allows to learn the joint representation from grayscale and binary images, for quality assessment. The experimental results, obtained on a large public dataset, demonstrates that our proposed method accurately identifies high and low quality images, and outperforms other techniques in terms of equal error rate (EER) minimization, including our previous DNN models, based either on grayscale or binary input.

Finger-vein biometrics has been extensively investigated for personal authentication. One of the open issues in finger-vein verification is the lack of robustness against image-quality degradation. Spurious and missing features in poor-quality images may degrade the system's performance. Despite recent advances in finger-vein quality assessment, current solutions depend on domain knowledge. In this paper, we propose a deep neural network (DNN) for representation learning to predict image quality using very limited knowledge. Driven by the primary target of biometric quality assessment, i.e., verification error minimization, we assume that low-quality images are falsely rejected in a verification system. Based on this assumption, the low- and high-quality images are labeled automatically. We then train a DNN on the resulting data set to predict the image quality. To further improve the DNN's robustness, the finger-vein image is divided into various patches, on which a patch-based DNN is trained. The deepest layers associated with the patches form together a complementary and an over-complete representation. Subsequently, the quality of each patch from a testing image is estimated and the quality scores from the image patches are conjointly input to probabilistic support vector machines (P-SVM) to boost quality-assessment performance. To the best of our knowledge, this is the first proposed work of deep learning-based quality assessment, not only for finger-vein biometrics, but also for other biometrics in general. The experimental results on two public finger-vein databases show that the proposed scheme accurately identifies high- and low-quality images and significantly outperforms existing approaches in terms of the impact on equal error-rate decrease.

Finger-vein biometrics has been extensively investigated for personal verification. Despite recent advances in finger-vein verification, current solutions completely depend on domain knowledge and still lack the robustness to extract finger-vein features from raw images. This paper proposes a deep learning model to extract and recover vein features using limited a priori knowledge. First, based on a combination of the known state-of-the-art handcrafted finger-vein image segmentation techniques, we automatically identify two regions: a clear region with high separability between finger-vein patterns and background, and an ambiguous region with low separability between them. The first is associated with pixels on which all the above-mentioned segmentation techniques assign the same segmentation label (either foreground or background), while the second corresponds to all the remaining pixels. This scheme is used to automatically discard the ambiguous region and to label the pixels of the clear region as foreground or background. A training data set is constructed based on the patches centered on the labeled pixels. Second, a convolutional neural network (CNN) is trained on the resulting data set to predict the probability of each pixel of being foreground (i.e., vein pixel), given a patch centered on it. The CNN learns what a finger-vein pattern is by learning the difference between vein patterns and background ones. The pixels in any region of a test image can then be classified effectively. Third, we propose another new and original contribution by developing and investigating a fully convolutional network to recover missing finger-vein patterns in the segmented image. The experimental results on two public finger-vein databases show a significant improvement in terms of finger-vein verification accuracy.