Vision-language pre-trained models (VLMs) have shown impressive cross-modal understanding, yet their “compositional understanding” ability remains under investigation. We introduce CompoVis, a framework for visually probing cross-modal gaps in VLMs. CompoVis optimizes the grid layout to highlight alignment clusters and boundaries, visually interprets multi-head attention and semantic drift, and enables interactive fine-tuning unconstrained by closed datasets or offline models. Quantitative experiments and case studies explore key insights: VLMs rely on entity shortcuts rather than comprehension-driven; stubborn global modality isolation and suboptimal fine-grained alignment remain; fine-tuning with negative samples does not fundamentally alleviate the gaps. Approximately 89% of participants ($n=27$) found that, compared to methods relying solely on data metrics, CompoVis offers a more innovative and effective approach for investigating modality gaps in VLMs.

Fingerprints, due to their uniqueness and stability, have become the most widely used biometric feature. Automated Fingerprint Recognition Systems (AFRS) have been applied in various scenarios for identity verification and access control. However, these systems have long faced serious threats from presentation attacks (PA), posing potential risks of privacy breaches and financial loss. Optical Coherence Tomography (OCT), as a non-invasive imaging technology, aligns with the demand for more secure and stable fingerprint recognition methods. By integrating OCT into AFRS, fingerprint recognition can be extended from traditional 2D surface fingerprints to OCT fingerprints containing 3D fingertip information. The rich and difficult-to-replicate structural details encoded in OCT fingerprints offer a highly promising solution for fingerprint presentation attack detection (PAD). Currently, publicly available OCT fingerprint datasets remain limited, and those specifically designed for PAD research are even rarer. This scarcity of data has significantly constrained research in OCT-based fingerprint PAD. To address this gap, a dedicated database for OCT fingerprint anti-spoofing, referred to as the ZJUT-PAD, has been designed and released by our research team. This database consists of 175 Presentation Attack Instruments (PAIs) made from 10 different materials, covering 35 distinct types. Each PAI was captured five times using two different OCT devices, resulting in a total of 1,750 PA instances. This database serves as a critical evaluation platform for OCT fingerprint PAD research, enabling a comprehensive assessment of performance, generalization capability, and cross-device robustness of PAD methods.

Optical coherence tomography (OCT) is a non-invasive imaging technique capable of acquiring depth information up to 1-3mm beneath the skin surface, including the stratum corneum and viable epidermis regions. This technique allows for the reconstruction of internal and external fingerprint images from grayscale data. However, existing fingerprint extraction methods heavily rely on contour features and current 2D approaches overlook the spatial continuity of biometric features in OCT images. Therefore, this paper proposes a novel iterative algorithm for internal and external fingerprint extraction from OCT images. This algorithm incorporates the spatial continuity of OCT slice images and an iterative feature enhancement module during the prediction phase to improve segmentation continuity. Additionally, a soft label technique is employed to reduce contour dependence and mitigate interference from noise and anomaly interference. Qualitative and quantitative experiments demonstrate significant improvements in segmentation accuracy with higher fingerprint quality, validating the effectiveness of the proposed approach.

In the complex landscape of the Internet, inter-domain routing systems are essential for ensuring seamless connectivity and reachability across autonomous systems. However, the lack of dependable security validation mechanisms in these systems poses persistent challenges. Vulnerabilities such as prefix hijacking, path forgery, and route leakage not only compromise network operators and users, but also threaten the stability and accessibility of the Internet’s core infrastructure. To address this, visualization and visual analytics techniques are adept at identifying and detecting security threats, offering network administrators effective methods to monitor and maintain network operations. This paper presents a comprehensive survey of the state-of-the-art research in visualization and visual analytics for inter-domain routing security. We delineate four scenarios for tasks analysis in network visualization: monitoring, detection, verification, and discovery. Each category is explored in detail, focusing on the employed data sources and visualization techniques. Several key findings are presented at the end of each category, aimed at providing researchers and practitioners with research inspiration. Furthermore, we examine the trends of academic interest observed in recent decades and propose potential directions for future research in visual analytics pertaining to Internet infrastructure security.

Optical Coherence Tomography (OCT) is a high-resolution, non-invasive imaging technology increasingly used for biometric data collection from fingertips. OCT captures volume data up to 3mm below the skin surface in the form of a series of B-scan images, enabling the reconstruction of internal fingerprints (IF) and internal sweat pores (ISP), thereby enhancing the security of biometric recognition. Despite the advantages, OCT images suffer from speckle noise and tissue discontinuity, making the extraction of subcutaneous biometric features challenging. Traditional hardware and software-based enhancement methods often result in over-smoothing and structural loss. Recent advancements in deep learning (DL) offer promising alternatives, with supervised DL methods showing efficacy when trained with high-quality paired datasets. However, the absence of ground-truth (GT) data makes it impossible to apply these models. This study proposes a novel supervised enhancement method for fingertip OCT images, with a paired dataset generation strategy. An OCT few-shot GAN and a Quality Estimation Module are proposed and incorporated into the strategy to realize translation from minimal GT manual augmentation to high-quality paired dataset, effectively addressing the challenge of data scarcity. A Fast Supervised Enhancement GAN (FSE-GAN) is proposed thereafter to perform simultaneous speckle noise reduction and tissue structure restoration, facilitating accurate extraction of internal fingerprints and sweat pores. Experiments demonstrate that the enhanced images significantly simplify IF and ISP extraction while achieving outstanding result quality.

As essential biometric features, fingerprints have been widely utilized in various security domains. However, the performance of conventional Automated Fingerprint Identification Systems (AFISs) is limited by the quality of the external fingerprint (EF), particularly in cases involving damaged or deformed prints. Using the internal fingerprint (IF) acquired by Optical Coherence Tomography (OCT) to address these limitations has emerged as a promising method. IFs can compensate for and restore missing ridge pattern features in degraded EFs, thereby improving the overall recognition accuracy of AFIS. However, the reconstruction of IF was significantly constrained by speckle noise in OCT images, making the accurate extraction of finger tissue contours complex and computationally intensive. To improve the applicability of OCT fingerprint, this paper proposes a Neural Architecture Search (NAS)-based OCT fingertip internal contour regression network, denoted as CRM-NAS. The CRM-NAS employs a NAS-based internal feature extraction module (NAS-IEM) to adaptively optimize the network architecture and complexity with noisy OCT fingertip data, facilitating the effective capture of global internal contour features. Furthermore, an attention-based contour regression module (Att-CRM) is introduced to refine local contour details by leveraging multi-scale intermediate features extracted from different network layers and to enable the generation of continuous and accurate internal contours. Experimental results demonstrate that CRM-NAS not only outperforms existing methods in terms of contour extraction accuracy, fingerprint reconstruction quality, and verification performance, but also maintains a relatively compact parameter size.

Advancements in internal fingerprint extraction technology have made the fusion of external and internal fingerprints possible. It offers a viable solution to the problem of degraded performance in Automatic Fingerprint Identification System (AFIS) caused by epidermal abrasion and aging. Traditional fusion methods focus on information maximization. But for fingerprint, features like wrinkles and scars often yield high gradient variation information. It is detrimental to generating high-quality fingerprint. To address this, we propose a novel quality driven fusion method for external and internal fingerprints. It comprises several components. Firstly, there is a lightweight and efficient Transformer-CNN hybrid model. Secondly, it includes a closed-loop quality driven fusion mechanism. This mechanism is equipped with a quality prediction module, Weighted Complementary Fusion (WCF), and quality feedback. Thirdly, there is a jointly optimized combined loss function, which is accompanied by an asynchronous cross-training strategy. Unlike traditional paradigms, we change the optimization objective. It is shifted from information maximization to quality maximization, which is more appropriate for fingerprint. Experimental evaluations have been conducted, covering aspects such as fingerprint quality, matching performance, and network model ablation. The method we proposed demonstrates superiority in terms of quality score and matching performance. It outperforms both traditional and state-of-the-art approaches. It gives a new research path to boost fingerprint identification performance in identity security authentication.

External fingerprints (EFs) based only on epidermal information are vulnerable to spoofing attacks and non-ideal skin conditions. To solve such shortcomings, internal fingerprints (IFs) collected using optical coherence tomography (OCT) have been proposed and widely researched. However, the development of IF is limited by the lack of in-depth researches on the IF and the EF-IF interoperability, which is partially caused by the lack of public OCT database. The obvious gap in the applications of EF and IF recognition motivated us to design and publish a comprehensive fingerprint database containing both traditional EFs and OCT IFs, denoted as ZJUT-EIFD. To the best of our knowledge, ZJUT-EIFD is the first public database that combines OCT and total internal reflection (TIR) via synchronous acquisition, with 399 different fingers from 60 subjects. In this article, the composition of the database, the quality of EFs and IFs, and the verification performance of different types of fingerprints were detailed. In addition, potential application directions of ZJUT-EIFD were demonstrated. ZJUT-EIFD can serve benchmarks and interoperability tests for EF-IF research, which will promote the research and development of EF and IF.

Chinese characters are complex and contain discriminative information, meaning that their writers have the potential to be recognized using less text. In this study, offline Chinese writer identification based on a single character was investigated. To extract comprehensive features to model Chinese characters, explicit and implicit information as well as global and local features are of interest. A dual-branch multitask fusion network is proposed that contains two branches for global and local feature extraction simultaneously, and introduces auxiliary tasks to help the main task. Content recognition, stroke number estimation, and stroke recognition are considered as three auxiliary tasks for explicit information. The main task extracts implicit information of writer identity. The experimental results validated the positive influences of auxiliary tasks on the writer identification task, with the stroke number estimation task being most helpful. In-depth research was conducted to investigate the influencing factors in Chinese writer identification, with respect to character complexity, stroke importance, and character number, which provides a systematic reference for the actual application of neural networks in Chinese writer identification.

Vision algorithms face challenges of limited visual presentation and unreliability in pedestrian activity assessment. In this article, we introduce LANDER, an interactive analysis system for visual exploration of pedestrian activity and uncertainty in surveillance videos. This visual analytics system focuses on three common categories of uncertainties in object tracking and action recognition. LANDER offers an overview visualization of activity and uncertainty, along with spatio-temporal exploration views closely associated with the scene. Expert evaluation and user study indicate that LANDER outperforms traditional video exploration in data presentation and analysis workflow. Specifically, compared to the baseline method, it excels in reducing retrieval time ($p< $0.01), enhancing uncertainty identification ($p< $0.05), and improving the user experience ($p< $0.05).

Optical coherence tomography (OCT), as a non-destructive and high-resolution imaging technique, has been used to collect 3D fingertip data, which contains surface and internal fingerprints. Methods have been proposed for OCT fingerprint reconstruction. However, these methods have complex processing flow and are time consuming. In this paper, an end-to-end convolutional neural network based surface and internal fingerprint reconstruction method is proposed. A simple yet effective contour regression module is proposed and integrated in the network for direct estimation of contours of stratum corneum and viable epidermis junction from noisy OCT volume data, thus greatly simplify the processing flow. The proposed network further integrates multi-task learning with conventional segmentation task as auxiliary task and contour regression task as main task to facilitate the feature extraction and improve the robustness of the network. Depthwise separable convolution is adapted to a light-weight network for network computation complexity reduction. To the best of our knowledge, it is the first time that an end-to-end method is proposed for surface and internal fingerprint extraction from noisy OCT volume data. Experiments and comparisons are carried out in terms of contour estimation accuracy, fingerprint quality, fingerprint matching performance and computation efficiency. Compared with conventional method, the proposed method utilizes only 6% of original network parameters and 0.7% of original computation time, but achieves comparably results. Fingerprint by depth proves the accuracy and robustness of contour regression than pixel-wise layer segmentation. The proposed method is noise-insensitive, process-simple and time-efficient for OCT fingerprint reconstruction, which is significant for real time application in Automated Fingerprint Recognition Systems.

Presentation attack detection (PAD) is a critical component of automated fingerprint recognition systems (AFRSs). However, existing PAD technologies based on optical coherence tomography (OCT) mainly rely on local information, ignoring the global continuity and correlation of physiological structures. Furthermore, the lack of appropriate presentation attack instruments (PAIs) that cater to the unique OCT characteristics leads to the insufficient evaluation of PAD. The identification features, including external fingerprint (EF), internal fingerprint (IF), and subcutaneous sweat pore (SSP), provide valuable information about the intrinsic connections of physiological structures. Such intrinsic connections hold potential clues for PAD. Building upon this premise, this paper proposed a novel PAD method based on three OCT hand-crafted features: EF-IF self-matching score (SMS), SSP number (SN), and SSP coincidence rate (SCR). These simple yet effective PAD features offer a more precise and detailed description of the internal physiological structure, enabling accurate distinction between presentation attack (PA) and bona-fide. The proposed method achieves a 4% Equal Error Rate (EER), significantly outperforming other existing PAD methods. Additionally, the cross-device experiment demonstrates the generalization capability of the proposed method on both our dataset and the public OCT dataset.

Due to the all-weather and all-time imaging capability of Synthetic Aperture Radar (SAR), SAR remote sensing analysis has attracted much attention recently. However, compared with optical images, SAR images are more difficult to be interpreted. If a SAR image could be translated into its corresponding optical image, then the generated optical image would be helpful for assisting the interpretation. Addressing this issue, we investigate how to translate SAR images to optical ones in this work, and propose a parallel generative adversarial model for SAR-to-optical image translation, called Parallel-GAN, consisting of a backbone image translation sub-network and an adjoint optical image reconstruction sub-network. Under the proposed model, the backbone image translation sub-network is designed to translate SAR images to optical ones, and simultaneously some of its intermediate layers are required to output similar latent features to those from the corresponding layers of the adjoint image reconstruction sub-network. Thanks to the imposed hierarchical latent optical features, the proposed Parallel-GAN could achieve the SAR-to-optical image translation effectively. Extensive experimental results on three public datasets demonstrate that the proposed method outperforms ten state-of-the-art methods for SAR-to-optical image translation.

Abstract Abstract Sweat pore, one of the level 3 features of fingerprint, has attracted much attention in fingerprint recognition. Traditional sweat pores on surface fingerprint are unclear or blurred when fingers are stained or damaged. Subcutaneous sweat pores, as cross section of the sweat glands, are resistant to external interferences. With 3D fingertip information measured by optical coherence tomography (OCT), the subcutaneous sweat pore estimation from OCT volume data is investigated. First, an adaptive subcutaneous pore image reconstruction method is proposed. It utilizes the skin surface and viable epidermis junction as reference and realizes depth‐adaptive pore image reconstruction. Second, a dilated U‐Net combining the U‐Net with dilated convolution is proposed for subcutaneous sweat pore extraction, which can prevent information loss of sweat pores caused by downsampling. To the best knowledge, it is the first time that subcutaneous sweat pore extraction is investigated and proposed. Experiments on subcutaneous pore image reconstruction and sweat pore extraction are both conducted. The qualitative and quantitative results show that the proposed adaptive method performs better in subcutaneous pore image reconstruction compared with the fix‐depth method, and the dilated U‐Net outperforms other methods on subcutaneous sweat pore extraction.

Optical coherence tomography (OCT), as a non-destructive and high-resolution fingerprint acquisition technology, is robust against poor skin conditions and resistant to spoof attacks. It measures fingertip information on and beneath skin as 3D volume data, containing the surface fingerprint, internal fingerprint and sweat glands. Various methods have been proposed to extract internal fingerprints, which ignore the inter-slice dependence and often require manually selected parameters. In this article, a modified U-Net that combines residual learning, bidirectional convolutional long short-term memory and hybrid dilated convolution (denoted as BCL-U Net) for OCT volume data segmentation and two fingerprint reconstruction approaches are proposed. To the best of our knowledge, it is the first time that simultaneous and automatic extraction is performed for surface fingerprint, internal fingerprint and sweat gland. The proposed BCL-U Net utilizes the spatial dependence in OCT volume data and deals with segmentation of objects with diverse sizes to achieve accurate extraction. Comparisons have been performed to demonstrate the advantages of the proposed method. A thorough evaluation of the recognition abilities of internal and surface fingerprints is conducted using a dataset significantly larger than previous studies. Four databases containing internal and surface fingerprints are generated from 1572 OCT volume data by the proposed method. The internal fingerprint matching experiment has achieved a lowest equal error rate (EER) of 0.95%. Mixed internal and surface fingerprint matching experiment is also performed and achieves an EER of 3.67%, verifying the consistency of the internal and surface fingerprints. The matching experiments for fingers under poor skin conditions show a 2.47% EER of internal fingerprints that is much lower than that of surface fingerprints, which proves the advantage of internal fingerprints and indicates the potential of the internal fingerprints to supplement or replace the surface fingerprints for some specific applications.

This paper studies the stabilizability and instabilizability problems for delayed complex-valued memristive neural networks within finite-time intervals. First, more general assumptions for complex-valued activation functions are given. To check that whether the closed-loop system is stable within a finite-time interval, a novel nonlinear delayed controller with separable real-imaginary parts is designed. It includes two independent parameters different from the existing ones, which makes the controller more general but also leads to great difficulties. To overcome these difficulties, two new inequalities are proposed and proved. Then, through Lyapunov function approach, sufficient conditions are derived for the finite-time stabilizability of the closed-loop system and the settling time is estimated. Accordingly, some criteria for the finite-time instabilizability are also established by adjusting different parameters in the designed controller. Finally, several numerical simulations are given to show the effectiveness and advantages of the proposed results.