Unrestrained palmprint recognition refers to a comprehensive identity authentication technology, that performs personal authentication based on the palmprint images captured in uncontrolled environments, i.e., smartphone cameras, surveillance footage, or near-infrared scenarios. However, unrestrained palmprint recognition faces significant challenges due to the variability in image quality, lighting conditions, and hand poses present in such settings. We observed that many existing methods utilize the subspace structure as a prior, where the block diagonal property of the data has been proved. In this paper, we consider a unified learning model to guarantee the consensus block diagonal property for all views, named high-confident block diagonal analysis for multi-view palmprint recognition (HCBDA_MPR). Particularly, this paper proposed a multi-view block diagonal regularizer to guide that all views learn a consensus block diagonal structure. In such a manner, the main discriminant features from each view can be preserved while the learning of the strict block diagonal structure across all views. Experimental results on a number of real-world unrestrained palmprint databases proved the superiority of the proposed method, where the highest recognition accuracies were obtained in comparison with the other state-of-the-art related methods.

Antibiotic Susceptibility Testing (AST) based on microorganism culturing is the gold-standard technique to determine whether a pathogen is susceptible or resistant to available antibiotics. While broth microdilution offers a potential high-throughput method for AST, reading and interpreting microtitre plates can be challenging, even for experienced clinical microbiologists. Machine learning models trained on images of microtitre plates obtained during AST could potentially accelerate and even automate this process. However, these image sets are highly imbalanced since each drug on the plate may exhibit different growth distributions due to varying resistance prevalence and mechanisms, which negatively impacts the performance of trained models. To address this problem, we propose a Generative Adversarial Network (GAN)-based framework, named CulplateGAN, to augment the dataset with images of plates displaying specific growth levels for particular drugs. The adversarial loss and weight-controlled content loss are introduced to achieve image transformation and content preservation. Moreover, a Multi-Culplate-GAN architecture is designed to generate multilevel outputs with one single input, which are optimized by the proposed domain-based adversarial loss and domain classification loss. We evaluate Culplate-GAN and Multi-Culplate-GAN by training a classifier on an AST Mycobacterium Tuberculosis dataset. Comprehensive results indicate that our method outperforms existing representative augmentation methods and can be generalized to plates containing other bacterial cultures.

In this paper, we address the challenge of procedure planning in instructional videos, aiming to generate coherent and task-aligned action sequences from start and end visual observations. Previous work has mainly relied on text-level supervision to bridge the gap between observed states and unobserved actions, but it struggles with capturing intricate temporal relationships among actions. Building on these efforts, we propose the Masked Temporal Interpolation Diffusion (MTID) model that introduces a latent space temporal interpolation module within the diffusion model. This module leverages a learnable interpolation matrix to generate intermediate latent features, thereby augmenting visual supervision with richer mid-state details. By integrating this enriched supervision into the model, we enable end-to-end training tailored to task-specific requirements, significantly enhancing the model's capacity to predict temporally coherent action sequences. Additionally, we introduce an action-aware mask projection mechanism to restrict the action generation space, combined with a task-adaptive masked proximity loss to prioritize more accurate reasoning results close to the given start and end states over those in intermediate steps. Simultaneously, it filters out task-irrelevant action predictions, leading to contextually aware action sequences. Experimental results across three widely used benchmark datasets demonstrate that our MTID achieves promising action planning performance on most metrics.

3D hand mesh reconstruction from 2D images is crucial for various computer visual tasks such as virtual reality and human-computer interaction, while it remains a challenging problem due to changed hand poses and diverse self-/cross-hand occlusions. In this paper, we propose a graph-based reasoning network for 3D hand mesh reconstruction from a single 2D RGB image by recovering the fine-grained 3D hand mesh keypoints in a coarse-to-fine manner. First, we extract the hand-joint-specific features to capture the overall hand structure via a CNN backbone with a linear projection sampling operation. Based on the hand-joint locations, we further progressively learn more hand mesh keypoints to construct the coarse hand shape via hierarchical attention-embedded graph learning layers. Finally, we leverage the 2D shallow semantic features to refine the coarse hand mesh keypoints into fine-grained 3D hand mesh vertices coordinates via cascaded graph learning layer with linear mapping. Experimental results on the widely used hand databases show that our method achieves outstanding performance in both single-hand and two-interactive-hand 3D mesh reconstruction.

Text Image Tampering Detection and Localization is vital for verifying digital text images authenticity. The neglect of text image-specific tampering features in existing methods constrains their robustness and generalization across diverse scenarios. To address this, we propose TRAMFuse, a framework combining the Directional Residual Attention Mechanism (DRAM), tailored for text image tampering, and the Feature Neighborhood Constraint Module (FNCM), a general-purpose tampering feature extractor. DRAM captures geometric and directional features unique to text images, while FNCM enhances robustness by identifying inconsistencies. A cross-modal fusion for RGB-X (CMX) module is employed to integrate multi-modal features. To further advance research in text image tampering detection, we have constructed a large-scale mixed text image tampering dataset, named DocTamperMix. Experiments demonstrate that TRAMFuse outperforms state-of-the-art methods in both tampering detection and localization, showcasing its effectiveness across diverse scenarios.

Image denoising is essential in low-level vision applications such as photography and automated driving. Existing methods struggle with distinguishing complex noise patterns in real-world scenes and consume significant computational resources due to reliance on Transformer-based models. In this work, the Context-guided Receptance Weighted Key-Value (CRWKV) model is proposed, combining enhanced multi-view feature integration with efficient sequence modeling. The Context-guided Token Shift (CTS) mechanism is introduced to effectively capture local spatial dependencies and enhance the model's ability to model real-world noise distributions. Also, the Frequency Mix (FMix) module extracting frequency-domain features is designed to isolate noise in high-frequency spectra, and is integrated with spatial representations through a multi-view learning process. To improve computational efficiency, the Bidirectional WKV (BiWKV) mechanism is adopted, enabling full pixel-sequence interaction with linear complexity while overcoming the causal selection constraints. The model is validated on multiple real-world image denoising datasets, outperforming the state-of-the-art methods quantitatively and reducing inference time up to 40%. Qualitative results further demonstrate the ability of our model to restore fine details in various scenes. The code is publicly available at https://github.com/Seeker98/CRWKV.

Current existing clustering methods for handling incomplete multi-view data primarily concentrate on learning a common representation or graph from the available views, while overlooking the latent information contained in the missing views and the imbalance of information among different views. Furthermore, instances with weak discriminative features usually degrading the precision of consistent representation or graph across all views. To address these problems, in this paper, we propose a simple but efficient method, called high-confident local structure guided consensus graph learning for incomplete multi-view clustering (HLSCG_IMC). Specifically, this method can adaptively learn a strict block diagonal structure from the available samples using a block diagonal representation regularizer. Different from the existing methods using a simple pairwise affinity graph for structure construction, we consider the influence of instances located at the edge of two clusters on the construction of graph for each view. By harnessing the proposed high-confident strict block diagonal structures, the approach seeks to directly guide the learning of the robust consensus graph. A number of experiments have been conducted to verify the efficacy of our approach.

Cooperative palmprint recognition, pivotal for civilian and commercial uses, stands as the most essential and broadly demanded branch in biometrics. These applications, often tied to financial transactions, require high accuracy in recognition. Currently, research in palmprint recognition primarily aims to enhance accuracy, with relatively few studies addressing the automatic and flexible palm region of interest (ROI) extraction (PROIE) suitable for complex scenes. Particularly, the intricate conditions of open environment, alongside the constraint of human finger skeletal extension limiting the visibility of Finger Valley Points (FVPs), render conventional FVPs-based PROIE methods ineffective. In response to this challenge, we propose an FVPs-Free Adaptive ROI Detection (FFARD) approach, which utilizes cross-dataset hand shape semantic transfer (CHSST) combined with the constrained palm inscribed circle search, delivering exceptional hand segmentation and precise PROIE. Furthermore, a Recurrent Layer Aggregation-based Neural Network (RLANN) is proposed to learn discriminative feature representation for high recognition accuracy in both open-set and closed-set modes. The Angular Center Proximity Loss (ACPLoss) is designed to enhance intra-class compactness and inter-class discrepancy between learned palmprint features. Overall, the combined FFARD and RLANN methods are proposed to address the challenges of palmprint recognition in open environment, collectively referred to as RDRLA. Experimental results on four palmprint benchmarks HIT-NIST-V1, IITD, MPD and BJTU_PalmV2 show the superiority of the proposed method RDRLA over the state-of-the-art (SOTA) competitors. The code of the proposed method is available athttps://github.com/godfatherwang2/RDRLA.

Due to its distinctive texture and intricate details, palmprint has emerged as a critical modality in biometric identity recognition. The absence of large-scale public palmprint datasets has substantially impeded the advancement of palmprint research, resulting in inadequate accuracy in commercial palmprint recognition systems. However, existing generative methods exhibit insufficient generalization, as the images they generate differ in specific ways from the conditional images. This paper proposes a method for generating palmprint images using a controllable diffusion model (PalmDiff), which addresses the issue of insufficient datasets by generating palmprint data, improving the accuracy of palmprint recognition. We introduce a diffusion process that effectively tackles the problems of excessive noise and loss of texture details commonly encountered in diffusion models. A linear attention mechanism is employed to enhance the backbone's expressive capacity and reduce the computational complexity. To this end, we proposed an ID loss function to enable the diffusion model to generate palmprint images under the same identical space consistently. PalmDiff is compared with other generation methods in terms of both image quality and the enhancement of palmprint recognition performance. Experiments show that PalmDiff performs well in image generation, with an FID score of 13.311 on MPD and 18.434 on Tongji. Besides, PalmDiff has significantly improved various backbones for palmprint recognition compared to other generation methods.

This letter introduces an innovative approach for image restoration. Our model is primarily motivated by the integration of curvature constraints into a self-supervised convolutional neural network (CNN), which combine the hand-crafted prior with CNN structure without training on external dataset. Firstly, it is argued that detail loss may be induced by sparsity-based models, which eliminate bases with low coefficients. Therefore, a geometric approach is proposed to preserve details, with the incorporation of Gaussian curvature as a regularization term for both noise suppression and detail preservation. Secondly, the deep image prior is used to establish the optimization backbone. This framework harnesses the translation in-variance and smoothness properties of CNN as a tightly supervised recovery mechanism for effective noise suppression. By combining a simple data-fitting term with curvature-based regularization terms, we develop a self-supervised model for image restoration that maintains fine details. Experimental results demonstrate the effectiveness of our proposed algorithm in addressing various image restoration problems.

Abstract One of the important factors in deep feature learning is their loss function design which highly influences the network performance. In this paper, we proposed a classroom (CR) learning approach along with arcface loss for contactless palmprint recognition to obtain high-level discriminative features without any extra load made to the network architecture. CR loss allows the network to learn the best possible feature representations for palmprint images. To validate our concept, we performed extensive experimental evaluations on various popular benchmark palmprint databases where our methods outperform the state-of-the-art methods. We also introduced a challenging contactless palmprint database called Harbin Institute of Technology-Network & Information Security Research Center contactless palmprint database version 1.0 (HIT-NIST-V1), as a new contribution to this domain. The result proves that the proposed CR loss consistently outperforms the SOTA methods for all the considered databases and especially for HIT-NIST-V1.

Abstract On-line gambling is the key illegal behaviour of public security department in most countries due to the potential threat to cyberspace security and social stability. Hence, the research on gambling domain names (GDN) classification is quite important and in great demand for academia and industry. Till now, there is very little research work on this topic. Most of the GDN training datasets in previous work were chosen from GDN blacklists provided by publicly available data sources, and the authors did not verify the authenticity and accuracy of these datasets, and the classification results are not particularly satisfactory. In this paper, certificated and textual analysis-based classification method CT-GDNC is proposed to get GDN training data set with an accuracy of 0.9776 and significantly improve the classification results of GDN. The exhaustive comparative experiments on 10K GDN obtained via Bert fine-tuning model and 10K benign data collected from Alex Top 1 million list show that the proposed method achieves new baseline result for GDN classification with classification accuracy 0.9936, precision 0.9936, F1 0.9936 and recall 0.9939.

Abstract Millions of new domain names are registered every day, but a large proportion of them are malicious and usually discovered and blacklisted after the crime has been committed. In order to improve the security of domain name registration, this paper proposes a lightweight detection method based on the AdaBoost to identify malicious domain names, which focuses on proactively detecting malicious domain names by exploring the abnormal WHOIS records. The domain name registries and registrars can adopt the proposed method as the first layer of defense to identify malicious domains on the domain registration stage. Extensive experiments on a large-scale database demonstrate that the proposed approach achieves satisfactory results on various malicious domain names.

Abstract As the backbone of the domain name system (DNS), DNS resolvers are essential to the Internet. Nowadays, the measurement of DNS resolvers, especially open DNS resolvers, has become a research focus. Previous research works show that DNS responses returned from some open DNS resolvers are not expected for clients and the Internet. We call these DNS responses ‘unexpected DNS responses’. Research on unexpected DNS responses is beneficial to the research, usage and management of open DNS resolvers. This paper explores unexpected DNS responses returned from open DNS resolvers in terms of identification and classification to better understand the behaviours of open DNS resolvers. First, an identification method is proposed to identify all kinds of DNS responses from each section of DNS messages. Second, a classification method is proposed to classify unexpected DNS responses by their influences on clients and the Internet. Furthermore, an efficient identification and classification method is proposed to simplify the above process. Among about 9 million responding open DNS resolvers in the experiments on the IPv4 address space, about 40% return unexpected DNS responses. Experimental results show that the proposed methods can identify and classify all kinds of DNS responses returned from open DNS resolvers.

Abstract Palmprint is an important key player in biometric family and also informs some extra basic personality details of an individual. In this paper, we utilize these extra information and designed an automated mobile vision (MV) system to extract principal lines from human palm and analyze them for behavioral significances. Hence, the main concern of this paper is to come up with a simple yet powerful low-level MV solution to extract the complex challenging features from palmprint. In the proposed system, the computational tasks are offloaded to a dedicated palmistry server and efficiently minimizes the energy consumption of mobile device after performing some preliminary computational low-level tasks. The implementation is divided into four major phases: (i) hand-image acquisition and pre-processing, (ii) region-of-interest extraction from the palm images, (iii) post-processing to extract principal lines and (iv) features computation for behavior analysis. The basic palmistry uses line lengths, angles, curves and branches to identify a person’s behavior. The exhaustive experiments show that the proposed system achieves an average accuracy of 96%, 92% and 84% for heart, life and head line detection and personality prediction, respectively. Finally, mapping the extracted results with the original palmprint is augmented back to the use for better visualization.

The Domain name system (DNS) is crucial to Internet services. Previous studies have pointed out that the name dependency in domain name resolution poses a risk to the security of DNS. In this paper, we present measurement results from a dataset containing resolution paths of domain names collected from a large-scale survey. This dataset is used to research the effect of the name dependency on the DNS, reaffirm findings in published work, and notice some significant differences. When name resolution spans multiple domains, it will lead to name dependency and make the resolution process more complex. Furthermore, we assess the risk of domain name resolution: a name resolution fault analysis model and the calculation of the failure probability of name resolution is proposed. The model can identify the key server sets that lead to the resolution failure of a domain name, and quantify the failure probability of its resolution. This research provides a breakthrough point for guiding the configuration, management, deployment, and upgrading of the DNS.

Online palmprint recognition and latent palmprint identification are two branches of palmprint studies. The former uses middle-resolution images collected by a digital camera in a well-controlled or contact-based environment with user cooperation for commercial applications and the latter uses high-resolution latent palmprints collected in crime scenes for forensic investigation. However, these two branches do not cover some palmprint images which have the potential for forensic investigation. Due to the prevalence of smartphone and consumer camera, more evidence is in the form of digital images taken in uncontrolled and uncooperative environment, e.g., child pornographic images and terrorist images, where the criminals commonly hide or cover their face. However, their palms can be observable. To study palmprint identification on images collected in uncontrolled and uncooperative environment, a new palmprint database is established and an end-to-end deep learning algorithm is proposed. The new database named NTU Palmprints from the Internet (NTU-PI-v1) contains 7881 images from 2035 palms collected from the Internet. The proposed algorithm consists of an alignment network and a feature extraction network and is end-to-end trainable. The proposed algorithm is compared with the state-of-the-art online palmprint recognition methods and evaluated on three public contactless palmprint databases, IITD, CASIA, and PolyU and two new databases, NTU-PI-v1 and NTU contactless palmprint database. The experimental results showed that the proposed algorithm outperforms the existing palmprint recognition methods.