Search Results (453)

Domain Generation Algorithms (DGAs) pose a persistent threat by enabling malware to dynamically generate numerous command-and-control domains, evading traditional blocklists. While machine learning-based detectors have achieved high accuracy, they operate as statistical pattern matchers and lack the human-like anomaly perception that enables security experts to intuitively recognize unnatural domains. This paper introduces CogNormDGA, a cognitive-driven framework that models normal domain characteristics from a defender’s perspective while also anticipating how attackers might exploit cognitive blind spots. Inspired by dual-process theory, CogNormDGA combines intuitive, pattern-based screening (System 1) with analytical, rule-based evaluation of phonotactic, morphological, and semantic violations (System 2). The cognitive principles of System 1 and System 2 are computationally realized as two distinct pathways: an Attentional Salience Network and a Linguistic Constraint Evaluator, respectively. The framework produces interpretable outputs via attention saliency maps and cognitive violation reports. Extensive experiments on 400,000 domains spanning 33 DGA families demonstrate that CogNormDGA achieves competitive detection performance (F1-score 0.941) while establishing a cognitive-driven detection paradigm that produces human-aligned explanations—a property critical for practical security. It shows promising results on low-entropy and novel DGA families. Human subject studies confirm strong alignment between the model’s internal explanations and expert reasoning. Furthermore, CogNormDGA is particularly effective against low-entropy DGA families that exploit cognitive blind spots. By bridging cognitive science and cybersecurity, our work offers an interpretable and human-aligned approach to threat detection, with promising resilience that requires further validation. Full article

Noninvasive blood glucose monitoring is crucial for detecting early dysglycemia, yet continuous glucose monitors remain invasive and costly. Electrocardiogram (ECG) and its derived heart rate variability (HRV) measure may offer a noninvasive indicator of autonomic and cardiac responses associated with acute changes in glucose. In this study, 30 adults underwent a 75 g oral glucose tolerance test with concurrent ECG Holter and interstitial glucose monitoring. From these recordings, HRV and ECG features were extracted. A deep learning classifier with HRV and ECG was then trained to detect hyperglycemia (glucose ≥ 180 mg/dL). Cross-correlation analysis confirmed a significant association between HRV and glucose (Pearson r ~0.65, p < 0.05) when aligning each participant’s data according to individual response delays. The model achieved high classification performance under rigorous temporal validation (accuracy ~89%, area under the receiver operating characteristic curve ~0.89). Saliency analyses revealed that the classifier’s decisions focus on distinct ECG waveform transitions and key HRV features linked to glucose-induced autonomic changes. Overall, acute hyperglycemia elicited discernible changes in HRV and cardiac conduction, supporting the feasibility of this physiologically grounded approach for detecting the acute hyperglycemic phase under controlled conditions. This method holds promise for real-time implementation in wearable devices, enabling early diabetes risk screening. Full article

The growing complexity of unwanted messages, especially SMS spam, presents a serious challenge to the security of digital communication and user experience. While conventional spam detection models are useful on clean datasets, they are vulnerable to targeted attacks that aim to evade detection. This study is motivated by the urgent need to evaluate the resilience of machine learning models against evolving threats in real-world applications. We specifically investigate the robustness and interpretability of a Multinomial Naive Bayes (MNB) model, representative of traditional machine learning, and a Tiny Text convolutional neural network (Tiny Text CNN), representative of deep learning models, for SMS spam detection. Using the UCI dataset under simulated adversarial text attacks, both models were tested against filler-word insertion and character-level perturbation attacks. Results show that while the Tiny Text CNN maintained higher overall robustness (accuracy: 0.9821 clean vs. 0.9758 under character attacks), both models experienced notable degradation in recall, with MNB being more susceptible to filler-word attacks. Interpretability analyses using LIME and gradient-based saliency maps indicated that adversarial perturbations alter feature importance, diminishing the influence of spam-indicative tokens. The findings underscore the trade-offs between model complexity and adversarial resilience, offering insights for developing more secure and interpretable spam detection systems. Full article

Electroluminescence (EL) imaging is widely used for photovoltaic (PV) module inspection, yet EL defect detection remains challenging due to the need for high-resolution inputs, low-contrast defects, and strong structured background patterns. To address these issues, we propose MSW-Mamba-Det, an end-to-end defect detection framework built on RT-DETR, comprising three components. (1) MSW-Mamba, a multi-scale windowed state-space module, adopts a Local/Stripe/Grid architecture to jointly model fine details and long-range dependencies; the Stripe branch strengthens directional continuity for elongated defects, while the Grid branch introduces coarse global context to improve cross-region consistency. Saliency- and gradient-guided gating is further used to suppress background-induced false responses. (2) DetailAware compensates for detail attenuation by restoring high-frequency textures and edges through multi-scale local enhancement, and applies pixel-wise adaptive gating to integrate global semantics and mitigate smoothing effects in deep representations. (3) PAFB (Pyramid Attention Fusion Block) aligns adjacent-scale features and improves multi-scale fusion, enhancing localization stability across defect sizes. Experiments on two public EL datasets show that MSW-Mamba-Det achieves $A{P}_{50:95}$ of 60.4% on PV-Multi-Defect-main and 68.0% on PVEL-AD, improving over RT-DETR by 2.5 points (from 57.9% to 60.4%) and 2.2 points (from 65.8% to 68.0%), respectively. MSW-Mamba-Det also outperforms 12 representative baselines, including CNN-, Transformer-, and recent YOLO-based models, in $A{P}_{50:95}$ on both datasets, with particularly strong performance on medium and large defects. These results demonstrate the effectiveness of the proposed modules for robust PV EL defect inspection under low-contrast and structured-background conditions. Full article

As urbanization transitions from incremental expansion to the optimized utilization of existing construction land, the precise identification of land-use status and changes has become a core requirement for enhancing refined land resource management. However, in urban built environments characterized by dense object distributions and complex geometric contours, existing change detection methods often struggle to capture subtle boundaries, leading to edge blurring and loss of detail. To address these challenges, this study proposes an Edge-aware Change Detection Network for urban construction land change identification. The model features a shared Siamese encoding network based on MiT-B1, leveraging its hierarchical multi-scale attention mechanism to balance local detail extraction with long-range semantic dependency capture, thereby overcoming the limitations of monolithic feature extraction. Furthermore, a multi-level feature concatenation and fusion strategy is designed to align and interact with bi-temporal features along the channel dimension, significantly enhancing the saliency and discriminative representation of change areas. Experimental results on the Yongzhou building change detection dataset demonstrate that the proposed model outperforms state-of-the-art methods in both visual recognition and quantitative metrics. It effectively resolves the difficulty of boundary definition in complex urban scenarios, providing localized high-precision technical support for the assessment and dynamic monitoring of construction land within the study area. Full article

Hybrid multi-backbone architectures and the utilization of edge cues for auxiliary training have become two major research trends in salient object detection (SOD). It is widely acknowledged that CNNs can effectively model local spatial structures, while Transformers can capture long-range global dependencies. However, the representation discrepancy between CNN and Transformer features, together with boundary-detail degradation during multi-scale fusion, remains a major challenge. In addition, how to effectively leverage edge cues as reliable structural guidance without introducing texture-induced false boundaries or boundary leakages remains an open issue. In this paper, we present SECA-Net, a unified framework that establishes a profound synergy between CNN and Transformer representations. It explicitly bridges their inherent discrepancies through level-dependent interaction strategies, while resolving structural degradation via a sequential “purify-and-guide” mechanism. This approach enables the network to extract and utilize edge cues effectively, thereby alleviating boundary degradation and texture-induced false contours. Specifically, we design a dual-encoder structure to extract features. A level-wise feature interaction (LFI) module is introduced to perform discrepancy-aware fusion across feature levels, stabilizing CNN–Transformer aggregation. Meanwhile, the features extracted from the CNN branch are projected into a semantic-aware edge refinement (SAER) module to produce clean multi-scale edge priors under high-level semantic guidance, suppressing texture-induced spurious edges. Finally, we design an edge-guided cross-attention feature aggregation (ECFA) module, which progressively injects refined edge priors as structural constraints into multi-scale saliency decoding via cascaded cross-attention, enabling effective structural refinement. Overall, LFI reduces cross-branch discrepancy, SAER purifies boundary priors, and ECFA integrates semantics and structure in a progressive decoding manner, forming a unified SECA-Net framework. Extensive experimental results on five benchmark SOD datasets show that SECA-Net outperforms 19 state-of-the-art methods, demonstrating its effectiveness. Specifically, our proposed method ranks first in $F_{\beta }$ and $BDE$ across all datasets, notably improving $F_{\beta }$ by $1.54\%$ on the challenging DUTS-TE dataset. Full article

Low-orbit thermal infrared bidirectional whisk-broom imaging offers wide-swath coverage and high spatial resolution for monitoring moving targets such as aircraft, but large scan angles and terrain undulation cause non-rigid geometric distortion and radiometric inconsistency between forward and backward scans. These effects generate strong clutter in difference images and degrade small and weak target detection. To address this problem, we propose an infrared small target detection method that fuses accurate registration and weighted difference. First, we propose a hybrid multi-scale registration algorithm that achieves coarse affine registration through sparse feature–point matching and then iteratively corrects nonlinear deformations by integrating a global grayscale-driven force with a local sparse-feature-guided force, yielding a registration error of 0.3281 pixels. On this basis, a multi-scale weighted convolutional morphological difference algorithm is proposed. A novel dual-structure hollow top-hat transform is constructed to accurately estimate the background, and a multi-directional convolution mechanism is introduced to effectively suppress anisotropic edge clutter and enhance target saliency. Experiments on SDGSAT-1 thermal infrared bidirectional whisk-broom data show an SCRG of 18.27, and a detection rate of 91.2% when the false alarm rate is below 0.15%. The method outperforms representative competing algorithms and provides a useful reference for space-based aerial moving target detection. Full article

Automated pixel-level detection of steel surface defects is a critical challenge in manufacturing quality control, complicated by the variation in defect size and shape, low contrast with background textures, and the diversity of defect patterns. This paper proposes ID-MSNet, an enhanced version of the UNet3+ architecture, designed specifically for the segmentation of three common steel surface defect types: inclusions, patches, and scratches. The proposed architecture introduces three targeted modifications: (1) a multi-scale feature learning module (MSFLM) in the encoder that uses dilated convolutions at multiple rates to capture contextual features across different scales, combined with DropBlock regularization and batch normalization to improve generalization; (2) an improved down-sampling (IDS) module that replaces standard max-pooling with learnable strided convolutions fused via 1 × 1 convolution, preserving richer feature representations; and (3) a convolutional block attention module (CBAM) integrated into the skip connections to selectively focus the model on spatially and channel-wise relevant defect regions. Experiments on the publicly available SD-saliency-900 dataset demonstrate that ID-MSNet achieved an 86.19% mIoU, outperforming all compared state-of-the-art segmentation models while using only 6.7 million parameters—approximately 75% fewer than the original UNet3+. These results establish ID-MSNet as a strong and efficient baseline for steel surface defect segmentation, with potential applicability to automated quality inspection in broader manufacturing contexts. Full article

Non-destructive and automated detection of bacterial contamination is a critical prerequisite for ensuring high efficiency production and quality control in plant tissue culture. In this study, we developed a multispectral image acquisition system for Alocasia explants and proposed a novel image fusion model, termed Intensity-Texture enhanced Swin Fusion (ITSF). The ITSF framework employs convolutional neural networks to extract texture and intensity features from visible and near-infrared channels. Subsequently, a Swin Transformer-based module is integrated to model long-range spatial dependencies, ensuring cross-domain integration between the texture and intensity features. We formulated a composite loss function to guide the fusion process toward optimal results. This objective function integrates texture loss, entropy weighted structural similarity index (SSIM) and intensity aware dynamic gain guided loss. Experimental results demonstrate that the proposed method significantly enhances the visual saliency of bacteria and achieves superior quantitative performance across a comprehensive range of objective image fusion metrics. The detection performance reached a mean Average Precision (mAP50) of 0.949 with the fused images, satisfying industrial requirements for high-precision inspection, which provides a critical technical solution for the industrialization of automated micropropagation. Full article

Object detection in unmanned aerial vehicle (UAV) imagery remains a crucial yet challenging task due to complex backgrounds, large scale variations, and the prevalence of small objects. Visible-spectrum images lack robustness under all-weather and all-illumination conditions; by contrast, multispectral sensing provides complementary cues (e.g., thermal signatures) that improve detection robustness. However, existing multispectral solutions often incur high computational costs and are therefore difficult to deploy on resource-constrained UAV platforms. To address these issues, SG-YOLO is proposed, a lightweight and efficient multispectral object detection framework that aims to balance accuracy and efficiency. First, a Spectral Gated Downsampling Stem (SGDS) is designed, in which grouped convolutions and a gating mechanism are employed at the early stage of the network to extract band-specific features, thereby maximizing spectral complementarity while minimizing redundancy. Second, a Spectral–Spatial Iterative Attention Fusion (SSIAF) module is introduced, in which spectral-wise (channel) attention and spatial-wise attention are iteratively coupled and cascaded in a multi-scale manner to jointly model cross-band dependencies and spatial saliency, thereby aggregating high-level semantic information while suppressing redundant spectral responses. Finally, a Spatial–Channel Synergistic Fusion (SCSF) module is designed to enhance multi-scale and cross-channel feature integration in the neck. Experiments on the MODA dataset show that SG-YOLOs achieves 72.4% mAP₅₀, outperforming the baseline by 3.2%. Moreover, compared with a range of mainstream one-stage detectors and multispectral detection methods, SG-YOLO delivers the best overall performance, providing an effective solution for UAV object detection while maintaining a favorable trade-off between model size and detection accuracy. Full article

Salient object ranking aims to assign a relative importance order to multiple objects in an image, aligning with human visual attention. However, existing methods struggle with ranking ambiguity in complex scenes, particularly when objects are numerous, occluded, or semantically similar, leading to decreased accuracy for low-saliency objects. To address this, we propose PairwiseSOR-MLMs, a novel framework leveraging multimodal large models and pairwise comparison to achieve salient object ranking. The approach decomposes global ranking into a series of pairwise comparison tasks. It first employs object detection and instance segmentation to identify objects, uses image inpainting to reconstruct scenes by removing occlusions, and then prompts MLMs to perform pairwise comparisons based on visual saliency cues. Finally, another MLM inference aggregates these comparisons into a consistent global ranking. Experiments on ASSR and IRSR benchmarks show our method achieves state-of-the-art or competitive performance across metrics, demonstrating robustness in handling occlusion and semantic similarity. Its pairwise comparison paradigm can extend to other relative assessment tasks. Full article

In complex driving environments, drivers must continuously detect and respond to critical visual information such as traffic signs and pedestrians. However, important targets may sometimes be overlooked due to high cognitive load during driving. Therefore, visual attention prompt methods have been proposed to guide drivers’ gaze toward relevant targets. A visual attention prompt method is a visual cue presented in a key area in a user’s field of view to draw his/her visual attention. This study evaluates the short-term performance of five visual attention prompt methods (Point, Arrow, Blur, Dusk, and ModAF) in a driving simulator and compares their performance between novice and proficient drivers. Eye-tracking data and multiple analyses are used to examine whether the influence of these methods could be maintained after they are disabled and to clarify drivers’ response patterns across methods in consideration with their driving proficiency. The results indicate that visual attention prompt methods could induce a short-term transfer effect, as drivers still tend to fixate on target traffic signs earlier after the methods are disabled, and the elapsed-time analysis estimates that this effect lasts about 84.35 s. Overall, the Point, Arrow, and Dusk methods show relatively stronger performance with significant reductions in the elapsed time to fixate on the traffic sign. The clustering analysis further shows that drivers’ response patterns are not uniform, with two clusters for novice drivers and three clusters for proficient drivers. The results suggest that most novice drivers tend to benefit from explicit non-directional visual cues that enhance target salience, such as the Point method, whereas proficient drivers are more likely to benefit from explicit directional visual cues that provide clear directional guidance, such as the Arrow method. These findings suggest that visual attention prompt methods may be useful for developing driver training strategies tailored to different levels of driving proficiency, helping drivers maintain more effective visual attention allocation during driving and potentially contributing to improved driving safety. Full article

This paper proposes a novel salient object detection method for operational hole localization in metallurgical furnaces, addressing challenging industrial conditions including extreme illumination variations and strong electromagnetic interference to enable two-level measurement in aluminum electrolysis cells and impact position recognition of the front-of-furnace operation robot. It employs a multi-feature fusion framework combining foreground and background saliency maps with center prior maps. Foreground saliency maps are generated through spatial compactness and local contrast computations, enhancing discriminative features while suppressing shared foreground–background characteristics. Background saliency maps are constructed via sparse reconstruction to exploit redundant features. Then method integrates edge extraction and density clustering to generate center prior maps that emphasize foreground target centroids and mitigate background noise. Comprehensive evaluations on both a specialized operational hole dataset and six public datasets demonstrate superior performance compared to other methods. On the specialized dataset, it achieves a precision of 0.8954, a maximum F-measure of 0.8994, and an S-measure of 0.8662. While maintaining operational robustness, the method offers a practical solution for furnace monitoring and robotic operation guidance in metallurgical processes. Full article

Monitoring bird activity at public roosts is essential for understanding stopover behavior during migration, assessing ecological change, and supporting conservation strategies. Existing weather radar-based roost detection methods primarily rely on high-reflectivity ring-shaped echoes, which can lead to missed detections when roost-related echo structures are weak or indistinct. To address this limitation, this study proposes a saliency-constrained multi-peak spectral approach for monitoring and identifying public bird roosts using weather radar. At the radar resolution-cell scale, a saliency-constrained multi-peak Doppler spectrum decomposition and classification method is developed. Mixed Doppler power spectra are decomposed into multiple independent subpeaks through spectral peak saliency detection, and spectral polarimetric features are utilized to identify bird-related subpeaks, yielding a set of bird motion subgroups within each resolution cell. On this basis, a Bird Roost Index (BRI) is introduced, which couples the number of bird subgroups with their radial velocity dispersion to quantitatively characterize the complexity of bird motion modes in local airspace. Finally, the proposed method is applied to operational S-band weather radar observations collected over the Dongting Lake Basin roosts region during the spring season. The results demonstrate that the BRI exhibits strong spatial consistency and coherent temporal evolution, enabling robust characterization of communal roosting activity. This confirms the robustness of the proposed approach and highlights its potential for operational monitoring of migratory bird communal roosts using weather radar spectral data. Full article

Change detection in remote sensing imagery is crucial for monitoring temporal variations in surface characteristics; nevertheless, it presents significant challenges owing to indistinct boundaries, limited semantic differentiation, and inadequate incorporation of multi-scale contextual information. To solve these problems, we propose EIMDGNet (Edge-Induced and Multi-Dimensional Grouped Difference Network), a novel architecture that enhances boundary representation and cross-scale feature interaction for accurate and robust change detection. EIMDGNet adopts a dual-branch ResNet18 backbone to extract multi-scale features from bi-temporal images, capturing both fine spatial detail and high-level semantic context. To improve boundary awareness and reduce pseudo-change interference, we introduce the Edge-Induced Differential Multi-Dimensional Group Enhancement Module (EID-MDGEM). This module enriches fine-grained spatial features through grouped pooling across spatial and channel dimensions, enabling precise localization of change contours. Within EID-MDGEM, the Edge Feature Enhancement Module (EFEM) integrates a parameter-free attention mechanism to generate edge-saliency maps, highlighting true change regions while suppressing background noise and irrelevant variations. To further enhance semantic consistency across feature scales, we design the Multi-Scale Hierarchical Progressive Fusion Module (MSHPM). This component employs a bottom-up progressive strategy to hierarchically integrate low-level spatial details with high-level semantic abstractions, thus increasing the continuity and completeness of detected change regions. By tightly coupling edge-aware enhancement with multi-scale hierarchical fusion, EIMDGNet effectively addresses major obstacles in change detection, including boundary ambiguity, inconsistent scale information, and feature misalignment. We evaluated EIMDGNet on five remote sensing change detection datasets: LEVIR-CD, DSIFN-CD, S2Looking, CLCD-CD and GVLM-CD. Our method consistently outperformed state-of-the-art approaches, achieving 91.49% F1 and 82.93% IoU on LEVIR-CD, 77.32% F1 and 69.39% IoU on DSIFN-CD, the highest 49.19% IoU and 99.20% OA on S2Looking, 81.65% F1 and 72.91% IoU on CLCD-CD, and 85.49% F1 and 76.08% IoU on GVLM-CD. These results demonstrate the superior accuracy and robustness of EIMDGNet across diverse change detection scenarios. Full article