Pattern Recognition Letters最新文献_第2页

AMDC: Attenuation map-guided dual-color space for underwater image color correction 用于水下图像色彩校正的衰减地图引导双色空间

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-13 DOI: 10.1016/j.patrec.2026.01.005

Shilong Sun , Baiqiang Yu , Ling Zhou , Junpeng Xu , Wenyi Zhao , Weidong Zhang

Underwater images frequently exhibit color distortions due to wavelength-dependent light attenuation and absorption, further complicated by irregular lighting conditions underwater. Traditional color correction methods primarily target global light attenuation but are less effective in handling local color shifts caused by discontinuous depth variations and artificial illumination. To address this issue, we propose a dual-space adaptive color correction method guided by an attenuation map, referred to as AMDC. Specifically, we first utilize global attenuation compensation by leveraging the maximum reference channel of the image. Building on the globally compensated result, we then introduce a dual-space collaborative correction strategy. In RGB space, we perform local adaptive compensation using a weighted sliding window. In CIELab space, we restore color saturation through a zero-symmetric adaptive offset correction approach. To retain the most visually optimal color features, we selectively fuse the a and b channels from the two correction results, producing a locally corrected image. Finally, we utilize the maximum attenuation map of the raw image to guide the fusion of the locally corrected image with the raw, generating the final color-corrected output. Extensive qualitative and quantitative experiments demonstrate the effectiveness and robustness of our method for underwater image color correction.

由于波长相关的光衰减和吸收，水下图像经常表现出颜色失真，水下不规则的照明条件进一步复杂化。传统的色彩校正方法主要针对全局光衰减，但对于处理因深度变化不连续和人工照明引起的局部色彩偏移效果较差。为了解决这个问题，我们提出了一种由衰减图引导的双空间自适应色彩校正方法，称为AMDC。具体来说，我们首先利用图像的最大参考通道来利用全局衰减补偿。在全局补偿结果的基础上，提出了一种双空间协同校正策略。在RGB空间中，我们使用加权滑动窗口进行局部自适应补偿。在CIELab空间中，我们通过零对称自适应偏移校正方法恢复色彩饱和度。为了保留视觉上最优的颜色特征，我们有选择地融合两个校正结果中的a和b通道，产生局部校正图像。最后，我们利用原始图像的最大衰减图来指导局部校正图像与原始图像的融合，生成最终的颜色校正输出。大量的定性和定量实验证明了我们的方法对水下图像颜色校正的有效性和鲁棒性。

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引用次数: 0

Entropy calibrated prototype embedding for transductive few-shot learning 基于熵校正的换能化短时学习原型嵌入

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-13 DOI: 10.1016/j.patrec.2026.01.015

Mengfei Guo , Jiahui Wang , Qin Xu , Bo Jiang , Bin Luo

Transductive Few-shot learning aims to generalize to new classes from limited labeled support and all unlabeled query samples. Widely adopted paradigms including prototypical networks and graph-based label propagation. The former classifying queries based on distances to class prototypes, while the latter propagates the labels based on support samples. However, Existing methods typically treat all samples with equal importance, neglect inherent reliabilities, and underutilize prototypes merely as static anchors. This paper proposes Entropy Calibrated Prototype Embedding (ECPE), a novel framework that not only integrates prototypical networks and label propagation methods but also addresses their respective limitations through an iterative refinement strategy. Firstly, we propose the Entropy Calibration (EC), which dynamically assesses sample reliability using prediction entropy to weigh their influence in label propagation. Secondly, Entropy-aware Prototype Embedding (EPE) we proposed treats prototypes as evolving synthetic nodes, iteratively updating them based on calibrated predictions and embedding high-certainty prototypes into the graph.With the iteration of label calibration, entropy-aware prototype embedding, and label propagation, the proposed ECPE enhances classification accuracy and robustness. Extensive experiments demonstrate that ECPE surpasses state-of-the-art performance on three standard Transductive FSL benchmarks. Our source code is published at: https://github.com/gmf-ahu/ECPE.

换向的少次学习旨在从有限的标记支持和所有未标记的查询样本中泛化到新的类。广泛采用的范例包括原型网络和基于图的标签传播。前者基于到类原型的距离对查询进行分类，而后者基于支持样本传播标签。然而，现有的方法通常对所有样本都同等重要，忽视了固有的可靠性，并且将原型仅仅作为静态锚点加以充分利用。本文提出了熵校正原型嵌入（ECPE）框架，该框架不仅集成了原型网络和标签传播方法，而且通过迭代改进策略解决了它们各自的局限性。首先，我们提出了熵校正（Entropy Calibration， EC）方法，该方法利用预测熵来衡量样本在标签传播过程中的影响，从而动态评估样本的可靠性。其次，我们提出的熵感知原型嵌入（EPE）将原型视为不断进化的合成节点，基于校准的预测迭代更新它们，并将高确定性原型嵌入到图中。通过标签校准、熵感知原型嵌入和标签传播的迭代，该方法提高了分类精度和鲁棒性。广泛的实验表明，ECPE在三个标准的换能器FSL基准测试中超过了最先进的性能。我们的源代码发布在：https://github.com/gmf-ahu/ECPE。

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引用次数: 0

Compressing model with few class-imbalance samples: An out-of-distribution expedition 类不平衡样本较少的压缩模型：一次分布外考察

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-13 DOI: 10.1016/j.patrec.2026.01.010

Tian-Shuang Wu , Shen-Huan Lyu , Yanyan Wang , Ning Chen , Zhihao Qu , Baoliu Ye

Few-sample model compression aims to compress a large pre-trained model into a compact one using only a few samples. However, previous methods typically assume a balanced class distribution, which is costly under severe data scarcity. In the presence of imbalance, the compressed model exhibits significant performance degradation. We propose a novel framework named OOD-Enhanced Few-Sample Model Compression (OE-FSMC), introducing out-of-distribution (OOD) samples with dynamically assigned labels to prevent bias during the compression process. To avoid overfitting the OOD samples, we incorporate a joint distillation loss and a class-dependent regularization term. Extensive experiments on multiple benchmark datasets show that our framework can be seamlessly incorporated into existing few-sample model compression methods, effectively mitigating the accuracy degradation caused by class imbalance.

少样本模型压缩的目的是利用少量样本将一个大的预训练模型压缩成一个紧凑的模型。然而，以前的方法通常假设类分布平衡，这在严重的数据稀缺性下是昂贵的。在存在不平衡的情况下，压缩模型表现出明显的性能下降。我们提出了一个新的框架，称为OOD增强的少样本模型压缩（OE-FSMC），引入了带有动态分配标签的out- distribution （OOD）样本，以防止压缩过程中的偏差。为了避免OOD样本的过拟合，我们结合了一个联合蒸馏损失和一个类相关的正则化项。在多个基准数据集上的大量实验表明，我们的框架可以无缝地结合到现有的少样本模型压缩方法中，有效地缓解了类不平衡导致的精度下降。

引用次数: 0

MER-CAPF: Audio-text emotion recognition through cross-attention mechanism and multi-granularity pooling strategy MER-CAPF：基于交叉注意机制和多粒度池化策略的声文情感识别

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-13 DOI: 10.1016/j.patrec.2026.01.008

Chengming Chen, Pengyuan Liu, Zhicheng Dong, Zhuo He, Zhijian Li

In the field of Human–Computer Interaction (HCI), emotion recognition is regarded as a critical yet challenging task due to its multimodal nature and limitations in data acquisition. To achieve accurate recognition of multimodal emotional information such as speech and text, this paper proposes a novel multimodal emotion recognition framework, MER-CAPF (Multimodal Emotion Recognition with Cross-Attention and Pooling Fusion). The framework employs a hierarchically frozen BERT model and a depthwise separable convolutional neural network (DSCNN) combined with a Bi-LSTM to extract features from the text and audio modalities, respectively. During the feature fusion stage, a multi-head cross-attention mechanism and multi-granularity pooling strategy are introduced to fully capture semantic and acoustic associations across modalities. In addition, the model incorporates parallel modality encoders with a progressive modality alignment mechanism to achieve synergistic alignment and deep interaction between speech and text features. Experiments conducted on three public benchmark datasets—IEMOCAP, MELD, and CMU-MOSEI demonstrate that MER-CAPF achieves accuracies of 74.73%, 63.26% and 67.38% on the IEMOCAP, MELD and CMU-MOSEI respectively, outperforming most existing methods and reaching a level comparable to recent state-of-the-art models, thereby validating the effectiveness and robustness of the proposed framework.

在人机交互（HCI）领域，情感识别由于其多模态特性和数据获取的局限性而被认为是一项关键而具有挑战性的任务。为了实现语音和文本等多模态情感信息的准确识别，本文提出了一种新的多模态情感识别框架MER-CAPF （multimodal emotion recognition with Cross-Attention and Pooling Fusion）。该框架采用层次冻结BERT模型和深度可分离卷积神经网络（DSCNN）结合Bi-LSTM分别从文本和音频模式中提取特征。在特征融合阶段，引入了多头交叉注意机制和多粒度池策略，以充分捕获跨模态的语义和声学关联。此外，该模型还结合了并行模态编码器和渐进式模态对齐机制，实现了语音和文本特征之间的协同对齐和深度交互。在IEMOCAP、MELD和CMU-MOSEI三个公共基准数据集上进行的实验表明，MER-CAPF在IEMOCAP、MELD和CMU-MOSEI上分别达到了74.73%、63.26%和67.38%的准确率，优于大多数现有方法，达到了与最近最先进模型相当的水平，从而验证了所提出框架的有效性和鲁棒性。

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引用次数: 0

Nighttime flare removal via frequency decoupling 通过频率解耦去除夜间耀斑

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-13 DOI: 10.1016/j.patrec.2026.01.012

Minglong Xue , Aoxiang Ning , Jinhong He , Shuaibin Fan , Senming Zhong

Existing methods for nighttime flare removal struggle to effectively decouple flare features from normal image texture features, frequently resulting in the loss of local details after flare removal. Due to the differences in the frequency distribution characteristics of flare and content structural information—where flare and lighting information are primarily concentrated in the low-frequency range, while content structural information is concentrated in the high-frequency range—we propose a frequency decoupling de-flare network (FDDNet). This method effectively decouples flare from content, enabling efficient flare removal. Specifically, the network consists of the Frequency Decoupling Module (FDM) and the Frequency Fusion Module (FFM). The FDM divides the image’s frequency features into low-frequency and high-frequency components by setting masks. It dynamically optimizes its weights to effectively decouple flare from content while maximizing the retention of structural content information. In addition, based on the traditional skip connections, we propose the Frequency Fusion Module. The module separately fuses the amplitude and phase of features from both the encoding and decoding stages, reducing the impact of flare and brightness anomalies on the reconstructed image while repairing local damage caused by flare removal. Extensive experiments show that our method significantly improves the performance of nighttime flare removal.

现有的夜间耀斑去除方法难以有效地将耀斑特征与正常图像纹理特征解耦，经常导致耀斑去除后局部细节的丢失。由于照明弹和内容结构信息的频率分布特征不同（照明弹和照明信息主要集中在低频范围，而内容结构信息主要集中在高频范围），本文提出了一种频率解耦去照明弹网络（FDDNet）。这种方法有效地将火炬从内容中分离出来，从而实现有效的火炬移除。具体来说，该网络由频率解耦模块（FDM）和频率融合模块（FFM）组成。FDM通过设置掩模将图像的频率特征分为低频和高频分量。它动态优化其权重，以有效地从内容中分离耀斑，同时最大限度地保留结构内容信息。此外，在传统跳线连接的基础上，提出了频率融合模块。该模块分别对编码和解码阶段的特征幅度和相位进行融合，减少耀斑和亮度异常对重构图像的影响，同时修复耀斑去除造成的局部损伤。大量实验表明，该方法显著提高了夜间耀斑去除的性能。

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引用次数: 0

Leveraging language to generalize natural images to few-shot medical image segmentation 利用语言对自然图像进行泛化，实现少镜头医学图像分割

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-13 DOI: 10.1016/j.patrec.2026.01.009

Feifan Song , Yuntian Bo , Shidong Wang , Yang Long , Haofeng Zhang

Cross-domain Few-shot Medical Image Segmentation (CD-FSMIS) typically involves pre-training on a large-scale source domain dataset (e.g., natural image dataset) before transferring to a target domain with limited data for pixel-wise segmentation. However, due to the significant domain gap between natural images and medical images, existing Few-shot Segmentation (FSS) methods suffer from severe performance degradation in cross-domain scenarios. We observe that using only annotated masks as cross-domain cues is insufficient, while rich textual information can effectively establish knowledge relationships between visual instances and language descriptions, mitigating domain shift. To address this, we propose a plug-in Cross-domain Text-guided (CD-TG) module that leverages text-domain alignment to construct a new alignment space for domain generalization. This plug-in module consists of two components, including: (1) Text Generation Unit that utilizes the GPT-4 question-answering system to generate standardized category-level textual descriptions, and (2) Semantic-guided Unit that aligns visual features with textual embeddings while incorporating existing mask information. We integrate this plug-in module into five mainstream FSS methods and evaluate it on four widely used medical image datasets, and the experimental results demonstrate its effectiveness. Code is available at https://github.com/Lilacis/CD_TG.

跨域少镜头医学图像分割（CD-FSMIS）通常涉及在大规模源域数据集（例如，自然图像数据集）上进行预训练，然后转移到具有有限数据的目标域进行逐像素分割。然而，由于自然图像和医学图像之间存在明显的领域差距，现有的Few-shot Segmentation （FSS）方法在跨领域场景下性能下降严重。我们发现，仅使用带注释的掩码作为跨领域线索是不够的，而丰富的文本信息可以有效地在视觉实例和语言描述之间建立知识关系，减轻领域转移。为了解决这个问题，我们提出了一个插件跨域文本引导（CD-TG）模块，它利用文本域对齐来构建一个新的域泛化对齐空间。该插件模块由两个部分组成，其中：(1)文本生成单元（Text Generation Unit）利用GPT-4问答系统生成标准化的类别级文本描述；(2)语义引导单元（Semantic-guided Unit）将视觉特征与文本嵌入对齐，同时结合现有掩码信息。我们将该插件模块集成到五种主流的FSS方法中，并在四种广泛使用的医学图像数据集上对其进行了评估，实验结果证明了其有效性。代码可从https://github.com/Lilacis/CD_TG获得。

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引用次数: 0

MM-Net: Facial expression recognition based on multi-level and multi-scale attention mechanisms 基于多层次多尺度注意机制的面部表情识别

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-13 DOI: 10.1016/j.patrec.2026.01.007

Dongjing Wang , Hao Peng , Xin Zhang , Na Li , Wenxiu Wang , Jinlin Zhu , Shuiguang Deng

Facial expression recognition (FER) has gained significant attention due to its diverse applications. Achieving accurate facial expression recognition requires the consideration of both global central features and subtle local features. To this end, we propose the Multi-level and Multi-scale Network (MM-Net), an FER network that leverages both multi-level and multi-scale attention mechanisms. Specifically, we design a multi-hierarchical feature learning mechanism to facilitate the FER task with Multi-Level Attention Block (MLAB) and Multi-scale Attention Block (MSAB). The MLAB focuses on learning fine-grained features with adaptive attention across different blocks in the shallow network. Meanwhile, the MSAB facilitates the multi-scale fusion of deep features, enabling the network to capture richer semantic information and expression of features. In addition, we propose Limited Center Loss, which optimizes the network by minimizing the distance between the same classes while increasing the gap between different classes. Experimental results on public datasets show that our proposed MM-Net outperforms current state-of-the-art methods, achieving results of 90.42% on the RAF-DB dataset, 90.05% on FERPlus, 65.91% on AffectNet, and 57.52% on SFEW.

面部表情识别由于其广泛的应用而受到广泛的关注。实现准确的面部表情识别需要同时考虑全局中心特征和微妙的局部特征。为此，我们提出了多层次和多尺度网络（MM-Net），这是一个利用多层次和多尺度注意机制的FER网络。具体来说，我们设计了一个多层次特征学习机制，以促进多层次注意块（MLAB）和多尺度注意块（MSAB）的FER任务。MLAB的重点是学习细粒度的特征，并在浅网络的不同块上使用自适应注意力。同时，MSAB有利于深度特征的多尺度融合，使网络能够捕获更丰富的语义信息和特征表达。此外，我们提出了有限中心损失，它通过最小化相同类别之间的距离而增加不同类别之间的差距来优化网络。在公共数据集上的实验结果表明，我们提出的MM-Net方法优于目前最先进的方法，在RAF-DB数据集上的结果为90.42%，在FERPlus上的结果为90.05%，在AffectNet上的结果为65.91%，在SFEW上的结果为57.52%。

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引用次数: 0

Wavelet-based diffusion transformer for image dehazing 基于小波的图像去雾扩散变换器

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-10 DOI: 10.1016/j.patrec.2026.01.016

Cheng Ma , Guojun Liu , Jing Yue

In current image dehazing methods based on diffusion models, few studies explore and leverage the inherent prior knowledge of hazy images. Additionally, the inherent complexity of these models often results in difficulties during training, which in turn lead to poor restoration performance in dense hazy environments. To address these challenges, this paper proposes a dehazing diffusion model based on Haar wavelet priors, aiming to fully exploit the characteristic that haze information is concentrated in the low-frequency region. Specifically, the Haar wavelet transform is first applied to decompose the hazy image, and the diffusion model is used to generate low-frequency information in the image, thereby reconstructing the main colors and content of the dehazed image. Moreover, a high-frequency enhancement module based on Gabor is designed to extract high-frequency details through multi-directional Gabor convolution filters, further improving the fine-grained restoration capability of the image. Subsequently, a multi-scale pooling block is adopted to reduce blocky artifacts caused by non-uniform haze conditions, enhancing the visual consistency of the image. Finally, the effectiveness of the proposed method is demonstrated on publicly available datasets, and the model’s generalization ability is tested on real hazy image datasets, as well as its potential for application in other downstream tasks. The code is available at https://github.com/Mccc1003/WDiT_Dehaze-main.

在目前基于扩散模型的图像去雾方法中，很少有研究探索和利用模糊图像固有的先验知识。此外，这些模型固有的复杂性往往会给训练带来困难，从而导致在密集雾霾环境下的恢复性能较差。针对这些挑战，本文提出了一种基于Haar小波先验的消雾扩散模型，旨在充分利用雾霾信息集中在低频区域的特点。具体来说，首先利用Haar小波变换对模糊图像进行分解，然后利用扩散模型在图像中生成低频信息，从而重建去雾图像的主要颜色和内容。设计了基于Gabor的高频增强模块，通过多向Gabor卷积滤波器提取高频细节，进一步提高了图像的细粒度恢复能力。随后，采用多尺度池化块来减少雾霾条件不均匀造成的块伪影，增强图像的视觉一致性。最后，在公开可用的数据集上验证了该方法的有效性，并在真实模糊图像数据集上测试了模型的泛化能力，以及在其他下游任务中的应用潜力。代码可在https://github.com/Mccc1003/WDiT_Dehaze-main上获得。

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引用次数: 0

MR-DETR: Miss reduction DETR with context frequency attention and adaptive query allocation strategy for small object detection MR-DETR：基于上下文频率关注和自适应查询分配策略的小目标检测的缺失减少DETR

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-09 DOI: 10.1016/j.patrec.2026.01.004

Hailan Shen, Zihan Wang, Shuo Huang, Zailiang Chen

Small object detection is a critical task in computer vision, aiming to accurately detect tiny instances within images. Although DETR-based methods have improved general object detection, they often suffer from missed detections of small objects due to their limited size and indistinct features. Moreover, DETR-based methods employ a fixed number of queries, making it difficult to adapt to the dynamic variations of scenes. In this study, we propose Miss Reduction DETR (MR-DETR), which leverages Context Frequency Attention (CFA) and an Adaptive Query Allocation Strategy (AQAS) to reduce missed detections. First, to better capture fine details of small objects, CFA is designed with two complementary branches known as context and frequency. The former branch employs axial strip convolutions to capture global contextual information, while the latter branch uses a frequency modulation module to emphasize local high-frequency details. Next, AQAS is introducted, which applies feature excitation and compression to the encoder’s output maps, dynamically evaluates object density, and automatically adjusts the number of queries based on a density-to-query mapping, thereby improving adaptability in complex scenes and reducing missed detections. Experimental results demonstrate that MR-DETR achieves state-of-the-art detection performance on the aerial image datasets VisDrone and AI-TOD, which mainly contain small objects.

小目标检测是计算机视觉中的一项关键任务，旨在准确检测图像中的微小目标。尽管基于der的方法改进了一般的目标检测，但由于小目标的尺寸有限，特征不清晰，往往会漏检。此外，基于der的方法使用固定数量的查询，使其难以适应场景的动态变化。在本研究中，我们提出了Miss Reduction DETR (MR-DETR)，它利用上下文频率注意（CFA）和自适应查询分配策略（AQAS）来减少遗漏检测。首先，为了更好地捕获小对象的精细细节，CFA设计了两个互补的分支，即上下文和频率。前一个分支采用轴向条带卷积来捕获全局上下文信息，而后一个分支使用调频模块来强调局部高频细节。接下来，介绍了AQAS算法，该算法对编码器的输出映射进行特征激励和压缩，动态评估对象密度，并基于密度-查询映射自动调整查询次数，从而提高了复杂场景下的适应性，减少了漏检。实验结果表明，MR-DETR在主要包含小目标的航空图像数据集VisDrone和AI-TOD上达到了最先进的检测性能。

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引用次数: 0

Signature-in-signature: A fidelity-preserving and usability-ensuring framework for dynamic handwritten signature protection 签名中的签名：动态手写签名保护的保真度和可用性框架

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Pattern Recognition Letters

Pub Date : 2026-01-07 DOI: 10.1016/j.patrec.2026.01.001

Tianyu Chen, Qi Cui, Zhangjie Fu

Dynamic handwritten signature (DHS) verification is widely used for identity authentication in modern applications, offering a blend of convenience and security. However, traditional verification processes necessitate users to upload multiple signature templates to remote application servers, raising critical risks to the privacy and security of sensitive data. To address these concerns, this paper proposes a robust watermarking framework for DHS data. By embedding unique watermarks as digital signatures into DHS data, the framework ensures effective traceability of DHS, allowing the identification of sources in case of DHS misuse or leakage. Specifically, we introduce a velocity-based loss function that minimizes trajectory distortion during the watermark embedding process, effectively preserving the fidelity of the DHS. In parallel, the training process leverages contrastive learning to ensure that the watermarked DHS remains closer to the original signature in feature space than to other DHS templates. This design guarantees that the usability of the watermarked DHS is unaffected, maintaining the accuracy and reliability of signature verification systems. Extensive experiments conducted on the large-scale dynamic signature dataset demonstrate that the watermarked signatures retain visual integrity and remain imperceptible to human observation. Furthermore, the embedded watermarks exhibit compatibility with a wide range of existing verification methods, ensuring that the framework does not compromise existing verification performance.

动态手写签名（DHS）验证在现代应用程序中广泛用于身份验证，它提供了便利性和安全性。然而，传统的验证过程需要用户将多个签名模板上传到远程应用服务器，这给敏感数据的隐私和安全带来了严重的风险。为了解决这些问题，本文提出了一种针对国土安全部数据的鲁棒水印框架。通过将独特的水印作为数字签名嵌入到国土安全部数据中，该框架确保了国土安全部的有效可追溯性，在国土安全部误用或泄漏的情况下可以识别来源。具体来说，我们引入了一个基于速度的损失函数，该函数在水印嵌入过程中最大限度地减少了轨迹失真，有效地保持了DHS的保真度。同时，训练过程利用对比学习来确保水印的DHS在特征空间中比其他DHS模板更接近原始签名。这种设计保证了水印DHS的可用性不受影响，保证了签名验证系统的准确性和可靠性。在大规模动态签名数据集上进行的大量实验表明，水印后的签名保持了视觉完整性，并且不被人类观察到。此外，嵌入的水印显示出与广泛的现有验证方法的兼容性，确保该框架不会损害现有的验证性能。

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引用次数: 0