Signal Processing-Image Communication最新文献_第2页

Advancing multimodal biometric image retrieval with sparse spectral graph convolution network and banyan tree growth optimization 基于稀疏谱图卷积网络和榕树生长优化的多模态生物特征图像检索

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-30 DOI: 10.1016/j.image.2026.117501

D. Binu

Recently, multimodal biometric systems have gained attention for enhancing recognition accuracy and robustness, yet they still face issues like noise interference, redundant features, low accuracy, and inefficient data integration. To overcome these complications, Advancing Multimodal Biometric Image Retrieval with Sparse Spectral Graph Convolution network and Banyan Tree Growth Optimization (MBI-SSGCN-BTGO) is proposed. Here, the input images are collected from soco-fingerprint-female-and-male, face-recognition, and mmu-iris-datasets. The input images are preprocessed using the Fast Guided Median Filter (FGMF) for contrast correction, image scaling, cropping, and normalization. Afterward, the Holistic Dynamic Frequency Transform (HDFT) is used to extract features from images. Then, Snow Ablation Optimization (SAO) is used to choose the most relevant features. The optimal features are used for image retrieval, aiding in identity verification prior to classification. The classification is done by Sparse Spectra Graph Convolutional Network (SSGCN) to classify the biometric system, such as woman and man for face-recognition dataset, female and male for soco-fingerprint-female-and-male dataset and left and right for mmu-iris-dataset. Finally, the Banyan Tree Growth Optimization (BTGO) algorithm is employed to optimize the weight parameters of SSGCN. By integrating BTGO, the model efficiently identifies optimal feature representations, improving convergence speed and overall classification performance. The proposed MBI-SSGCN-BTGO approach is implemented in Python and its performance is examined undersome metrics. The performance of MBI-SSGCN-BTGO technique attains 16.17 %, 17.43 %, 19.23 % lower False Acceptance Rate (FAR) and 29.45 %, 28.42 % and 29.11 % higher precision and 26.17 %, 27.43 % when compared with existing techniques respectively.

近年来，多模态生物识别系统在提高识别精度和鲁棒性方面受到了广泛关注，但仍存在噪声干扰、特征冗余、准确率低、数据集成效率低等问题。为了克服这些问题，提出了基于稀疏谱图卷积网络和榕树生长优化的多模态生物特征图像检索方法（MBI-SSGCN-BTGO）。在这里，输入的图像是从社会-指纹-女性和男性，面部识别和虹膜-数据集收集的。使用快速引导中值滤波器（FGMF）对输入图像进行对比度校正、图像缩放、裁剪和归一化预处理。然后，利用整体动态频率变换（HDFT）对图像进行特征提取。然后，利用雪消融优化（SAO）选择最相关的特征。最优特征用于图像检索，有助于在分类之前进行身份验证。利用稀疏光谱图卷积网络（SSGCN）对生物识别系统进行分类，如人脸识别数据集为女性和男性，社会指纹-女性和男性数据集为女性和男性，mu-虹膜数据集为左和右。最后，采用Banyan Tree Growth Optimization （BTGO）算法对SSGCN的权值参数进行优化。通过集成BTGO，模型有效地识别出最优特征表示，提高了收敛速度和整体分类性能。提出的MBI-SSGCN-BTGO方法在Python中实现，并根据一些指标检查其性能。与现有技术相比，mbi - ssgn - btgo技术的误接受率（FAR）分别降低了16.17%、17.43%、19.23%，准确率分别提高了29.45%、28.42%、29.11%和26.17%、27.43%。

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引用次数: 0

Research on path planning utilizing landmark salient features and composition techniques 利用地标性显著特征和构图技术的路径规划研究

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-30 DOI: 10.1016/j.image.2026.117505

Cong Xiao, Gang Chen, Zhengwei Yao, Weijie Zhang, Zhaohui Huang, Qingshu Yuan

In virtual reality, traditional path planning often focuses solely on the shortest route, neglecting the pursuit of scenic beauty and immersive experiences for visitors. This paper presents an aesthetic global path planning framework that integrates landmark saliency with photographic composition principles to generate visually pleasing roaming routes in virtual environments. Unlike previous approaches, which either rely solely on visual saliency maps or use handcrafted aesthetic rules for viewpoint selection, our method establishes a unified optimization process that couples perceptual significance and aesthetic spatial flow. Specifically, the framework introduces an Aesthetic A* algorithm that extends traditional A* pathfinding by incorporating a dual-objective cost function balancing geometric distance and aesthetic score. In addition, landmark-aware constraints ensure that the generated viewpoints align with salient scene elements, while a cubic Bezier-based path smoother refines local trajectories to preserve visual continuity. Experiments on various architectural and cultural scenes demonstrate that the proposed method not only improves the visual coherence and landmark coverage of roaming paths but also yields significantly higher subjective aesthetic ratings compared to existing saliency-based or rule-based approaches.

在虚拟现实中，传统的路径规划往往只关注最短路线，忽视了对风景美和游客沉浸式体验的追求。本文提出了一种美学全局路径规划框架，该框架将地标性显著性与摄影构图原则相结合，以生成视觉上令人愉悦的虚拟环境漫游路线。与之前的方法不同，这些方法要么仅仅依赖于视觉显著性地图，要么使用手工制作的美学规则来选择视点，我们的方法建立了一个统一的优化过程，将感知意义和美学空间流结合起来。具体来说，该框架引入了美学A*算法，该算法通过结合平衡几何距离和美学分数的双目标成本函数来扩展传统的A*寻路。此外，地标感知约束确保生成的视点与突出的场景元素对齐，而基于立方贝塞尔的路径平滑器则精炼局部轨迹以保持视觉连续性。对各种建筑和文化场景的实验表明，与现有的基于显著性或基于规则的方法相比，所提出的方法不仅提高了漫游路径的视觉一致性和地标覆盖率，而且产生了显着更高的主观审美评分。

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引用次数: 0

Markerless emotion recognition from full-body movements for Social XR 社交XR的全身动作无标记情感识别

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-21 DOI: 10.1016/j.image.2026.117489

Michael Neri , Sara Baldoni , Marco Carli , Federica Battisti

In this work, an emotion recognition system for enhancing social XR applications is presented. Although several techniques for emotion recognition have been proposed in the literature, they either require invasive and advanced equipment or exploit facial expressions, speech excerpts, physiological data, and text. In this contribution, on the contrary, an approach for markerless emotion classification through body language is designed. More specifically, human movements are analyzed over time by extracting the skeleton joints in videos acquired by consumer cameras. A normalization procedure has been introduced to provide a depth-independent skeleton representation without distorting the skeleton shape. The performance of the proposed method have been assessed using a dataset of videos recorded from multiple points of view. An ad-hoc learning-based emotion classifier has been trained to recognize four emotions (happiness, boredom, interest, and disgust) achieving an average accuracy of 72.5%. The pre-processed dataset, code, and demo with pre-trained models are available at https://github.com/michaelneri/emotion-recognition-human-movements.

在这项工作中，提出了一个增强社交XR应用的情感识别系统。虽然在文献中提出了几种情绪识别技术，但它们要么需要侵入性和先进的设备，要么利用面部表情、语音摘录、生理数据和文本。相反，本文设计了一种通过肢体语言进行无标记情感分类的方法。更具体地说，通过从消费者相机获取的视频中提取骨骼关节，随着时间的推移分析人类的运动。引入了一种归一化过程，以提供与深度无关的骨架表示，而不会扭曲骨架形状。使用从多个角度记录的视频数据集评估了所提出方法的性能。一个专门的基于学习的情绪分类器已经被训练来识别四种情绪（快乐、无聊、兴趣和厌恶），平均准确率为72.5%。预处理的数据集、代码和带有预训练模型的演示可以在https://github.com/michaelneri/emotion-recognition-human-movements上获得。

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引用次数: 0

Refining data granularity and feature fusion for boundary refinement in instance segmentation 细化数据粒度和特征融合，用于实例分割的边界细化

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-19 DOI: 10.1016/j.image.2026.117490

Yumeng Yan , Mingming Kong , Maochao Zhang , Shunnan Zhao , Chao Zhang

Considerable efforts have been made in the development of current instance segmentation approaches, but the segmentation of mask boundaries remains a challenge. Feature maps with low spatial resolution, along with the small proportion of edge pixels in relation to the total pixel count, lead to inaccurate boundaries in instance masks. Furthermore, the parsing of feature maps in high resolution networks is typically at a low level, making it difficult for the network to learn deeper semantic features. This paper presents improvements to Boundary Patch Refinement (BPR) for Instance Segmentation to address the above issues. First, we improve the bounding box extraction methods utilized in the data processing, refining the granularity of the data. Second, we introduce a feature fusion approach specifically designed to optimize the feature fusion module within the backbone network. Third, we propose Deep enhancement and Memory optimization (DAM), a module that enhances the network’s ability to learn deeper features, improves its efficiency in acquiring semantic information, and substantially reduces the computational overhead during training. Experimental results demonstrate that our network yields notable improvements in both segmentation accuracy and computational efficiency and outperforms existing methods. The code is available at https://github.com/njezmjez/RDGFBR.

目前的实例分割方法已经取得了长足的发展，但掩码边界的分割仍然是一个挑战。低空间分辨率的特征地图，以及边缘像素相对于总像素数的比例很小，导致实例掩码的边界不准确。此外，在高分辨率网络中，特征映射的解析通常处于较低的层次，这使得网络难以学习更深层次的语义特征。为了解决上述问题，本文提出了边界补丁细化（BPR）的实例分割方法。首先，我们改进了数据处理中使用的边界框提取方法，细化了数据的粒度。其次，我们引入了一种专门设计的特征融合方法，以优化骨干网内的特征融合模块。第三，我们提出了深度增强和记忆优化（DAM）模块，该模块增强了网络学习更深层特征的能力，提高了其获取语义信息的效率，并大大减少了训练过程中的计算开销。实验结果表明，我们的网络在分割精度和计算效率方面都有显著提高，并且优于现有的方法。代码可在https://github.com/njezmjez/RDGFBR上获得。

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引用次数: 0

Enhancing few-shot semantic segmentation in remote sensing through magnitude-based pruning 利用基于量值的剪枝增强遥感少镜头语义分割

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-19 DOI: 10.1016/j.image.2026.117492

Kingsley Amoafo , Godwin Banafo Akrong , Ebenezer Owusu

Few-Shot Semantic Segmentation in remote sensing faces significant challenges owing to the limited availability of labeled data and the complexity of high-resolution imagery. To address these challenges, we propose a novel framework that integrates magnitude-based pruning with Cross-Matching and Self-Matching modules. By systematically pruning 30% of the redundant weights from the backbone network, we enhanced the feature extraction and segmentation accuracy while maintaining the model efficiency. The Cross-Matching Module establishes robust semantic correspondences between the support and query images, whereas the Self-Matching Module refines segmentation through intra-query correlations, incorporating spatial and semantic proximity to improve the feature consistency. Experimental evaluations of the DLRSD-5i and ISAID-5i datasets demonstrated the effectiveness of the proposed method. On DLRSD-5i, the pruned SCCNet achieved a mean mIoU improvement of +9.40 (1-shot) and +6.46 (5-shot) compared to the baseline, outperforming state-of-the-art models. Similarly, on ISAID-5i, the pruned ResNet-101 surpassed SOTA by +1.18 (1-shot) and +0.69 (5-shot) in mean mIoU. These results validate the effectiveness of pruning in optimizing the baseline model for FSSS tasks, thereby enhancing its ability to generalize and accurately segment complex remote-sensing imagery. We demonstrated that high-quality pruned feature maps can enhance segmentation accuracy without the need for additional enhancement modules. This approach not only improves segmentation performance but also provides valuable insights into the role of backbone optimization in FSSS. Our findings highlight the potential of magnitude-based pruning as a foundational strategy for aligning backbone optimization with the demands of few-shot tasks, thereby offering a scalable solution for remote sensing segmentation tasks.

由于标记数据的可用性有限和高分辨率图像的复杂性，遥感中的少镜头语义分割面临着重大挑战。为了解决这些挑战，我们提出了一个新的框架，该框架将基于量的修剪与交叉匹配和自匹配模块集成在一起。通过系统地修剪骨干网络中30%的冗余权值，在保持模型效率的同时，提高了特征提取和分割的精度。交叉匹配模块在支持图像和查询图像之间建立鲁棒的语义对应关系，而自匹配模块通过查询内相关性来细化分割，结合空间和语义接近性来提高特征一致性。对DLRSD-5i和ISAID-5i数据集的实验评估证明了该方法的有效性。在DLRSD-5i上，与基线相比，修剪后的SCCNet的平均mIoU提高了+9.40（1次）和+6.46（5次），优于最先进的模型。同样，在ISAID-5i上，修剪后的ResNet-101的平均mIoU比SOTA高出+1.18（1次）和+0.69（5次）。这些结果验证了修剪在优化FSSS任务基线模型方面的有效性，从而增强了其对复杂遥感图像的泛化和准确分割能力。我们证明了高质量的修剪特征映射可以提高分割精度，而不需要额外的增强模块。这种方法不仅提高了分割性能，而且对骨干优化在FSSS中的作用提供了有价值的见解。我们的研究结果强调了基于震级的剪枝作为一种基本策略的潜力，可以使主干优化与少镜头任务的需求保持一致，从而为遥感分割任务提供可扩展的解决方案。

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引用次数: 0

Global and interactive graph channel attention for robust stereo matching 全局和交互式图形通道关注鲁棒立体匹配

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-19 DOI: 10.1016/j.image.2026.117491

Jun Yu , Xiaofeng Wang , Yingying Su , Zhiheng Sun , Jiameng Sun

Current learning-based stereo matching is generally poor in adaptively exploring the robust and salient features at different scenes, leading to ambiguity of matching, especially in challenging areas. To tackle this problem, inspired by the global representation of the graph, we propose a Graph Channel Attention (GCA) to globally and interactively learn binocular attention for robust stereo matching, instead of traditional separate local monocular attention. We first construct a 2D binocular graph structure with left and right subgraphs, where the left and right channel information can globally interact. After that, our interactive graph inference with cross interaction and inner aggregation is proposed to improve the linkage inference between and within binocular graphs, which can consider global and interactive attention information like real human eyes. Thus, our GCA alters the channel attention from traditional 1D to binocular 2D, which can imitate the global interaction and attention ability of real human eyes. Finally, we utilize the GCA into stereo matching, and experiment results show that our method demonstrates state-of-the-art performance on KITTI 2012/2015 and Middlebury Stereo Evaluation v.3.

目前基于学习的立体匹配在不同场景下的鲁棒性和显著性特征的自适应挖掘能力较差，导致匹配的模糊性，特别是在具有挑战性的区域。为了解决这个问题，受图的全局表示的启发，我们提出了一种图通道注意（GCA）来全局交互式地学习双眼注意，以实现鲁棒的立体匹配，而不是传统的单独的局部单眼注意。我们首先构建了一个具有左右子图的二维双目图结构，其中左右通道信息可以全局交互。在此基础上，提出了具有交叉交互和内部聚合的交互图推理，改进了双目图之间和双目图内部的联动推理，使其能够像真实人眼一样考虑全局和交互的注意信息。因此，我们的GCA将通道注意力从传统的1D转变为双目2D，可以模仿真实人眼的全局交互和注意力能力。最后，我们将GCA应用到立体匹配中，实验结果表明，我们的方法在KITTI 2012/2015和Middlebury stereo Evaluation v.3上具有最先进的性能。

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引用次数: 0

KNN improved Transformer for 3D object detection KNN改进的3D目标检测变压器

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-16 DOI: 10.1016/j.image.2026.117488

Chen Jiang, Shuxia Lu, Xianghu Zhou, Tingting Ma, Junhai Zhai

In recent years, 3D object detection in autonomous driving perception has gained significant attention in the industry. Due to its characteristics, LiDAR has become the most commonly used and essential sensor. However, voxel-based networks often lose context information during the voxelization process, which negatively impacts the detection of small objects. In this paper, we address the challenge of low accuracy in LiDAR-based detection, especially for small object categories, by proposing an improved Transformer structure. Transformers are a type of deep learning model known for their ability to capture long-range dependencies and contextual relationships in data. In our approach, we incorporate a k-Nearest Neighbors (KNN) algorithm, which is a method for identifying the closest points in space, to enhance the spatial relationships between point clouds. This combination allows the model to better capture context information, strengthen feature extraction, and significantly reduce both missed and false detections. Our method is designed to be plug-and-play, allowing it to be directly applied to existing point cloud detectors. We evaluate our approach on the public KITTI and Astyx datasets. Experimental results show significant improvements, especially in detecting small object categories, even in challenging conditions.

近年来，自动驾驶感知中的三维物体检测受到了业界的广泛关注。由于激光雷达的特点，它已成为最常用和必不可少的传感器。然而，基于体素的网络在体素化过程中往往会丢失上下文信息，这对小物体的检测产生了负面影响。在本文中，我们通过提出一种改进的Transformer结构来解决基于lidar的检测精度低的挑战，特别是对于小目标类别。变形金刚是一种深度学习模型，以其捕获数据中的长期依赖关系和上下文关系的能力而闻名。在我们的方法中，我们结合了k-最近邻（KNN）算法，这是一种识别空间中最近点的方法，以增强点云之间的空间关系。这种组合使模型能够更好地捕获上下文信息，加强特征提取，并显着减少遗漏和错误的检测。我们的方法被设计为即插即用，允许它直接应用于现有的点云探测器。我们在公共KITTI和Astyx数据集上评估了我们的方法。实验结果显示了显著的改进，特别是在检测小物体类别方面，即使在具有挑战性的条件下。

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引用次数: 0

FlyAwareV2: A multimodal cross-domain UAV dataset for urban scene understanding FlyAwareV2：用于城市场景理解的多模态跨域无人机数据集

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-16 DOI: 10.1016/j.image.2026.117483

Francesco Barbato , Matteo Caligiuri , Pietro Zanuttigh

The development of computer vision algorithms for Unmanned Aerial Vehicle (UAV) applications in urban environments heavily relies on the availability of large-scale datasets with accurate annotations. However, collecting and annotating real-world UAV data is extremely challenging and costly. To address this limitation, we present FlyAwareV2, a novel multimodal dataset encompassing both real and synthetic UAV imagery tailored for urban scene understanding tasks. Building upon the recently introduced SynDrone and FlyAware datasets, FlyAwareV2 introduces several new key contributions: (1) Multimodal data (RGB, depth, semantic labels) across diverse environmental conditions including varying weather and daytime; (2) Depth maps for real samples computed via state-of-the-art monocular depth estimation; (3) Benchmarks for RGB and multimodal semantic segmentation on standard architectures; (4) Studies on synthetic-to-real domain adaptation to assess the generalization capabilities of models trained on the synthetic data. With its rich set of annotations and environmental diversity, FlyAwareV2 provides a valuable resource for research on UAV-based 3D urban scene understanding. Dataset link: https://medialab.dei.unipd.it/paper_data/FlyAwareV2

城市环境中无人机（UAV）应用的计算机视觉算法的发展严重依赖于具有准确注释的大规模数据集的可用性。然而，收集和注释真实世界的无人机数据是极具挑战性和昂贵的。为了解决这一限制，我们提出了FlyAwareV2，这是一个新的多模态数据集，包含为城市场景理解任务量身定制的真实和合成无人机图像。基于最近推出的SynDrone和FlyAware数据集，FlyAwareV2引入了几个新的关键贡献：(1)跨不同环境条件（包括不同天气和白天）的多模态数据（RGB，深度，语义标签）；(2)通过最先进的单目深度估计计算的真实样本深度图；(3)标准架构下RGB和多模态语义分割的基准测试；(4)研究综合到实际的域自适应，以评估在综合数据上训练的模型的泛化能力。凭借其丰富的注释和环境多样性，FlyAwareV2为基于无人机的3D城市场景理解研究提供了宝贵的资源。数据集链接：https://medialab.dei.unipd.it/paper_data/FlyAwareV2

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引用次数: 0

UHW-former: U-shape hybrid transformer with wavelet-based multi-scale feature fusion for nighttime UAV tracking UHW-former：基于小波多尺度特征融合的u型混合变压器，用于夜间无人机跟踪

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-15 DOI: 10.1016/j.image.2026.117484

Haijun Wang, Haoyu Qu, Lihua Qi, Zihao Su

Most advancements in unmanned aerial vehicle (UAV) tracking have focused on daytime scenarios with optimal lighting conditions. However, the unpredictable and complex noise inherent in camera systems significantly impairs the effectiveness of UAV tracking algorithms, particularly in low-light environments. To address this challenge, we introduce a novel U-shaped plug-and-play denoising network that reduces cluttered and intricate real-world noise, thereby enhancing nighttime UAV tracking performance. Specifically, the U-shaped denoising network utilizes a CNN-Transformer block as the encoder, which incorporates hybrid attention to simultaneously capture both local details and global structures. Additionally, to further improve the denoising effect, we design a wavelet-based multi-scale feature fusion block that adaptively combines features from various stages of the encoding process. Finally, we develop a multi-feature collaboration decoder to fully integrate comprehensive features through multi-head transposed cross-attention. Extensive experiments demonstrate that the proposed UHW-former achieves remarkable denoising performance and significantly enhances nighttime UAV tracking.

无人机（UAV）跟踪的大多数进展都集中在具有最佳照明条件的白天场景。然而，相机系统中固有的不可预测和复杂的噪声显著地削弱了无人机跟踪算法的有效性，特别是在低光环境中。为了应对这一挑战，我们引入了一种新颖的u形即插即用去噪网络，减少了混乱和复杂的现实世界噪声，从而提高了夜间无人机的跟踪性能。具体来说，u形去噪网络利用CNN-Transformer块作为编码器，它结合了混合注意，同时捕获局部细节和全局结构。此外，为了进一步提高去噪效果，我们设计了一个基于小波的多尺度特征融合块，自适应地将编码过程中各个阶段的特征融合在一起。最后，我们开发了一个多特征协同解码器，通过多头转置交叉注意充分集成综合特征。大量实验表明，所提出的UHW-former具有良好的去噪性能，显著提高了无人机夜间跟踪能力。

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引用次数: 0

Cryptospace image steganography for cloud security via cycle-consistent GAN 基于周期一致GAN的云安全加密空间图像隐写

IF 2.7 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Signal Processing-Image Communication

Pub Date : 2026-01-14 DOI: 10.1016/j.image.2026.117487

Shuying Xu , Chin-Chen Chang , Ji-Hwei Horng , Ching-Chun Chang

Cryptospace steganography has attracted increasing attention as an effective approach for enhancing data security in cloud environments. This paper proposes a hybrid framework that integrates cycle-consistent generative adversarial networks (CycleGAN) with the difference expansion (DE) technique to provide both image encryption and data hiding services. In the proposed framework, an image encryption network and a data encryption network are designed to encrypt the digital image and secret data, respectively, enabling a key-free architecture. A dropout-driven strategy is further introduced to support secure and isolated access control for multiple user groups on a shared cloud platform. Experimental results show that the proposed method achieves an embedding rate above 0.47 bpp and a secret data extraction accuracy exceeding 91%, demonstrating superior performance compared with state-of-the-art methods.

加密空间隐写作为一种增强云环境下数据安全性的有效方法，越来越受到人们的关注。本文提出了一种将循环一致生成对抗网络（CycleGAN）与差分展开（DE）技术相结合的混合框架，以提供图像加密和数据隐藏服务。在提出的框架中，设计了一个图像加密网络和一个数据加密网络，分别对数字图像和秘密数据进行加密，从而实现无密钥架构。进一步引入了退出驱动策略，以支持共享云平台上多个用户组的安全和隔离访问控制。实验结果表明，该方法的嵌入率在0.47 bpp以上，秘密数据提取准确率超过91%，与现有方法相比，性能优越。

引用次数: 0