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A Semantic Controllable Long Text Steganography Framework Based on LLM Prompt Engineering and Knowledge Graph 基于 LLM 提示工程和知识图谱的语义可控长文本隐写术框架
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-09 DOI: 10.1109/LSP.2024.3456636
Yihao Li;Ru Zhang;Jianyi Liu;Qi Lei
With ongoing advancements in natural language technology, text steganography has achieved notable progress. However, existing methods primarily concentrate on the probability distribution between words, often overlooking comprehensive control over text semantics. Particularly in the case of longer texts, these methods struggle to preserve coherence and contextual consistency, thereby increasing the risk of detection in practical applications. To effectively improve steganography security, we propose a semantic controllable long-text steganography framework based on prompt engineering and knowledge graph (KG) integration, obviating supplementary training. This framework leverages triplets from the KG and task descriptions to construct prompts, directing the large language model (LLM) to generate text that aligns with the triplet content. Subsequently, the model effectively embeds secret information by encoding the candidate pools established around the sampled target words. The experimental results demonstrate that our framework ensures the concealment of steganographic text while maintaining the relevance and consistency of the content as expected. Moreover, it can be flexibly adapted to various application scenarios, showcasing its potential and advantages in practical implementations.
随着自然语言技术的不断进步,文本隐写术取得了显著进展。然而,现有的方法主要集中在词与词之间的概率分布上,往往忽略了对文本语义的全面控制。特别是对于较长的文本,这些方法很难保持连贯性和上下文的一致性,从而增加了实际应用中被检测到的风险。为了有效提高隐写术的安全性,我们提出了一种基于提示工程和知识图谱(KG)集成的语义可控长文本隐写术框架,从而避免了补充培训。该框架利用知识图谱和任务描述中的三元组构建提示,引导大语言模型(LLM)生成与三元组内容一致的文本。随后,该模型通过对围绕采样目标词建立的候选词库进行编码,有效地嵌入了秘密信息。实验结果表明,我们的框架既能确保隐写文本的隐蔽性,又能保持预期内容的相关性和一致性。此外,它还能灵活地适应各种应用场景,展示了其在实际应用中的潜力和优势。
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引用次数: 0
Fine-Grained Temporal-Enhanced Transformer for Dynamic Facial Expression Recognition 用于动态面部表情识别的细粒度时态增强变换器
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-09 DOI: 10.1109/LSP.2024.3456668
Yaning Zhang;Jiahe Zhang;Linlin Shen;Zitong Yu;Zan Gao
Dynamic facial expression recognition (DFER) plays a vital role in understanding human emotions and behaviors. Existing efforts tend to fall into a single modality self-supervised pretraining learning paradigm, which limits the representation ability of models. Besides, coarse-grained temporal modeling struggles to capture subtle facial expression representations from various inputs. In this letter, we propose a novel method for DFER, termed fine-grained temporal-enhanced transformer (FTET-DFER), which consists of two stages. First, we employ the inherent correlation between visual and auditory modalities in real videos, to capture temporally dense representations such as facial movements and expressions, in a self-supervised audio-visual learning manner. Second, we utilize the learned embeddings as targets, to achieve the DFER. In addition, we design the FTET block to study fine-grained temporal-enhanced facial expression features based on intra-clip locally-enhanced relations as well as inter-clip locally-enhanced global relationships in videos. Extensive experiments show that FTET-DFER outperforms the state-of-the-arts through within-dataset and cross-dataset evaluation.
动态面部表情识别(DFER)在理解人类情绪和行为方面起着至关重要的作用。现有的研究往往陷入单一模态自监督预训练学习模式,这限制了模型的表征能力。此外,粗粒度的时间建模难以捕捉来自各种输入的微妙面部表情表征。在这封信中,我们提出了一种新的 DFER 方法,称为细粒度时空增强变换器(FTET-DFER),它包括两个阶段。首先,我们利用真实视频中视觉和听觉模式之间固有的相关性,以自我监督的视听学习方式捕捉时间上密集的表征,如面部动作和表情。其次,我们利用学习到的嵌入作为目标,实现 DFER。此外,我们还设计了 FTET 模块,以研究基于视频片段内局部增强关系和片段间局部增强全局关系的细粒度时间增强面部表情特征。大量实验表明,通过数据集内和跨数据集评估,FTET-DFER 的表现优于同行。
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引用次数: 0
Speed-Improved Off-Focus Imaging Technique for Real-Aperture Imaging System Based on Wavenumber Spectrum Fusion 基于 Wavenumber Spectrum Fusion 的实感孔径成像系统离焦速度改进型成像技术
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-09 DOI: 10.1109/LSP.2024.3456635
WenRui Zhang;ShiYou Wu;YiCai Ji;Chao Li;GuangYou Fang
This letter introduces a speed-improved imaging technique for a real-aperture MIMO system engaged in off-focus imaging. The existing off-focus imaging algorithm is time-intensive due to the extensive interpolation and reliance on traditional frequency-domain back-projection algorithms (FDBPA). To address its limitation, the least squares (LS) method is used to fit the non-analytical single-trip history function into a second-order polynomial function. Both MIMO array and the wideband received chirp signal are subdivided into multiple sub-arrays and sub-bands to reconstruct the low-resolution images using FDBPA. The wavenumber spectrums of these low-resolution images are band-limited, thus they can be combined to get the global high-resolution image. Simulation and experiment confirmed the efficacy of the proposed technique called Fusion-BPA. It is a state-of-art, fast algorithm to deal with the non-linear scene imaging problem.
这封信介绍了一种用于离焦成像的真实孔径 MIMO 系统的速度改进型成像技术。现有的离焦成像算法需要大量插值并依赖于传统的频域反投影算法(FDBPA),因此耗费大量时间。为解决其局限性,采用最小二乘法(LS)将非分析性单程历史函数拟合为二阶多项式函数。MIMO 阵列和接收到的宽带啁啾信号都被细分为多个子阵列和子波段,利用 FDBPA 重建低分辨率图像。这些低分辨率图像的波谱是带限的,因此可以将它们组合起来得到全局高分辨率图像。模拟和实验证实了所提出的 Fusion-BPA 技术的有效性。这是一种处理非线性场景成像问题的先进快速算法。
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引用次数: 0
A Frequency Domain Auxiliary Network for Image Retrieval 用于图像检索的频域辅助网络
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-09 DOI: 10.1109/LSP.2024.3456632
Zhiming Zhang;Jiao Liu;Yongfeng Dong;Jun Zhang
Image retrieval aims to find the most semantically similar images in the database. Existing deep hash-based retrieval algorithms utilize data augmentation strategies thus generating generalized hash codes. However, simple data augmentation only improves the accuracy of hash codes from the perspective of sample diversity, without fully utilizing the inherent characteristics of the images. In this letter, we explore the frequency domain information of images and propose a Frequency Domain Auxiliary Network (FDANet) for deep hash retrieval. To capture frequency domain information that can cope with image transformations, we develop the spectrum enhancement module (SEM) in FDANet. The SEM utilizes Fourier transform techniques to extract the amplitude component that can reflect the low-level statistics of the image. Then, leveraging the extracted amplitude components, the retrieval network enhances its perception of regions undergoing relative changes in the original spatial domain. Experiments on several image retrieval benchmarks demonstrate that our method outperforms other state-of-the-art hash algorithms in terms of performance on the test metrics.
图像检索旨在找到数据库中语义最相似的图像。现有的基于深度散列的检索算法采用数据增强策略,从而生成通用散列码。然而,简单的数据增强只能从样本多样性的角度提高哈希编码的准确性,而不能充分利用图像的固有特性。在这封信中,我们探索了图像的频域信息,并提出了一种用于深度哈希检索的频域辅助网络(FDANet)。为了捕捉能应对图像变换的频域信息,我们在 FDANet 中开发了频谱增强模块(SEM)。频谱增强模块利用傅立叶变换技术提取能反映图像低层次统计信息的振幅分量。然后,检索网络利用提取的振幅分量,增强对原始空间域中发生相对变化的区域的感知。对多个图像检索基准的实验表明,我们的方法在测试指标上的性能优于其他最先进的哈希算法。
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引用次数: 0
Newton Time-Reassigned Multi-Synchrosqueezing Wavelet Transform 牛顿时间重新分配多同步阙值小波变换
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-06 DOI: 10.1109/LSP.2024.3455990
Wenting Li;Zhuosheng Zhang;Rui Zhang
In this letter, we introduce an accurate group delay estimator and two high-resolution time-frequency analysis methods to characterize fast frequency-varying signals. Firstly, we explore the limitations of time-reassigned synchrosqueezing wavelet transform and its multi-synchrosqueezing case in dealing with fast frequency-varying signals. Secondly, we present Newton group delay estimator based on wavelet transform properties and Newton's method. Based on this, we introduce the Newton time-reassigned synchrosqueezing wavelet transform, which improves the readability of the time-frequency representation, by reassigning the wavelet transform coefficients into the group delay trajectories along the time direction, and further derive its reconstruction formula. Moreover, we propose Newton time-reassigned multi-synchrosqueezing wavelet transform by multiple squeezing operations, which can achieve a more concentrated time-frequency representation and accurate signal reconstruction. Finally, we employ synthetic and real signals to verify the effectiveness of the proposed methods on the time-frequency energy concentration, group delay estimation and signal reconstruction.
在这封信中,我们介绍了一种精确的群延迟估计器和两种高分辨率时频分析方法,以描述快速频变信号的特征。首先,我们探讨了时间重新分配同步小波变换及其多同步小波变换在处理快速频变信号时的局限性。其次,我们提出了基于小波变换特性和牛顿方法的牛顿群延迟估计器。在此基础上,我们引入了牛顿时间重分配同步小波变换,通过将小波变换系数沿时间方向重分配到群延迟轨迹中,提高了时频表示的可读性,并进一步推导出其重构公式。此外,我们还提出了牛顿时间重新分配多同步挤压小波变换,通过多次挤压操作,实现更集中的时频表示和精确的信号重构。最后,我们利用合成信号和真实信号验证了所提方法在时频能量集中、群延迟估计和信号重构方面的有效性。
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引用次数: 0
Matrix Embedding Based Multiple Histograms Modification for Efficient Reversible Data Hiding 基于矩阵嵌入的多重直方图修改实现高效可逆数据隐藏
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-06 DOI: 10.1109/LSP.2024.3455995
Xiang Li;Mengyao Xiao;Xiaolong Li;Shijun Xiang;Yao Zhao
Recently, matrix embedding (ME), a well-known steganographic technique, has been employed in reversible data hiding (RDH) for the first time, improving the performance of single histogram modification (SHM) methods. In this letter, the ME-based RDH strategy is extended from SHM to the more effective multiple histograms modification (MHM) to further improve the reversible embedding performance. The capacity-distortion model is first established in the novel scenario. Then, some theoretical results for payload partition and expansion-bins-determination are given. Finally, based on the derived theoretical investigations, an efficient RDH method with low computational complexity is proposed. Experimental results show that the proposed method can achieve better visual quality compared to some state-of-the-art methods.
最近,矩阵嵌入(ME)这一著名的隐写技术首次被用于可逆数据隐藏(RDH),从而改善了单直方图修改(SHM)方法的性能。在这封信中,基于 ME 的 RDH 策略从 SHM 扩展到了更有效的多直方图修改(MHM),从而进一步提高了可逆嵌入性能。首先在新方案中建立了容量-失真模型。然后,给出了有效载荷分割和扩展箱确定的一些理论结果。最后,基于推导出的理论研究,提出了一种计算复杂度较低的高效 RDH 方法。实验结果表明,与一些最先进的方法相比,所提出的方法可以获得更好的视觉质量。
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引用次数: 0
General Distortion Metric Based Histogram Shifting for Reversible Data Hiding 基于一般失真度标的直方图移动实现可逆数据隐藏
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-06 DOI: 10.1109/LSP.2024.3456005
Xingyuan Liang;Shijun Xiang
In reversible data hiding (RDH) community, researchers often embed bits by shifting prediction-error histogram based on the mean-square error metric (MSEM). This will cause more pixel distortion in the smooth areas. Considering that the human eye is more sensitive to the distortion in the smooth areas, in this letter, we propose a new histogram shifting strategy for RDH by referring to the general distortion metric (GDM). With the GDM, data can be embedded by first modifying those pixels in the texture areas. In both theoretical analysis and experimental testing, we have shown that the use of the proposed GDM-based histogram shifting strategy for RDH can further improve the visual quality of marked images in higher SSIM values by comparing with typical MSEM-based histogram shifting methods.
在可逆数据隐藏(RDH)领域,研究人员通常根据均方误差指标(MSEM),通过移动预测误差直方图来嵌入比特。这会导致光滑区域的像素失真更多。考虑到人眼对平滑区域的失真更为敏感,我们在这封信中提出了一种针对 RDH 的新直方图移动策略,即参照一般失真指标(GDM)。利用 GDM,可以通过首先修改纹理区域的像素来嵌入数据。在理论分析和实验测试中,我们都表明,与典型的基于 MSEM 的直方图移动方法相比,使用所提出的基于 GDM 的 RDH 直方图移动策略可以进一步提高标记图像在较高 SSIM 值下的视觉质量。
{"title":"General Distortion Metric Based Histogram Shifting for Reversible Data Hiding","authors":"Xingyuan Liang;Shijun Xiang","doi":"10.1109/LSP.2024.3456005","DOIUrl":"10.1109/LSP.2024.3456005","url":null,"abstract":"In reversible data hiding (RDH) community, researchers often embed bits by shifting prediction-error histogram based on the mean-square error metric (MSEM). This will cause more pixel distortion in the smooth areas. Considering that the human eye is more sensitive to the distortion in the smooth areas, in this letter, we propose a new histogram shifting strategy for RDH by referring to the general distortion metric (GDM). With the GDM, data can be embedded by first modifying those pixels in the texture areas. In both theoretical analysis and experimental testing, we have shown that the use of the proposed GDM-based histogram shifting strategy for RDH can further improve the visual quality of marked images in higher SSIM values by comparing with typical MSEM-based histogram shifting methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142218764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Sparsity-Based Adaptive Beamforming for Coherent Signals With Polarized Sensor Arrays 基于稀疏性的自适应波束成形,用于偏振传感器阵列的相干信号
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-06 DOI: 10.1109/LSP.2024.3455994
Tianpeng Liu;Yun Cheng;Junpeng Shi;Zhen Liu;Yongxiang Liu
A sparsity-based adaptive beamforming (ABF) method is introduced to effectively process coherent signals with polarized sensor arrays (PSA). This method exploits the spatial sparsity of observed signals by transforming it into row-sparsity within a waveform-polarization composite matrix through data reorganization. This row-sparsity is subsequently cast as an $ell _{2,1}$ norm minimization problem, characterized by a gridless and compact mathematical expression with a Hermitian Toeplitz matrix. Then, a matrix factorization-based gradient descent (GD) algorithm is introduced to effectively resolve this optimization problem. The experimental evaluations demonstrate that the GD algorithm significantly outperforms the MOSEK solver in terms of computational efficiency. Further comparative analysis demonstrates that the proposed method outperforms the existing techniques, especially in contexts of low signal-to-noise ratio (SNR), with a moderate increase in computational runtime.
本文介绍了一种基于稀疏性的自适应波束成形(ABF)方法,可有效处理偏振传感器阵列(PSA)的相干信号。该方法利用观测信号的空间稀疏性,通过数据重组将其转化为波形偏振复合矩阵内的行稀疏性。这种行稀疏性随后被转化为一个$ell _{2,1}$规范最小化问题,该问题的特点是使用赫米特托普利兹矩阵进行无网格和紧凑的数学表达。然后,引入一种基于矩阵因式分解的梯度下降(GD)算法来有效解决这一优化问题。实验评估表明,GD 算法的计算效率明显优于 MOSEK 求解器。进一步的比较分析表明,所提出的方法优于现有技术,尤其是在信噪比(SNR)较低的情况下,但计算运行时间略有增加。
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引用次数: 0
Text-Guided Prototype Generation for Occluded Person Re-Identification 文本引导下的原型生成,用于模糊人物再识别
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-06 DOI: 10.1109/LSP.2024.3456007
Min Jiang;Xinyu Liu;Jun Kong
Occluded person re-identification (ReID) focuses on identifying persons who are partially occluded, especially in multi-camera scenarios. The majority of methods employ the background to make artificial occlusions. However, simple artificial occlusions could not effectively simulate real-world occluded scenarios, due to its lack of semantic information and its limitation in disrupting the model's attention. In this paper, we present the Text-Guided Prototype Generation (TGPG) for occluded person ReID. On the one hand, to fully employ the potential of text as priori information, the Mask Prototype Generation (MPG) strategy is presented to generate the prototypes that could capture attention in the pretrained model, similar to the realistic occlusions. On the other hand, to create a relationship between holistic person features and occluded person features, the Intra-modality Spatial Consistency (ISC) loss is introduced, enhancing the consistency and representativeness of the generated mask prototypes. Comprehensive experiments conducted on the Occluded-Duke and Occluded-ReID datasets confirm our method's superiority over state-of-the-art approaches.
被遮挡人员再识别(ReID)的重点是识别部分被遮挡的人员,尤其是在多摄像头场景中。大多数方法都是利用背景进行人工遮挡。然而,由于缺乏语义信息以及在扰乱模型注意力方面的局限性,简单的人工遮挡无法有效模拟真实世界中的遮挡场景。在本文中,我们提出了文本引导原型生成(TGPG)技术,用于模糊人物 ReID。一方面,为了充分利用文本作为先验信息的潜力,我们提出了掩码原型生成(MPG)策略,以生成可以在预训练模型中吸引注意力的原型,类似于现实中的遮挡物。另一方面,为了在整体人物特征和遮挡人物特征之间建立联系,引入了模态内空间一致性(ISC)损失,以增强生成的遮挡原型的一致性和代表性。在 Occluded-Duke 和 Occluded-ReID 数据集上进行的综合实验证实了我们的方法优于最先进的方法。
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引用次数: 0
Enhancing Inter-Class Separability With High-Order Strangers for Multi-View Clustering 利用高阶陌生人增强多视角聚类的类间可分性
IF 3.2 2区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Pub Date : 2024-09-06 DOI: 10.1109/LSP.2024.3455988
Chundan Liu;Qian Zhang;Yongyong Chen;Junyu Dong;Chong Peng
Multi-view clustering has attracted extensive attention in recent years, which aims at integrating data from different views to improve the clustering performance. In this letter, we propose a novel approach for multi-view clustering. We propose to leverage high-order stranger information of the samples with the aid of Markov random walks to enhance inter-class separability of representation matrix in each view. Then, we seek a direct and intuitive clustering interpretation through view-specific spectral embeddings and cross-view spectral rotation fusion with auto-adjusted weights. Extensive experimental results confirm the effectiveness of our method.
近年来,多视图聚类引起了广泛关注,其目的是整合来自不同视图的数据以提高聚类性能。在这封信中,我们提出了一种新颖的多视图聚类方法。我们建议借助马尔可夫随机游走来利用样本的高阶陌生人信息,以增强每个视图中表示矩阵的类间可分性。然后,我们通过特定视图的光谱嵌入和自动调整权重的跨视图光谱旋转融合,寻求一种直接而直观的聚类解释。广泛的实验结果证实了我们方法的有效性。
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引用次数: 0
期刊
IEEE Signal Processing Letters
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