IEEE Signal Processing Letters最新文献_第3页

Transient Segment Detection Based on Energy Spectrum Coefficient Entropy Posterior Probability Density 基于能谱系数熵后验概率密度的暂态片段检测

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-27 DOI: 10.1109/LSP.2025.3555421

Jingbo Zhang;Rongwen Lin;Qingshuo Liu

{"title":"Transient Segment Detection Based on Energy Spectrum Coefficient Entropy Posterior Probability Density","authors":"Jingbo Zhang;Rongwen Lin;Qingshuo Liu","doi":"10.1109/LSP.2025.3555421","DOIUrl":"https://doi.org/10.1109/LSP.2025.3555421","url":null,"abstract":"In wireless communication devices, the power envelope during the transient segment of energy-limited pulse signals effectively captures the physical characteristics of the radio frequency circuit. This unique signature can serve as a radio frequency fingerprinting, enhancing the security of the wireless communication system. A key issue in this context is accurately detecting transient signals. The existing detection algorithms can only detect the start time of transient segments, making them unable to characterize the complete transient segments. This study proposes a transient segment detection algorithm based on energy spectral coefficient entropy posterior probability density (ESCE-PPD), which can simultaneously estimate the start and end time of transient segments without relying on prior information. The effectiveness of the proposed algorithm is verified using an open source Bluetooth dataset, and its performance is compared with existing algorithms. The results demonstrate that the ESCE-PPD algorithm adds the capability to detect the end time of transient segments without increasing computational complexity and reducing anti-noise performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1505-1509"},"PeriodicalIF":3.2,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830478","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

Alternating Offer-Based Payment Allocation for Privacy Non-Disclosure in Federated Learning 联邦学习中基于交替出价的隐私保密支付分配

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-27 DOI: 10.1109/LSP.2025.3555386

Suyeon Jin;Chaeyeon Cha;Hyunggon Park

引用次数: 0

Watermark Removal Attack Against Text-to-Image Generative Model Watermarking 针对文本到图像生成模型水印的水印去除攻击

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-26 DOI: 10.1109/LSP.2025.3554514

Zihan Yuan;Li Li;Zichi Wang;Jingyuan Jiang;Xinpeng Zhang

{"title":"Watermark Removal Attack Against Text-to-Image Generative Model Watermarking","authors":"Zihan Yuan;Li Li;Zichi Wang;Jingyuan Jiang;Xinpeng Zhang","doi":"10.1109/LSP.2025.3554514","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554514","url":null,"abstract":"The artist's style can be quickly imitated by fine-tuning a text-to-image model using artist's artworks, which raises serious copyright concerns. Scholars have proposed many watermarking methods to protect the artists' copyright. To evaluate the security and enhance the performance of existing watermarking, this paper proposes a watermark removal attack for text-to-image generative model watermarking for the first time. This attack aims to invalidate watermarking designed to detect art theft mimicry in text-to-image models. In this method, a watermark recognition network and a watermark removal network are designed. The watermark recognition network identifies whether an artwork contains watermark, and the watermark removal network is used to remove it. Consequently, text-to-image models fine-tuned with watermark-removed artworks can reproduce an artist's style while evading watermark detection. This makes the copyright authentication of artworks ineffective. Experiments show that the proposed attack can effectively remove watermarks, with watermark extraction accuracy dropping below 48.64%. Additionally, the images after watermark removal retain high similarity to the original images, with PSNR exceeding 27.96 and SSIM exceeding 0.92.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1470-1474"},"PeriodicalIF":3.2,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809027","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

Exploring Non-Matching Multiple References for Speech Quality Assessment 语音质量评价的非匹配多参考文献探索

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-26 DOI: 10.1109/LSP.2025.3555190

Bao Thang Ta;Nhat Minh Le;Huynh Thi Thanh Binh;Van Hai Do

引用次数: 0

Multi-IRS-Aided Localization for Next-Generation Wireless Networks in Fading Environments 衰落环境下下一代无线网络的多irs辅助定位

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-25 DOI: 10.1109/LSP.2025.3554038

Nasir Saeed

{"title":"Multi-IRS-Aided Localization for Next-Generation Wireless Networks in Fading Environments","authors":"Nasir Saeed","doi":"10.1109/LSP.2025.3554038","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554038","url":null,"abstract":"The growing demand for location-based services (LBS) in complex environments has increased the need for precise and reliable user localization techniques. Traditional methods often face limitations in scenarios with few access points (APs) and non-line-of-sight (NLOS) propagation, resulting in reduced accuracy. This paper presents a novel localization framework that leverages multiple Intelligent Reflecting Surfaces (IRS) to address these challenges and improve positioning accuracy in constrained conditions. The proposed method employs multiple IRSs to enhance signal propagation, mitigating the effects of NLOS conditions and improving signal quality. A Maximum Likelihood Estimation (MLE) algorithm is used to estimate user positions, while the Cramér-Rao Lower Bound (CRLB) is derived to benchmark the theoretical accuracy. By utilizing the reconfigurable capabilities of IRSs, the system dynamically adjusts wireless channels to optimize localization performance. Performance evaluations under practical fading conditions demonstrate significant improvements in accuracy compared to traditional methods. The results highlight the effectiveness and robustness of the proposed framework in diverse environments, showcasing the potential of IRS technology for advanced localization applications.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1460-1464"},"PeriodicalIF":3.2,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808973","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

NADH: A NTRU-Based Adaptive Data Hiding Scheme for Underwater Acoustic Sensor Networks NADH：一种基于ntrus的水声传感器网络自适应数据隐藏方案

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3554414

Ming Xu;Tongtong Guo

引用次数: 0

Cooperative Vehicle Tracking in VANET Using a Distributed Improved Cubature Kalman Filter 基于分布式改进Cubature Kalman滤波的VANET协同车辆跟踪

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3553788

Xiaomei Qu;Tao Liu;Lei Mu;Wenrong Tan;Huanyan Jian

引用次数: 0

Generating Image Counterfactuals in Deep Learning Models Without the Aid of Generative Models 在没有生成模型的帮助下，深度学习模型中生成图像反事实

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3554511

Ao Xu;Zihao Li;Yukai Zhang;Tieru Wu

{"title":"Generating Image Counterfactuals in Deep Learning Models Without the Aid of Generative Models","authors":"Ao Xu;Zihao Li;Yukai Zhang;Tieru Wu","doi":"10.1109/LSP.2025.3554511","DOIUrl":"https://doi.org/10.1109/LSP.2025.3554511","url":null,"abstract":"With the rapid development of artificial intelligence, particularly the rise of deep learning, the importance of Explainable Artificial Intelligence has become increasingly prominent. Among its key techniques, counterfactual explanation plays a crucial role in understanding the decision-making mechanisms of opaque models. However, the high dimensionality and complex feature patterns of image data pose significant challenges for the task of generating counterfactuals for images. Existing literature has proposed various algorithms based on different assumptions, many of which rely on the existence of appropriate generative models. Some of these assumptions, particularly the assumption regarding the existence of generative models, may be overly stringent. To address this issue, this letter introduces a novel assumption-free image counterfactual generation algorithm, DFO-S, based on Score Matching and gradient-free optimization techniques. The proposed method achieves high-quality counterfactual generation without relying on generative models. Through extensive empirical analysis, we demonstrate the significant superiority of our method in terms of performance.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1495-1499"},"PeriodicalIF":3.2,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830479","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

An Optimal Hierarchical Arithmetic Average Fusion of GM-PHD Filters GM-PHD滤波器的最优层次算法平均融合

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-24 DOI: 10.1109/LSP.2025.3554142

Xue Yu;Feng Xi-An

引用次数: 0

Domain-Assisted Few-Shot Linguistic Steganalysis in Imbalanced Class Scenarios 非平衡类场景下的域辅助少射语言隐写分析

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-21 DOI: 10.1109/LSP.2025.3553427

Qingying Niu;Zhen Yang;Yufei Luo;Jiangrui Zhao;Yuwen Jiang

{"title":"Domain-Assisted Few-Shot Linguistic Steganalysis in Imbalanced Class Scenarios","authors":"Qingying Niu;Zhen Yang;Yufei Luo;Jiangrui Zhao;Yuwen Jiang","doi":"10.1109/LSP.2025.3553427","DOIUrl":"https://doi.org/10.1109/LSP.2025.3553427","url":null,"abstract":"Linguistic steganalysis aims to distinguish stego text from cover text. However, most existing methods heavily rely on a large number of stego text samples for training. In real-world scenarios, the cover text is far more abundant than the stego text, making it extremely difficult to obtain sufficient stego text for training. Furthermore, the scarcity of stego text also increases the difficulty of detection, posing greater challenges for steganalysis. In contrast, cover text is relatively easier to obtain in real-world scenarios, but current methods fail to fully utilize this resource. In this paper, we propose a Domain-Assisted Few-shot linguistic steganalysis method called DAF-Stega. To make full use of the cover text, we incorporate cover texts from multiple domains to assist in training. To address the scarcity of stego texts, we perform few-shot steganalysis based on a small amount of stego text and employ dynamic decision-making to generate pseudo-labels for self-training, enhancing model performance. Experimental results show that in few-shot learning scenarios, DAF-Stega effectively addresses the steganalysis problem under uncertain stego text proportions and outperforms existing methods.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1391-1395"},"PeriodicalIF":3.2,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748911","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