IEEE Signal Processing Letters最新文献

Domain-Assisted Few-Shot Linguistic Steganalysis in Imbalanced Class Scenarios

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

Dual-Branch Network for No-Reference Super-Resolution Image Quality Assessment

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-21 DOI: 10.1109/LSP.2025.3553432

Tong Tang;Fan Yang;Xinyu Lin;Weisheng Li

引用次数: 0

Modified Price Nonlinear Frequency Modulated Waveform for Improved Performance

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-21 DOI: 10.1109/LSP.2025.3553437

Geon U Kim;Jeong Phill Kim

引用次数: 0

SequenceOut: Boosting CNNs by Freezing Layers

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-20 DOI: 10.1109/LSP.2025.3553430

Shitala Prasad;Rakesh Paul;Mayur Kamat

引用次数: 0

ViLNM: Visual-Language Noise Modeling for Text-to-Image Person Retrieval

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-20 DOI: 10.1109/LSP.2025.3553424

Guolin Xu;Yong Feng;Yanying Chen;Guofan Duan;Mingliang Zhou

{"title":"ViLNM: Visual-Language Noise Modeling for Text-to-Image Person Retrieval","authors":"Guolin Xu;Yong Feng;Yanying Chen;Guofan Duan;Mingliang Zhou","doi":"10.1109/LSP.2025.3553424","DOIUrl":"https://doi.org/10.1109/LSP.2025.3553424","url":null,"abstract":"Text-to-image person retrieval (TPR) focuses on finding a specific person based on the textual description, and most methods implicitly assume the training image-text pairs are correctly aligned. In practice, the image-text pairs exist under-correlated or false-correlated due to the low quality of the images and annotation errors. Meanwhile, remarkable similarities between different person identities may lead to a mismatch between text and image. To tackle the two issues, we present a Visual-Language Noise Modeling (ViLNM) method that successfully captures robust cross-modal associations even with noise. Specifically, we design a Noise Token Aware (NTA) module that eliminates the words in the textual description that do not match the image, utilizing the matched words to establish a more reliable association. Besides, to enhance the recognition ability of the model for different person identities, we propose a Joint Inter and Intra-Modal Contrastive Loss (JII) and Local Aggregation (LA) module to increase the feature differences between different person identities. We conduct comprehensive experiments on three public benchmarks, and ViLNM performs best.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1386-1390"},"PeriodicalIF":3.2,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748913","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

Low Complexity MRC Detection of IRS-Aided Single User MIMO-OTFS

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-20 DOI: 10.1109/LSP.2025.3553428

Sapta Girish Neelam

引用次数: 0

Blind Deconvolution of Graph Signals: Robustness to Graph Perturbations

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-19 DOI: 10.1109/LSP.2025.3553064

Chang Ye;Gonzalo Mateos

引用次数: 0

Factorization-Based Information Reconstruction for Enhancing Missing Modality Robustness

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-18 DOI: 10.1109/LSP.2025.3552435

Chao Wang;Miaolei Zhou;Xiaofei Yu;Junchao Weng;Yong Zhang

{"title":"Factorization-Based Information Reconstruction for Enhancing Missing Modality Robustness","authors":"Chao Wang;Miaolei Zhou;Xiaofei Yu;Junchao Weng;Yong Zhang","doi":"10.1109/LSP.2025.3552435","DOIUrl":"https://doi.org/10.1109/LSP.2025.3552435","url":null,"abstract":"In recent years, Multimodal Sentiment Analysis (MSA) has emerged as a prominent research area, utilizing multiple signals to better understand human sentiment. Previous studies in MSA have primarily concentrated on performing interaction and fusion with complete signals. However, they have overlooked the issue of missing signals, which commonly arise in real-world scenarios due to factors such as occlusion, privacy concerns, and device malfunctions, leading to reduced generalizability. To this end, we propose a Factorization-based Information Reconstruction Framework (FIRF) to mitigate the modality missing problem in the MSA task. Specifically, we propose a fine-grained complementary factorization module that factorizes modality into synergistic, modality-heterogeneous, and noisy representations and design elaborate constraint paradigms for representation learning. Furthermore, we design a distribution calibration self-distillation module that fully recovers the missing semantics by utilizing bidirectional knowledge transfer. Comprehensive experiments on two datasets indicate that FIRF has a significant performance advantage over previous methods with uncertain missing modalities.","PeriodicalId":13154,"journal":{"name":"IEEE Signal Processing Letters","volume":"32 ","pages":"1376-1380"},"PeriodicalIF":3.2,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748819","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

DNGG: Medical Image Lossless Encryption via Deep Network Guided Generative

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-18 DOI: 10.1109/LSP.2025.3552528

Lin Fan;Meng Li;Zhenting Hu;Yuan Hong;Dexing Kong

引用次数: 0

Optimizing the Order of Modes in Tensor Train Decomposition

IF 3.2 2区工程技术

IEEE Signal Processing Letters Pub Date : 2025-03-17 DOI: 10.1109/LSP.2025.3552005

Petr Tichavský;Ondřej Straka

引用次数: 0