Journal of Information Security and Applications最新文献_第3页

A user-centric privacy transformation framework for federated learning in industrial internet of things 面向工业物联网联合学习的以用户为中心的隐私转换框架

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-05-01 Epub Date: 2026-01-23 DOI: 10.1016/j.jisa.2026.104380

Assem Utaliyeva, Yoon-Ho Choi

{"title":"A user-centric privacy transformation framework for federated learning in industrial internet of things","authors":"Assem Utaliyeva, Yoon-Ho Choi","doi":"10.1016/j.jisa.2026.104380","DOIUrl":"10.1016/j.jisa.2026.104380","url":null,"abstract":"<div><div>Privacy preservation is a critical challenge in the Industrial Internet of Things (IIoT), particularly in multi-client environments such as Federated Learning (FL) where industrial stakeholders have different privacy needs. Existing privacy-preserving methods such as Differential Privacy (DP) often enforce uniform constraints. This leads to a suboptimal balance, resulting in either excessive protection that degrades model utility or insufficient protection that leaves sensitive data vulnerable. To address this, we propose a novel user-centric privacy-preserving mechanism that dynamically adapts and customizes privacy protection for each client. Proposed method moves beyond traditional one-size-fits all methods by allowing each IIoT client to define a user-centric profile, specifying sensitive information beyond standard. This flexibility ensures privacy measures aligned with common industrial policies and individual needs while maintaining data usability. To further strengthen protection, we introduce adversary-aware transformations. We formulate this process as a constrained optimization problem to find the minimum privacy budget required to protect from both human and machine adversaries. Experimental results demonstrate that proposed method effectively mitigates privacy risks while preserving model accuracy. This ensures an optimal trade-off between data privacy, compliance and learning efficacy which is crucial for real-world applications.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"98 ","pages":"Article 104380"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Security analysis on a public-key inverted-index keyword search scheme with designated tester 一种指定测试者的公钥反索引关键字搜索方案的安全性分析

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-05-01 Epub Date: 2026-02-13 DOI: 10.1016/j.jisa.2026.104410

Mizuki Hayashi , Keita Emura

{"title":"Security analysis on a public-key inverted-index keyword search scheme with designated tester","authors":"Mizuki Hayashi , Keita Emura","doi":"10.1016/j.jisa.2026.104410","DOIUrl":"10.1016/j.jisa.2026.104410","url":null,"abstract":"<div><div>Gao et al. (IEEE Internet of Things Journal, 2024) proposed a public-key inverted-index keyword search scheme with a designated tester as an extension of public-key encryption with keyword search (PEKS). In their scheme, the server (acting as the tester) holds a secret key and uses it to execute the search algorithm under the designated tester setting. They proved that no information about the keyword is revealed from trapdoors under the decisional Diffie-Hellman (DDH) assumption. However, their construction employs a symmetric pairing, which can effectively serve as a DDH solver. Consequently, the underlying complexity assumption does not hold, and it is expected that keyword information can be extracted from trapdoors. In this paper, we present two keyword guessing attacks against the Gao et al. scheme that reveal keyword information from a trapdoor. The first attack succeeds using only the server’s secret key and the challenge trapdoor, without requiring any additional encryption or trapdoor queries. We note that, in their security model, an adversary is not allowed to obtain the server’s secret key; therefore, our attack lies outside their defined model. Nevertheless, we discuss the role of the server and argue that our attack scenario is reasonable. The second attack does not rely on the server’s secret key but instead exploits the linkability of two trapdoors. In both attacks, the computational complexity is limited to two pairing operations, making them practical in terms of computational cost.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"98 ","pages":"Article 104410"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dual-driven synergy of blockchain and federated learning for trustworthy medical data sharing in internet of medical things 区块链与联邦学习双驱动协同，实现医疗物联网医疗数据可信共享

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-03-01 Epub Date: 2025-12-22 DOI: 10.1016/j.jisa.2025.104348

Chenquan Gan , Xin Tan , Qingyi Zhu , Akanksha Saini , Deepak Kumar Jain , Abebe Diro

{"title":"Dual-driven synergy of blockchain and federated learning for trustworthy medical data sharing in internet of medical things","authors":"Chenquan Gan , Xin Tan , Qingyi Zhu , Akanksha Saini , Deepak Kumar Jain , Abebe Diro","doi":"10.1016/j.jisa.2025.104348","DOIUrl":"10.1016/j.jisa.2025.104348","url":null,"abstract":"<div><div>In the Internet of Medical Things (IoMT) field, data sharing is crucial for enhancing the quality and efficiency of diagnosis and treatment. However, due to data privacy and security concerns, data sharing among medical institutions is hindered, presenting challenges in integration and privacy protection. To address these issues, we propose a trustworthy medical data sharing method based on the dual-driven synergy of blockchain and federated learning. Different from previous work, it can resist reasoning, poisoning, and collusion attacks because it covers a more comprehensive discussion on privacy, security, and reputation incentives. This method includes: 1) Privacy protection mechanism: integrating random response and differential privacy technology to resist inference attacks, protect model privacy, and reduce the impact of noise on model performance; 2) Dual-quality threshold aggregation mechanism: Precisely filter out malicious and low-quality nodes through quality thresholds, introduce contribution values including node participation to assign aggregation weights to nodes, improve model performance and resist poisoning attacks; 3) Reputation assessment and incentive mechanism: Calculate the reputation value based on the historical performance of nodes, and design a reputation consensus to encourage honest participation of nodes, punish malicious behavior, and at the same time reduce the entry of malicious nodes into the committee to resist collusion attacks. Finally, we compare our method with state-of-the-art approaches on real-world medical image datasets OrganMNIST_A and BloodMNIST. The results demonstrate that our method achieves superior performance in both Accuracy and F1 Score metrics.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"97 ","pages":"Article 104348"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A large-capacity and robust screen-shooting resilient image watermarking based on attention-enhanced invertible neural network 基于注意增强可逆神经网络的大容量鲁棒截屏弹性图像水印

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-03-01 Epub Date: 2025-12-23 DOI: 10.1016/j.jisa.2025.104346

Jingyi Zhang , Zhenzhen Zhang , Zichen Li , Bo Gao , Jinfeng Kou

{"title":"A large-capacity and robust screen-shooting resilient image watermarking based on attention-enhanced invertible neural network","authors":"Jingyi Zhang , Zhenzhen Zhang , Zichen Li , Bo Gao , Jinfeng Kou","doi":"10.1016/j.jisa.2025.104346","DOIUrl":"10.1016/j.jisa.2025.104346","url":null,"abstract":"<div><div>To address the growing issue of illegal screen-shooting behavior, screen-shooting resilient watermarking has become an increasingly important area of research in copyright protection. This technology aims to resist the complex distortions caused by screen-shooting and accurately extract the watermark from the watermarked images. However, existing screen-shooting resilient watermarking schemes often overlook the importance of the watermark capacity, failing to strike a balance among watermark capacity, robustness and visual quality, which hinders their practical application. To tackle these problems, we propose a large capacity and robust image watermarking scheme based on Invertible Neural Network (INN) in this paper. The proposed method combines INN with a channel-spatial attention mechanism to increase watermark capacity and enhance visual quality. Additionally, a frequency domain feature enhancement (FDFE) module is introduced to assist watermark extraction, improving the model’s robustness in real-world screen-shooting scenarios. The experimental results show that when the embedding capacity increases to 400 bits, the proposed algorithm exhibits stronger resistance to screen-shooting attacks compared to the state-of-the-art (SoTA) algorithm with an embedding capacity of 30 bits. The watermark extraction accuracy of the proposed method remains above 99 % under all shooting angles and distances, with an average extraction accuracy of 99.81 %, significantly outperforming the compared SoTA methods.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"97 ","pages":"Article 104346"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145841426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PVO-based reversible data hiding with flexible strip moving using classification-based adaptive prediction guided by CNN 基于分类自适应预测的基于pvo的柔性条带移动可逆数据隐藏

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-03-01 Epub Date: 2026-01-10 DOI: 10.1016/j.jisa.2025.104368

Guojun Fan , Chen Zuo , Lei Lu , Zhihai Yang , Juan Zhao , Zhibin Pan

{"title":"PVO-based reversible data hiding with flexible strip moving using classification-based adaptive prediction guided by CNN","authors":"Guojun Fan , Chen Zuo , Lei Lu , Zhihai Yang , Juan Zhao , Zhibin Pan","doi":"10.1016/j.jisa.2025.104368","DOIUrl":"10.1016/j.jisa.2025.104368","url":null,"abstract":"<div><div>Pixel-value-ordering (PVO) based reversible data hiding methods have been widely investigated by researchers because of their effective trade-off between embedding capacity and stego image fidelity. Conventional PVO-based methods divide the cover image into non-overlapped blocks and embed secret data into their maximum/minimum pixels. However, the embedding capacity and overall performance is limited by block amount. Although we previously introduced an approach that overlaps blocks to enlarge the embedding capacity, the overlapping rate is still limited because the two-dimensional structure of the cover image, and approach rules are complicated. To address these problems, in this work, we propose to convert the cover image into a one-dimensional array. With the special one-dimensional characteristics, the steps to obtain overlapped sequences are simplified and the overlapping rate is also increased. Moreover, to generate a better distributed one-dimensional array, we also propose a deep neural network (DNN) based prediction method. Different from directly using DNN for prediction in existing methods, we use DNN to evaluate the suitability of pixels for using different classical predictors. Two thresholds are then determined to classify the pixels, allowing predictors to be used adaptively. Experiments demonstrate that the proposed prediction method achieves high prediction accuracy, and the proposed flexible strip moving mode also outstands state-of-the-art methods in overall embedding performance.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"97 ","pages":"Article 104368"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Blockchain-enabled heterogeneous communication protocol with identifiable abort in federated learning for artificial intelligence of things 支持区块链的异构通信协议，在人工智能事物的联邦学习中具有可识别的中止

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-03-01 Epub Date: 2026-01-12 DOI: 10.1016/j.jisa.2025.104370

Hu Xiong , Qiyong Xian , Kuo-Hui Yeh

{"title":"Blockchain-enabled heterogeneous communication protocol with identifiable abort in federated learning for artificial intelligence of things","authors":"Hu Xiong , Qiyong Xian , Kuo-Hui Yeh","doi":"10.1016/j.jisa.2025.104370","DOIUrl":"10.1016/j.jisa.2025.104370","url":null,"abstract":"<div><div>The convergence of the Internet of Things (IoT) and artificial intelligence (AI) has led to the emergence of the Artificial Intelligence of Things (AIoT), creating smart devices that learn from data and make autonomous decisions. Local data produced by AIoT devices can be used to create a universal model through federated learning (FL). However, ensuring secure communication in FL among devices from public key infrastructure (PKI) and certificateless (CL) cryptosystems, is a major challenge due to untrusted channels and privacy concerns. To tackle these challenges, this article presents a novel blockchain-enabled heterogeneous communication protocol with identifiable abort (BEHCPwIA), which helps participants negotiate a group public key and their corresponding private keys in one communication round. The proposed protocol not only ensures indistinguishability and semantic security, but also applies an identifiable abort mechanism to enhance communication transparency and accountability. With the support of this BEHCPwIA protocol, the FL could foster greater collaboration and trust among participants in the expanding AIoT landscape. Experimental simulations indicate that the proposed protocol not only maintains high security but also demonstrates high efficiency compared to existing solutions.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"97 ","pages":"Article 104370"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An imperceptible dynamic anticipated backdoor attack in federated learning 联邦学习中不可察觉的动态预期后门攻击

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.jisa.2025.104317

Yingqiang Xie , Wei Ren , Tianqing Zhu , Lianchong Zhang

{"title":"An imperceptible dynamic anticipated backdoor attack in federated learning","authors":"Yingqiang Xie , Wei Ren , Tianqing Zhu , Lianchong Zhang","doi":"10.1016/j.jisa.2025.104317","DOIUrl":"10.1016/j.jisa.2025.104317","url":null,"abstract":"<div><div>Federated Learning is a distributed machine learning paradigm that allows multiple clients to collaboratively train a global model while preserving privacy by avoiding the exchange of raw data. However, its distributed nature makes it vulnerable to backdoor attacks, which threaten the integrity and security of the model. Existing attacks often rely on fixed triggers or optimizations of the local model, failing to adapt to dynamic updates of the global model. We propose a new and effective attack named IDABA (Imperceptible Dynamic Anticipated Backdoor Attack), a novel dynamic backdoor attack method for FL, addressing these limitations by ensuring visual imperceptibility and persistence. IDABA generates visually imperceptible poisoned samples and employs Model-Contrastive Loss (MOON) to maintain similarity with the global model. It also predicts future global model states to optimize trigger effectiveness. Experiments on CIFAR10, MNIST, GTSRB, and TinyImageNet show that IDABA achieves higher Attack Success Rates (ASR) while maintaining model accuracy. It demonstrates strong adaptability against defense mechanisms such as Krum and Multi-Krum. GradCam analysis and image quality metrics confirm the visual stealthiness of IDABA’s backdoor samples.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"97 ","pages":"Article 104317"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

V2G-RingChain: Blockchain-enabled linkable ring signature scheme for vehicle-to-grid security and privacy V2G-RingChain：支持区块链的可链接环签名方案，用于车辆到电网的安全和隐私

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-03-01 Epub Date: 2026-01-10 DOI: 10.1016/j.jisa.2026.104371

Zhenqi Wang , Peng Yang , Dongmei Yang , Xiaoyan Liu , Hongmei Pei

{"title":"V2G-RingChain: Blockchain-enabled linkable ring signature scheme for vehicle-to-grid security and privacy","authors":"Zhenqi Wang , Peng Yang , Dongmei Yang , Xiaoyan Liu , Hongmei Pei","doi":"10.1016/j.jisa.2026.104371","DOIUrl":"10.1016/j.jisa.2026.104371","url":null,"abstract":"<div><div>Vehicle-to-Grid (V2G) technology enables electric vehicles to obtain power from the smart grid and supply electricity back during peak hours, and it is crucial for enhancing energy efficiency and reducing carbon emissions. However, attackers may steal sensitive information from electric vehicles through frequent charging and discharging transactions, or even forge such transactions to breach V2G security. Existing schemes mainly adopt ring signatures to achieve forgery resistance and privacy preservation for charging and discharging transactions, and extend them into multi-layer ring signature structures to support multi-input and multi-output transactions. Nevertheless, such schemes either cause the signature size and verification complexity to grow with the transaction scale, or rely on logarithmic compression proofs that introduce substantial system overhead, thereby limiting their practical applicability. To address this issue, we propose an efficient blockchain-enabled linkable ring signature scheme named V2G-RingChain. Specifically, we design an innovative multi-layer linkable ring signature protocol, which reconstructs the key images structure to reduce the computation time required for signing and combines Sum Arguments of Knowledge to control the signature size at a logarithmic level. Moreover, we construct a blockchain-enabled ring transaction mechanism to decouple anonymous electricity payments, blockchain verification, and bill extraction, which relies on Pedersen Commitments and Back-Maxwell RangeProof for hidden account amounts and non-negative judgment. Finally, we conduct a comprehensive security analysis demonstrating that our scheme ensures security and privacy. We conducted extensive experiments, and the results show that V2G-RingChain significantly reduces signature generation cost, verification cost, and communication cost compared with state-of-the-art schemes.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"97 ","pages":"Article 104371"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A class-imbalance-aware intrusion detection system based on spatiotemporal graph neural networks for software-defined vehicles 基于时空图神经网络的软件定义车辆类不平衡感知入侵检测系统

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.jisa.2025.104340

Sishan Wang , Youqun Zhao , Xin Fu , Huachao Si , Wentao Wang , Lei Xue

{"title":"A class-imbalance-aware intrusion detection system based on spatiotemporal graph neural networks for software-defined vehicles","authors":"Sishan Wang , Youqun Zhao , Xin Fu , Huachao Si , Wentao Wang , Lei Xue","doi":"10.1016/j.jisa.2025.104340","DOIUrl":"10.1016/j.jisa.2025.104340","url":null,"abstract":"<div><div>Advanced driving systems (ADS) and vehicle-to-everything (V2X) technologies are accelerating the shift to software-defined vehicles (SDVs), which have dramatically increased the demand for connectivity and bandwidth of in-vehicle networks (IVNs), e.g., automotive Ethernet. The SOME/IP (Scalable service-Oriented MiddlewarE over IP) protocol, a middleware standard for automotive Ethernet, presents unique security challenges: the lack of security mechanisms and its dynamic session behaviors render traditional rule-based Intrusion Detection Systems (IDSs) ineffective. To address these challenges, we propose GATransformer, a novel hybrid Graph Attention Network (GAT) with Transformer architecture that can learn spatial-temporal dependencies for SOME/IP-based IVNs in the class imbalance scenario. A comprehensive evaluation on a SOME/IP dataset built from a real-world Connected and Autonomous Vehicle (CAV) indicates that the GATransformer enhances robustness in a class imbalance scenario and significantly outperforms conventional Deep Learning (DL) architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), standalone Transformers, and baseline GAT. The proposed model achieves an F1-score of 0.9999 with 0.004 ms inference latency on server-grade hardware (NVIDIA RTX 3090) and maintains robust performance (F1-score: 0.9983) with sub-millisecond latency (0.151 ms) when deployed on an automotive-grade embedded platform (NVIDIA Jetson Orin Nano). These results validate the possibility of deploying the hybrid Graph Neural Networks (GNNs) for real-time automotive intrusion detection, representing a significant advancement toward securing next-generation service-oriented architectures (SOA) against evolving cyber threats.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"97 ","pages":"Article 104340"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enabling diverse styles coverless image steganography with two-stage latent transformation and diffusion model 利用两级潜伏变换和扩散模型实现多种风格的无盖图像隐写

IF 3.7 2区计算机科学

Journal of Information Security and Applications Pub Date : 2026-03-01 Epub Date: 2025-12-11 DOI: 10.1016/j.jisa.2025.104328

Jiangtao Guo, Buwei Tian, Junyong Jiang, Lu Dong

{"title":"Enabling diverse styles coverless image steganography with two-stage latent transformation and diffusion model","authors":"Jiangtao Guo, Buwei Tian, Junyong Jiang, Lu Dong","doi":"10.1016/j.jisa.2025.104328","DOIUrl":"10.1016/j.jisa.2025.104328","url":null,"abstract":"<div><div>Coverless image steganography (CIS) aims to map secret images into container images without modifying the original images for concealment purposes. However, most current CIS methods rely heavily on deep learning models, which require extensive training datasets and demonstrate limited robustness to variations in image styles. Particularly when applied to images with substantial stylistic variations, these methods often produce unsatisfactory steganographic results, leading to significant degradation in the quality of steganographic image (stego-image). Furthermore, existing diffusion model-based CIS approaches can only achieve effective concealment between images with similar styles, thereby limiting the diversity of application scenarios. To address these limitations, we propose a training-free CIS method based on the diffusion model (DStyleStego), which does not rely on the traditional training, and can effectively handle different styles of images, guaranteeing the image quality and the security of steganographic information. Specifically, we design a two-stage latent transformation method to improve the security and flexibility of image steganography. In addition, we introduce a detail compensation function to recover detail information lost during the diffusion process to improve the quality and fidelity of the generated images. Extensive experimental results demonstrate that DStyleStego achieves efficient and stable steganography across diverse image datasets (Stego260 and UniStega) while exhibiting significant advantages in terms of image quality preservation.</div></div>","PeriodicalId":48638,"journal":{"name":"Journal of Information Security and Applications","volume":"97 ","pages":"Article 104328"},"PeriodicalIF":3.7,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0