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StegMamba: Distortion-Free Immune-Cover for Multi-Image Steganography With State Space Model

IEEE Transactions on Circuits and Systems for Video technology 2025年第5期35卷 4576-4591页

作者： Luo, Ting Zhou, Yuhang He, Zhouyan Jiang, Gangyi Xu, Haiyong Qi, Shuren Zhang, Yushu Ningbo University College of Science and Technology Ningbo315212 China Ningbo University Faculty of Information Science and Engineering Ningbo315211 China The Chinese University of Hong Kong Department of Mathematics Hong Kong Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China

Multi-image steganography ensures privacy protection while avoiding suspicion from third parties by embedding multiple secret images within a cover image. However, existing multi-image steganographic methods fail to model global spatial correlations to reduce image damage at the low computation cost. Moreover, they do not account for the anti-distortion capability of the cover image, which is crucial for achieving imperceptible and ensuring security. To overcome these limitations, we propose StegMamba, a distortion-free immune-cover for multi-image steganography architecture with a state space model. Specifically, we first explore the potential of the linear computational cost model Mamba for data hiding tasks through a steganography Mamba block (SMB), whose efficiency makes it suitable for real-time applications. Subsequently, considering that images with distortion resistance reduce embedding damage, the original cover image is reconstructed through immune-cover construction module (ICCM) and associated with the steganography task. Moreover, well-coupled features facilitate fusion, and thus a wavelet-based interaction module (WIM) is designed for effective communication between the immune-cover and the secret images. Compared with the state-of-the-art global attention-based methods, the proposed StegMamba obtains PSNR gains of 3.30 dB, 1.37 dB, and 1.92 dB for the stego image, and two secret recovery images, respectively, and the reduction of 2.87% in detection accuracy for anti-steganalysis. © 1991-2012 IEEE.

关键词： Cost reduction

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Prioritization and meta-heuristic approach for efficient congestion control in vehicular ad hoc networks (VANETs)

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International Journal of information technology (Singapore) 2025年 1-9页

作者： Sharma, Swati Khanum, Saba Madhumala, R.B. Maroju, Abhilash Aggarwal, Saurabh Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab India Department of Computer Science and Engineering MSIT Janakpuri Delhi India Department of Computer Science and Engineering Dayanand Sagar Academy of Technology and Management (DSATM) Bengaluru India Department of Computer Science and Engineering University of Cumberlands Williamsburg United States Department of Information Technology University of the Cumberlands Williamsburg United States

Vehicular ad hoc networks (VANETs) have emerged as a key area of interest in the research community due to their wide range of applications. As the number of vehicles increases, VANETs encounter challenges with access control, which affects communication between the source vehicle and other vehicles. To overcome this issue, the prioritization and meta-heuristic-based congestion control (PMCC) scheme is proposed in the given paper. It addresses this congestion by prioritizing messages and dynamically adjusting the transmission rate of safety messages. In the prioritization phase, message priority is determined using both dynamic and static factors. Then, to control channel load within VANETs, the PMCC scheme employs a Tabu Search optimization (TSO) algorithm to adjust the transmission rate of beacon messages. Comparative analysis of the PMCC scheme with existing schemes such as urban vehicular broadCAST (UV-CAST), distributed-fair power adjustment for the vehicular environment (D-FPAV), and loss differentiation rate adaptation (LORA) is conducted based on metrics including reception probability, average delay, and vehicle coverage ratio. The results show the superior performance of the proposed PMMC scheme in all aspects. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： Decentralized congestion control (DCC) Distributed-fair power adjustment for the vehicular environment (D-FPAV) Loss differentiation rate adaptation (LORA) PMCC TSO Urban vehicular broadCAST (UV-CAST) VANETs

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Evaluation of jet energy determination in heavy-ion collisions using Machine Learning 2

Evaluation of jet energy determination in heavy-ion collisio...

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2nd International Conference on Big Data, Computational Intelligence, and Applications, BDCIA 2024

作者： Zhao, Yangguang Wang, Yuhui Zhou, Yujie Bao, Qihan College of Computer Science and Technology Qingdao University Qingdao266071 China College of Food Science Sichuan Agriculture University Chengdu610000 China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781510689053

In high-energy physics experiments, the precise reconstruction of the transverse momentum of jets is challenging. The primary obstacle is the interference caused by background noise particles overlapping with the jet and the limitations of the sensors currently being used. A traditional noise reduction method is area-based subtraction, which calculates the mean value of certain events. In previous studies, some noise reduction methods utilizing machine learning techniques have been proposed. In this study, we evaluate the performance of various machine learning models in reconstructing the jet transverse momentum in simulated heavy-ion events, based on Pythia events embedded in a thermal background distribution. The performance of the models at the particle level is compared to that using pseudo-towers of various resolutions in eta, phi to evaluate the impact of the sensor performance limitations. The results demonstrate that as the resolution decreases, the performance of the machine learning models also degrades, but still performs better than the traditional method. This provides a reference for the application of machine learning methods. © 2025 SPIE.

关键词： Heavy ions

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Privacy-Preserving Keyword Search with Access Control for Secret Sharing-Based Data Outsourcing

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IEEE Access 2025年 13卷 73625-73651页

作者： Kamal, Ahmad Akmal Aminuddin Mohd Okada, Masamune Fujisawa, Masaya Tokyo University of Science Department of Information and Computer Technology 125 Tokyo85858 Japan Tokyo University of Science Graduate School of Engineering Tokyo125-8585 Japan

Searchable encryption, also known as secure search, is a technology that enables search operations on encrypted data while maintaining its confidentiality. Extensive research has been conducted on searchable encryption utilizing public-key and symmetric encryptions. However, public-key encryption involves significant computational expenses and is deemed inefficient for querying large databases, particularly in cloud settings. Hence, our attention is on searchable encryption using a secret sharing scheme (also known as searchable secret sharing), which is recognized for its minimal computational complexity. A secret sharing scheme is a method to transform a secret input into several distinct values known as shares. Kamal et al. introduced a simple searchable encryption utilizing secret sharing schemes, yet did not include user access control capabilities. In this study, we introduce a secure search method with user access control employing a secure computation based on a (k, n) threshold secret sharing scheme, where every data stored in the cloud has an owner, and owners grant access to users at their *** assume a client-server model to perform a secure computation between the owner, the player, and the n ≥ k cloud servers. Furthermore, we provide an in-depth analysis of the security aspects of our proposed distribution, query generation, and search processes, demonstrating that our approach is resilient against an honest-but-curious adversary with information from up to k−1 computing servers. Moreover, we also include an improved and efficient method by using a (n, n) additive secret sharing scheme when assuming n = k computing servers. We provide discussion comparing both our proposed methods in terms of their computation and communication costs. Finally, we also include a detailed performance analysis using Python, including a comparison with conventional secret sharing-based methods. © 2013 IEEE.

关键词： Cryptography Cloud computing Encryption Servers Access control Keyword search Data privacy Outsourcing Indium tin oxide Homomorphic encryption

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A Lightweight Convolutional Neural Network with Squeeze and Excitation Module for Security Authentication Using Wireless Channel

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computers, Materials & Continua 2025年第5期83卷 2025-2040页

作者： Xiaoying Qiu Xiaoyu Ma Guangxu Zhao Jinwei Yu Wenbao Jiang Zhaozhong Guo Maozhi Xu College of Computer Science Beijing Information Science and Technology UniversityBeijing100192China School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing100876China School of Mathematical Sciences Peking UniversityBeijing100871China

Physical layer authentication(PLA)in the context of the Internet of Things(IoT)has gained significant *** with traditional encryption and blockchain technologies,PLA provides a more computationally efficient alternative to exploiting the properties of the wireless medium *** existing PLA solutions rely on static mechanisms,which are insufficient to address the authentication challenges in fifth generation(5G)and beyond wireless ***,with the massive increase in mobile device access,the communication security of the IoT is vulnerable to spoofing *** overcome the above challenges,this paper proposes a lightweight deep convolutional neural network(CNN)equipped with squeeze and excitation module(SE module)in dynamic wireless environments,namely *** be more specific,a convolution factorization is developed to reduce the complexity of PLA models based on deep ***,an SE module is designed in the deep CNN to enhance useful features andmaximize authentication *** with the existing solutions,the proposed SE-ConvNet enabled PLA scheme performs excellently in mobile and time-varying wireless environments while maintaining lower computational complexity.

关键词： Physical layer authentication blockchain squeeze and excitation module computational cost mobile scenario

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Integrating Edge Intelligence with Blockchain-Driven Secured IoT Healthcare Optimization Model

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computers, Materials & Continua 2025年第5期83卷 1973-1986页

作者： Khulud Salem Alshudukhi Mamoona Humayun Ghadah Naif Alwakid Department of Computer Science College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computing School of Arts Humanities and Social SciencesUniversity of RoehamptonLondonSW155PHUK

The Internet ofThings(IoT)and edge computing have substantially contributed to the development and growth of smart *** handled time-constrained services and mobile devices to capture the observing environment for surveillance *** systems are composed of wireless cameras,digital devices,and tiny sensors to facilitate the operations of crucial healthcare ***,many interactive applications have been proposed,including integrating intelligent systems to handle data processing and enable dynamic communication functionalities for crucial IoT ***,most solutions lack optimizing relayingmethods and impose excessive overheads for maintaining devices’***,data integrity and trust are another vital consideration for nextgeneration *** research proposed a load-balanced trusted surveillance routing model with collaborative decisions at network edges to enhance energymanagement and resource *** leverages graph-based optimization to enable reliable analysis of decision-making ***,mobile devices integratewith the proposed model to sustain trusted routes with lightweight privacy-preserving and *** proposed model analyzed its performance results in a simulation-based environment and illustrated an exceptional improvement in packet loss ratio,energy consumption,detection anomaly,and blockchain overhead than related solutions.

关键词： Smart cities load balancing blockchain health systems edge computing

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Escape Routing Using Deep Reinforcement Learning

Escape Routing Using Deep Reinforcement Learning

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2025 International Workshop on Advanced Imaging technology, IWAIT 2025

作者： Cheng, Hsu Fu Cheng, Dun Wei Department of Computer Science and Information Engineering National Yunlin University of Science & Technology Yunlin Taiwan

ISBN: (数字)9781510688131

ISBN: (纸本)9781510688124

Escape routing is a critical task in the design of printed circuit boards (PCB) and integrated circuits (IC), aiming to establish effective connections among multiple pin points while avoiding path overlaps and interference from *** paper proposes a Deep Reinforcement Learning (DRL)-based solution utilizing a Deep Q-Network (DQN) to address the escape routing *** problem is modeled as a Markov Decision Process (MDP), and the agent learns effective routing strategies through interactions with the *** were conducted in a 25×26 simulated grid environment, testing scenarios with 30, 60, and 90 pin *** study highlights the potential of Deep Reinforcement Learning in solving escape routing problems, offering a novel approach to addressing routing challenges in PCB design. © 2024 SPIE.

关键词： Deep reinforcement learning

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Comparative Analysis of Brain Tumor Classification Using Transfer Learning 3rd

Comparative Analysis of Brain Tumor Classification Using Tra...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Al-Sultan, Noorah Aljuaid, Hanan Department of Computer Science College of Computer and Information Sciences Princess Noura University Riyadh Saudi Arabia

ISBN: (纸本)9783031876462

Accurate brain tumor classification is crucial for precise diagnosis and effective treatment planning. Despite the extensive use of comparative analyses of deep learning models in the literature, no prior studies have utilized data from the National Guard Hospital, which provides a unique and clinically relevant dataset. This research aims to address this gap by evaluating the performance of various deep learning models on this specific dataset to identify the most accurate and reliable model for brain tumor classification. This study leverages transfer learning techniques in Convolutional Neural Networks (CNNs), employing pre-trained models to enhance the classification process. By fine-tuning models such as VGG16, ResNet50, InceptionV3, and Xception, the research capitalizes on the strengths of these architectures trained on large-scale datasets to improve classification accuracy in a novel, real-world setting. The comparative analysis conducted in this research highlights the performance of these CNN models on the National Guard Hospital’s dataset, filling a critical gap in the literature where this specific dataset has been previously underexplored. Results show that VGG16 achieves the highest accuracy at 91.47%, followed by ResNet50 at 86.80%, InceptionV3 at 82.67%, and Xception at 82.13%. These findings demonstrate the potential of transfer learning models in enhancing brain tumor classification accuracy when applied to specialized clinical data, ultimately contributing to improved patient care. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Next-k Items Recommendation with Item Novelty 14th

Next-k Items Recommendation with Item Novelty

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14th International Conference on Frontier Computing, FC 2024

作者： Ma, Hao-Shang Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung Taiwan

ISBN: (纸本)9789819623945

Recommendation engines leverage past user preferences to forecast their future interests. Many deep learning-based recommendation systems aim to explore the intricate dynamics between users and particular items. Typically, the most recommendation systems have a tendency to suggest items based on their popularity or relevance to the user’s previous interactions, which will lead to a narrow range of recommendations. We would like to tackle this issue by evaluating potential items based on the novelty concept, effectively broadening the spectrum of recommendations. Furthermore, we detail a framework that allows for the seamless integration of this novel feature into next-k item recommendation architectures. Through rigorous experiments, the proposed method demonstrated superior performance compared with existing methods, confirming that integrating a novelty score can significantly refine the quality of recommendations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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HRAM-VITON: High-Resolution Virtual Try-On with Attention Mechanism

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computers, Materials & Continua 2025年第2期82卷 2753-2768页

作者： Yue Chen Xiaoman Liang Mugang Lin Fachao Zhang Huihuang Zhao College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002China

The objective of image-based virtual try-on is to seamlessly integrate clothing onto a target image, generating a realistic representation of the character in the specified attire. However, existing virtual try-on methods frequently encounter challenges, including misalignment between the body and clothing, noticeable artifacts, and the loss of intricate garment details. To overcome these challenges, we introduce a two-stage high-resolution virtual try-on framework that integrates an attention mechanism, comprising a garment warping stage and an image generation stage. During the garment warping stage, we incorporate a channel attention mechanism to effectively retain the critical features of the garment, addressing challenges such as the loss of patterns, colors, and other essential details commonly observed in virtual try-on images produced by existing methods. During the image generation stage, with the aim of maximizing the utilization of the information proffered by the input image, the input features undergo double sampling within the normalization procedure, thereby enhancing the detail fidelity and clothing alignment efficacy of the output image. Experimental evaluations conducted on high-resolution datasets validate the effectiveness of the proposed method. Results demonstrate significant improvements in preserving garment details, reducing artifacts, and achieving superior alignment between the clothing and body compared to baseline methods, establishing its advantage in generating realistic and high-quality virtual try-on images.

关键词： Virtual try-on attention mechanism high-resolution image generation

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