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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Wu, Dongkuo Wang, Xueyi Wang, Xingwei Huang, Min Wang, Zhitong College of Computer Science and Engineering Northeastern University Liaoning Shenyang China College of Software Northeastern University Liaoning Shenyang China College of Information Science and Engineering Northeastern University Liaoning Shenyang China

ISBN: (纸本)9783031714696

With the rapid advancement of wireless networks, edge computing has emerged as a promising paradigm for providing computing services to nearby latency-sensitive applications. Toward this trend, resource trading markets among mobile users (MUs) and edge clouds are emerging which need well-designed allocation and pricing mechanisms. In this paper, we propose a double auction-based multi-market resource trading framework (DAMRT), which integrates the dynamic programming and padding-based double auction methods, aiming to achieve approximate social welfare maximization and guarantee the properties of truthfulness and budget balance in the constrained edge computing system. To be specific, for admission control among different base station (BS) central markets, a dynamic programming-based method is proposed to obtain the optimal BS-MU association set. For each market, a truthful padding-based double auction resource allocation mechanism (TPDA) is proposed, which uses a linear programming-based padding method and a binary search algorithm to obtain the near-optimal allocation solution, and leverages a critical-value and a VCG-based pricing strategy for winning MUs and service providers. Our theoretical analysis proves that TPDA achieves truthfulness, individual rationality, and budget balance. Furthermore, simulation results verify the effectiveness of DAMRT. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Budget control

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Exploring the Privacy-Accuracy Trade-Off Using Adaptive Gradient Clipping in Federated Learning

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IEEE Transactions on Network science and engineering 2025年第3期12卷 2254-2265页

作者： Zhang, Benteng Mao, Yingchi He, Xiaoming Ping, Ping Huang, Huawei Wu, Jie Hohai University College of Computer Science and Software Engineering Nanjing211100 China Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing210003 China Sun Yat-Sen University School of Software Engineering Zhuhai519082 China Temple University Center for Networked Computing PhiladelphiaPA19122 United States

In Differentially Private Federated Learning (DP-FL), gradient clipping can prevent excessive noise from being added to the gradient and ensure that the impact of noise is within a controllable range. However, state-of-the-art methods adopt fixed or imprecise clipping thresholds for gradient clipping, which is not adaptive to the changes in the gradients. This issue can lead to a significant degradation in accuracy while training the global model. To this end, we propose Differential Privacy Federated Adaptive gradient Clipping based on gradient Norm (DP-FedACN). DP-FedACN can calculate the decay rate of the clipping threshold by considering the overall changing trend of the gradient norm. Furthermore, DP-FedACN can accurately adjust the clipping threshold for each training round according to the actual changes in gradient norm, clipping loss, and decay rate. Experimental results demonstrate that DP-FedACN can maintain privacy protection performance similar to that of DP-FedAvg under member inference attacks and model inversion attacks. DP-FedACN significantly outperforms DP-FedAGNC and DP-FedDDC in privacy protection metrics. Additionally, the test accuracy of DP-FedACN is approximately 2.61%, 1.01%, and 1.03% higher than the other three baseline methods, respectively. DP-FedACN can improve the global model training accuracy while ensuring the privacy protection of the model. All experimental results demonstrate that the proposed DP-FedACN can help find a fine-grained privacy-accuracy trade-off in DP-FL. © 2013 IEEE.

关键词： Federated learning

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Highly Robust and Diverse Coverless Image Steganography Against Passive and Active Steganalysis

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2771-2787页

作者： Guo, Bobiao Ping, Ping Xu, Feng Hohai University College of Computer Science and Software Engineering Nanjing210098 China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing210098 China Jiangsu Ocean University School of Computer Engineering Lianyungang222005 China

To avoid the pixel modification traces left by steganography from being detected by passive steganalysis, and to prevent the hidden data from being destroyed by active steganalysis attacks, Coverless Image Steganography (CIS) that does not modify pixels has attracted widespread attention. However, most existing CIS methods are limited in their maximum capacity due to insufficient diversity in their hash sequences. In addition, these methods struggle to maintain high robustness against both geometric and non-geometric attacks simultaneously. To address these two issues, a new coverless image steganography method is proposed to enhance CIS methods’ applicability, security, and robustness in highly insecure networks. During the hiding process, hash sequences are generated by a SHA-256 algorithm that integrates inter-block and inter-channel fusion, providing higher diversity than other CIS methods. Consequently, the proposed CIS method achieves higher capacity on publicly available datasets. During the extraction process, an evaluation metric that combines visual and histogram similarity is designed to improve the accuracy of inverse image retrieval. The experimental results demonstrate that the proposed CIS method achieves capacity increases of 26.73% and 38.34% over other CIS methods on the VOC and COCO datasets, respectively. Moreover, this method exhibits nearly 100% robustness against common active steganalysis. © 2004-2012 IEEE.

关键词： Steganography Robustness Feature Extraction Data Mining Image Retrieval Visualization Accuracy software Indexes Histograms Coverless Image Steganography CIS Hash Generation Algorithm Inverse Image Retrieval Steganalysis High Robustness Robust Image Image Coding Hidden Information Retrieval Accuracy Visual Similarity COCO Dataset Convolutional Neural Network Computational Complexity Robust Method Image Dataset Bit Error Rate Types Of Attacks Auxiliary Information Scale Invariant Feature Transform Index Database Histogram Bins Length Information Cover Image Military Intelligence Secret Information Center Crop Secure Transmission Salt And Pepper Noise XOR Operation Number Of Different Sequences JPEG Compression Generative Adversarial Networks Pixel Position Common Attacks

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Unpaired 3D Shape-to-Shape Translation via Gradient-Guided Triplane Diffusion

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3376-3386页

作者： Zhang, Wenxiao Rahmani, Hossein Liu, Jun Hohai University College of Computer Science and Software Engineering Nanjing211100 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom

Unpaired shape-to-shape translation refers to the task of transforming the geometry and semantics of an input shape into a new shape domain without paired training data. Previous methods utilize GAN-based architectures to perform shape translation, employing adversarial training to transform the source shape encoding into the target domain in the low-dimensional latent feature space. However, these methods encounter difficulties in generating diverse and high-quality results, as they often suffer from issues such as "mode collapse". This leads to limited generation diversity and makes it challenging to find an accurate latent code that adequately represents the input shape. In this paper, we achieve unpaired shape-to-shape translation via a triplane diffusion model, in which we factorize 3D objects into triplane representations and conduct a diffusion process on these representations to accomplish shape domain transformation. We observe that by adding an appropriate amount of noise to an input object during the forward diffusion process, domain-specific shape structures are smoothed out while the overall structure is still preserved. Subsequently, we progressively remove the noise via an unconditional diffusion model trained on the target shape domain in the reverse diffusion process. This allows us to obtain a denoised output that retains the structural similarities of the source input while aligning with the distribution of the target shape domain. During this process, we propose two gradient-based guidance mechanisms to guide the translation process to guarantee more faithful results during the denoising process. We conduct extensive experiments on different shape domains, and the experimental results demonstrate that our method achieves superior shape fidelity with high quality compared to current state-of-the-art baselines. © 1995-2012 IEEE.

关键词： Signal encoding

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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SLwF: A Split Learning Without Forgetting Framework for Internet of Things

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IEEE Internet of Things Journal 2025年第9期12卷 12008-12020页

作者： Feng, Xingyu Jia, Renqi Luo, Chengwen Leung, Victor C. M. Xu, Weitao School of Information Engineering Beijing100190 China City University of Hong Kong Shenzhen Research Institute Hong Kong Hong Kong City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

Split learning (SL) is widely regarded as a promising distributed machine learning framework with superior privacy-preserving properties, lower communication and computation costs. However, in real Internet of Things (IoT) scenarios, existing SL may not perform well because the local data of IoT devices often do not follow the same distribution. This leads to the model continuously adapting to the current data distribution in each training epoch, resulting in a catastrophic forgetting phenomenon. Existing methods typically attempt to add raw or generated data from previous devices in the current training epoch to review knowledge, but direct access to the local data of other devices carries serious privacy risks. Data augmentation techniques based on generative networks often have poor robustness and increase the computation cost on the device side. To address these challenges, we propose a new SL framework called SL without Forgetting (SLwF). To mitigate catastrophic forgetting without accessing any previous data, we propose a contrastive learning-based training method that leverages current training data to review previous knowledge, and learn new knowledge better. Furthermore, we adopt an exponential moving average (EMA)-based model update strategy to preserve lost knowledge, further alleviating the forgetting problem. We implement the SLwF framework in real IoT scenarios and extensively evaluated its performance using four publicly available datasets. Compared to other related research (e.g., IoTSL), SLwF performs better in terms of final accuracy and robustness while avoiding excessive device energy consumption. © 2014 IEEE.

关键词： Contrastive Learning

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CLS-3D: Content-Wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection in Autonomous Vehicles

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IEEE Access 2025年 13卷 69840-69856页

作者： Mushtaq, Husnain Latif, Sohaib Saad Bin Ilyas, Muhammad Mohsin, Syed Muhammad Ali, Mohammed Central South University School of Computer Science and Engineering Changsha410083 China Grand Asian University Department of Computer Science and Software Engineering Sialkot51040 Pakistan The University of Chenab Department of Computer Science Gujranwala50700 Pakistan COMSATS University Islamabad Department of Computer Science Islamabad45550 Pakistan Virtual University of Pakistan Standard College of Modern Sciences Lahore55150 Pakistan King Khalid University Department of Computer Science Abha61421 Saudi Arabia Abha61421 Saudi Arabia

Accurate 3D object detection is vital in autonomous driving. Single-modal detectors, using either camera or LiDAR, struggle with issues like limited depth perception or difficulty in distinguishing semantically similar objects. While multimodal approaches aim to address these limitations by combining LiDAR and camera data, they often face complexities in integrating sparse and uneven point cloud distributions, resulting in inefficient feature fusion. To tackle these challenges, we propose CLS-3D (Content-wise LiDAR-Camera Fusion and Slot Reweighting Transformer for 3D Object Detection). This novel framework fuses LiDAR and camera features using a single multi-modal backbone and augments them with the semantic probabilities obtained from the image stream. Our method captures local and global spatial relationships through a slot reweighting mechanism and incorporates I3C-IoU loss for precise box regression. The semantically augmented features, via a single multi-modal backbone, are embedded using a content-based transformer and processed through a slot-wise auto-encoder structure with channel-wise positional embeddings and a feed-forward MLP network. Our model improves temporal consistency and detection accuracy by dynamically adjusting feature relevance through slot-wise reweighting. We further define a I3C-IoU metric, considering centre, overlap, and scale for enhanced box regression accuracy. This mechanism allows the model to focus on significant temporal information, enhancing its ability to learn complex sequences and improving the overall performance of 3D object detection, especially in challenging scenarios such as occlusion and long-range detection. Extensive experiments on the KITTI and nuScenes benchmark demonstrate that CLS-3D achieves state-of-the-art performance, with 89.52% 3D mAP and 94.08% BEV mAP, outperforming existing methods. © 2013 IEEE.

关键词： Depth perception

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YOLOCSP-PEST for Crops Pest Localization and Classification

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computers, Materials & Continua 2025年第2期82卷 2373-2388页

作者： Farooq Ali Huma Qayyum Kashif Saleem Iftikhar Ahmad Muhammad Javed Iqbal Department of Software Engineering University of Engineering and TechnologyTaxila47050Pakistan Department of Computer Science&Engineering College of Applied Studies&Community ServiceKing Saud UniversityRiyadh11362Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science University of Engineering and TechnologyTaxila47050Pakistan

Preservation of the crops depends on early and accurate detection of pests on crops as they cause several diseases decreasing crop production and quality. Several deep-learning techniques have been applied to overcome the issue of pest detection on crops. We have developed the YOLOCSP-PEST model for Pest localization and classification. With the Cross Stage Partial Network (CSPNET) backbone, the proposed model is a modified version of You Only Look Once Version 7 (YOLOv7) that is intended primarily for pest localization and classification. Our proposed model gives exceptionally good results under conditions that are very challenging for any other comparable models especially conditions where we have issues with the luminance and the orientation of the images. It helps farmers working out on their crops in distant areas to determine any infestation quickly and accurately on their crops which helps in the quality and quantity of the production yield. The model has been trained and tested on 2 datasets namely the IP102 data set and a local crop data set on both of which it has shown exceptional results. It gave us a mean average precision (mAP) of 88.40% along with a precision of 85.55% and a recall of 84.25% on the IP102 dataset meanwhile giving a mAP of 97.18% on the local data set along with a recall of 94.88% and a precision of 97.50%. These findings demonstrate that the proposed model is very effective in detecting real-life scenarios and can help in the production of crops improving the yield quality and quantity at the same time.

关键词： Deep learning classification of pests YOLOCSP-PEST pest detection

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IPT-ILR: Image Pyramid Transformer Coupled With Information Loss Regularization for All-in-One Image Restoration

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IEEE Transactions on Circuits and Systems for Video Technology 2025年第5期35卷 4341-4356页

作者： Yang, Sai Hu, Bin Liu, Fan Wu, Xiaoxin Ding, Weiping Zhou, Jun Nantong University School of Electrical Engineering and Automation Nantong226019 China Nantong University School of Artificial Intelligence and Computer Science Nantong226019 China Hohai University College of Computer Science and Software Engineering Nanjing210098 China Griffith University School of Information and Communication Technology NathanQLD4111 Australia

All-in-one image restoration has recently developed to be a new research trend in the low-level computer vision field, aiming to tackle multiple image degradation types simultaneously in a unified model. As a typical multi-task learning, existing approaches focus on modeling either the specificity or commonality among different image restoration tasks. To exploit the unique strengths of both worlds, we propose a method of Image Pyramid Transformer coupled with Information Loss Regularization (IPT-ILR), in which the multi-scale architecture structure can excavate more information for multiple restoration tasks concurrently, while the learning strategy can identify the difference among multiple restoration tasks depending on the degree of information loss in each restoration task. Specifically, it first establishes a new Image Pyramid Transformer Network (IPT-Network) to accommodate multiple image restoration tasks. Given original degraded images, the IPT-Network exploits the image pyramid technique to establish a series of images with different scales, which are then restored by transformer-like auto-encoders. Moreover, the restored image on a low-level scale is referenced to assist restoring the degraded image on a high-level scale. Next, Information Loss Regularization (ILR) is presented to optimize the IPT-Network. ILR calculates the average distance between degraded images and their clean counterparts as the weights, which automatically implement different penalties for different image restoration tasks, thus avoiding the short-cut phenomenon for the easy task while encouraging the hard task. Extensive experiments have been conducted with 6 image restoration tasks in the all-in-one setting. The results show our method performs favorably against numerous state-of-the-art methods across most tasks, including image denoising, image deblurring, image dehazing, image deraining, image desnowing, as well as low-light enhancement. © 1991-2012 IEEE.

关键词： Restoration

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Next-Generation Cybersecurity Solution: A Decentralized Ransomware Recovery Network (DRRN) with Secret Sharing

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SN computer science 2025年第5期6卷 1-22页

作者： Ali, Sijjad Wang, Jia Leung, Victor C. M. Ali, Asad College of Computer Science and Software Engineering Shenzhen University Yuehai Campus Guangdong Shenzhen 518060 China College of Electronics and information Engineering Shenzhen University Yuehai Campus Guangdong Shenzhen 518060 China

The increasing frequency of ransomware attacks, with a reported growth rate of 59%, has exposed significant vulnerabilities in existing cybersecurity measures. Traditional defenses, such as routine data backups and endpoint protection systems, often fall short against sophisticated ransomware that encrypts data and demands exorbitant payments for decryption keys. These limitations are exacerbated by attackers leveraging centralized storage systems and static recovery methods, making data recovery costly, unreliable, and time-consuming. To address this pressing issue, this paper proposes a novel Decentralized Ransomware Recovery Network, which leverages decentralized storage and secret sharing theory to enhance data security and recovery. The proposed scheme introduces a decentralized encryption/decryption framework that distributes data and keys across the network, reducing the economic incentive for attackers by enabling secure and reliable data recovery without ransom payments. At its core, the system utilizes Shamir’s Secret Sharing Scheme, where encryption keys are divided into shares and distributed among nodes. In the event of an attack, these shares are reconstructed to restore access to the encrypted files. Additionally, the proposed scheme incorporates a dynamic key reconfiguration mechanism, where keys are updated after each recovery process to thwart repeated attacks. Through extensive simulations and evaluations under various scenarios, the proposed scheme demonstrates superior performance in terms of recovery efficiency, security resilience, and adaptability, achieving a significant reduction in attack success rates. By offering a robust and preventative defense mechanism, this research highlights the novelty and practicality of the proposed scheme as an effective cybersecurity strategy against ransomware. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity defense Decentralized Ransomware Recovery Network (DRRN) Ransomware Resilience Secret sharing schemes

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