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A multip-level additive distortion method for security improvement in palette image steganography

Journal of Visual Communication and Image Representation 2025年 110卷

作者： Chen, Yi Wang, Hongxia Cui, Yunhe Shen, Guowei Guo, Chun Liu, Yong Wu, Hanzhou State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Characteristic Key Laboratory of Software Engineering and Information Security Guizhou University Guiyang550025 China School of Cyber Science and Engineering Sichuan University Chengdu610065 China School of Artificial Intelligence Hubei University of Automotive Technology Shiyan442002 China School of Big Data and Computer Science Guizhou Normal University Guiyang550025 China School of Communication and Information Engineering Shanghai University Shanghai200444 China

With the rapid development of the Internet and communication technology, palette images have become a preferred media for steganography. However, the security of palette image steganography faces a big problem. To address this, we propose a multiple-level additive distortion method for security improvement in palette image steganography. The proposed multiple-level additive distortion method comprises an index-level cost method and a pixel-level cost method. The index-level and the pixel-level costs by the two methods can respectively reflect the relationship changes of adjacent indices and the pixels corresponding to the adjacent indices. Meanwhile, the index-level and the pixel-level costs can also reflect the modification impact of steganography. Therefore, the proposed method can improve the security of palette image steganography. We conducted extensive experiments on three datasets to verify the security improvement. Experiment results have shown our proposed multiple-level distortion method indeed has an advantage in security when compared with four state-of-the-art methods. © 2025 Elsevier Inc.

关键词： Additive costs Color table Index matrix Multiple-level distortions Palette images Security Steganography

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Can Large Language Models Serve as Evaluators for Code Summarization?

arXiv

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arXiv 2024年

作者： Wu, Yang Wan, Yao Chu, Zhaoyang Zhao, Wenting Liu, Ye Zhang, Hongyu Shi, Xuanhua Yu, Philip S. Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Big Data and Social Computing Lab University of Illinois Chicago Chicago United States Salesforce Research Palo Alto United States School of Big Data and Software Engineering Chongqing University Chongqing China

Code summarization facilitates program comprehension and software maintenance by converting code snippets into natural-language descriptions. Over the years, numerous methods have been developed for this task, but a key challenge remains: effectively evaluating the quality of generated summaries. While human evaluation is effective for assessing code summary quality, it is labor-intensive and difficult to scale. Commonly used automatic metrics, such as BLEU, ROUGE-L, METEOR, and BERTScore, often fail to align closely with human judgments. In this paper, we explore the potential of Large Language Models (LLMs) for evaluating code summarization. We propose CODERPE (Role-Player for Code Summarization Evaluation), a novel method that leverages role-player prompting to assess the quality of generated summaries. Specifically, we prompt an LLM agent to play diverse roles, such as code reviewer, code author, code editor, and system analyst. Each role evaluates the quality of code summaries across key dimensions, including coherence, consistency, fluency, and relevance. We further explore the robustness of LLMs as evaluators by employing various prompting strategies, including chain-of-thought reasoning, in-context learning, and tailored rating form designs. The results demonstrate that LLMs serve as effective evaluators for code summarization methods. Notably, our LLM-based evaluator, CODERPE, achieves an 81.59% Spearman correlation with human evaluations, outperforming the existing BERTScore metric by 17.27%. © 2024, CC BY.

关键词： software agents

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PATR: A Novel Poisoning Attack Based on Triangle Relations Against Deep Learning-Based Recommender Systems 17th

PATR: A Novel Poisoning Attack Based on Triangle Relations...

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17th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021

作者： Chao, Meiling Gao, Min Zhang, Junwei Wang, Zongwei Zhao, Quanwu He, Yulin School of Big Data and Software Engineering Chongqing University Chongqing China School of Economics and Business Administration Chongqing University Chongqing China Chongqing Aerospace Polytechnic Chongqing China

ISBN: (纸本)9783030926373

Recommender systems (RSs) have emerged as an effective way to deal with information overload and are very popular in e-commerce. However, because of the open nature of collaborative characteristics of the systems, RSs are susceptible to poisoning attacks, which inject fake user profiles into RSs to increase or decrease the recommended frequency of the target item. The traditional poisoning attack methods (such as random attack and average attack) are easy to be detected and lack of generality since they usually use global statistics, e.g., the number of each user’s ratings and the average rating for filler items. Moreover, as deep learning (DL) becomes more widely used in RSs, attackers are likely to use related techniques to attack RSs. To explore the robustness of DL-based RSs under the possible attacks, we propose a novel poisoning attack with triangle relations (PATR). The triangle relations refer to the balance among a fake user and two real users, aiming to improve attack performance. We also present a novel fake & real sampling strategy, i.e., sampling a set of fake users from the real users, to decrease the possibility of being detected. Comprehensive experiments on three public datasets show that PATR outperforms traditional poisoning attacks on attack effectiveness and anti-detection capability. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Recommender systems

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Generalized Undersampling of Center Points Based on Granular Ball 7

Generalized Undersampling of Center Points Based on Granular...

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7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Bai, Hao Wu, Yanmin Qi, Jinli Chen, Yanyi Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computer Science and Technology ChongQing China ChongQing College of Electronic Engineering Department of Artificial Intelligence and Big Data ChongQing China ChongQing College of Electronic Engineering Department of General Education and International Studies ChongQing China Chongqing University of Posts and Telecommunications Department of Software Engineering ChongQing China

ISBN: (纸本)9781665487115

In this paper, we propose a new generic sampling classification method based on granular Ball sampling because of good robust noise immunity and possessing properties that can be classified as an undersampling method, called Granular Ball Center-based Common Under-sampling. The method uses adaptively generated Ball to cover the data space, and the sampled data are supported by the centroid properties of the Ball. Unlike the boundary sampling of granular ball sampling, this method mainly uses the center points to smooth out the noise, which not only can clearly represent the attributes, but also can reduce some computational effort, and in most of the time, it can obtain almost the same accuracy as the original model, or even can perform better. Compared with undersampling, it can be better adapted to the task of unbalanced classification as well as non-unbalanced classification. The above can show that the method is able to be generalized without the restriction of specific classifiers and also has a strong adaptability to the dataset. The method can also be effectively used as an undersampling method for unbalanced classification because it is supported by the centroid property, and the method has a speedup effect for most classifiers. These advantages form a strong advantage of the method in improving classifier performance. © 2022 IEEE.

关键词： Cloud computing Adaptation models Computational modeling big data Task analysis

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Modeling Interactions Among Microservices Communicating via FIFO or Bag Buffers 12th

Modeling Interactions Among Microservices Communicating via ...

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12th EAI International Conference on Simulation Tools and Techniques, SIMUtools 2020

作者： Dai, Fei Yang, Jinmei Mo, Qi Zhou, Hua Qi, Lianyong School of Big Data and Intelligent Engineering Southwest Forestry University Kunming China School of Software Yunnan University Kunming China School of Information Science and Engineering Qufu Normal University Jining China

ISBN: (纸本)9783030727949

Interactions among individual microservices communicating asynchronously via FIFO or bag buffers vary significantly even for the same buffer size. Different interactions among microservices will lead to different interaction behaviors, which can make microservices systems malfunction during their execution. However, these two asynchronous communication models with FIFO or bag buffers are seldom distinguished. In this paper, we present new results for the interaction differences between one asynchronous communication model with FIFO buffers and another asynchronous communication model with bag buffers. First, we propose a framework to uniformly define two asynchronous communication models. Second, we model interaction behaviors among microservices as sequences of send and receive message actions under these two asynchronous communication models. Finally, we compare these two asynchronous communication models using refinement checking to show their differences. Experimental results show that the asynchronous communication model with FIFO buffers is included in the asynchronous communication model with bag buffers. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2021.

关键词： Computers

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GRAFRAUD: Fraud Detection using Graph databases and Neural Networks

GRAFRAUD: Fraud Detection using Graph Databases and Neural N...

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2023 4th International Informatics and software engineering Conference (IISEC)

作者： Alperen Sayar Şuayip Arslan Ajeet Singh Raina Seyit Ertuğrul Tuna Çakar Data Science Tam Finans R&D Istanbul Türkiye Big Data and Data Governance Massachusetts Institute of Technology Massachusetts USA Docker Containerized Software Docker Inc. California USA Computer Engineering MEF University Istanbul Türkiye

The issue of fraud has become a significant concern for many companies, particularly in the finance sector, but the traditional methods of detecting fraud are no longer adequate. Innovative technologies are necessary to identify complex fraudulent activities, and RedisGraph, a high-performance graph database, may offer a solution. With the assistance of neural networks, RedisGraph can accurately and efficiently detect fraudulent transactions in vast and intricate environments. Companies typically use a combination of Python and Oracle databases to design fraud detection systems. which provide robust data management and real time AI processing capabilities. These technologies allow to create fraud detection systems that can determine fraudulent activities in real-time. But according to advancements of fraud methods only using of these systems not efficient nowadays. This article presents a proof of concept based on an essential use case of RedisGraph-powered neural networks in detecting financial fraud. It demonstrates the value of carefully employing Python and Oracle database to construct and deploy real-time systems that can efficiently detect fraudulent activities.

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Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach

arXiv

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arXiv 2024年

作者： Xu, Changxin Qiao, Yuxin Zhou, Zhanxin Ni, Fanghao Xiong, Jize Computer Information Technology Northern Arizona University Flagstaff United States Big Data and Business Intelligence Universidad Internacional Isabel I de Castilla Burgos Spain Software Engineering Indian Institute of Technology Assam Guwahati India

Federated Learning (FL) is a distributed machine learning paradigm that allows clients to train models on their data while preserving their privacy. FL algorithms, such as Federated Averaging (FedAvg) and its variants, have been shown to converge well in many scenarios. However, these methods require clients to upload their local updates to the server in a synchronous manner, which can be slow and unreliable in realistic FL settings. To address this issue, researchers have developed asynchronous FL methods that allow clients to continue training on their local data using a stale global model. However, most of these methods simply aggregate all of the received updates without considering their relative contributions, which can slow down convergence. In this paper, we propose a contribution-aware asynchronous FL method that takes into account the staleness and statistical heterogeneity of the received updates. Our method dynamically adjusts the contribution of each update based on these factors, which can speed up convergence compared to existing methods. © 2024, CC BY.

关键词： Machine learning

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Audio Steganography Based Backdoor Attack for Speech Recognition software

Audio Steganography Based Backdoor Attack for Speech Recogni...

引用

IEEE Annual International Computer software and Applications Conference (COMPSAC)

作者： Mengyuan Zhang Shunhui Ji Hanbo Cai Hai Dong Pengcheng Zhang Yunhe Li Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China School of Computing Technologies RMIT University Melbourne Australia

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

With the growing prevalence of deep learning in the speech area, speech recognition, voice control, and related applications have become integral parts of people's lives. However, the rise of malicious third-party platforms has introduced significant security concerns, particularly through backdoor attacks. These attacks implant triggers that manipulate speech recognition models to produce specific labels, thereby compromising the system's integrity. Studying speech backdoor attacks is crucial for evaluating the security of speech recognition software, and iden-tifying and addressing potential vulnerabilities. Existing methods for speech backdoor attacks usually employ fixed perturbations as triggers. However, these perturbations may be discernible to the human ear, making them easily detectable. To address this issue, we propose a frequency domain-embedded backdoor attack method based on echo hiding. Echo hiding is a steganography technique based on audio. This method embeds hidden information into the frequency spectrum of the echo signal, leveraging the masking property of the human auditory system. It is difficult to arouse suspicion or detect the presence of hidden information since echo is perceived as a natural phenomenon in auditory perception. Furthermore, it does not cause a significant decrease in audio quality. Experimental results show the effectiveness of our method in different settings.

关键词： Training Steganography Perturbation methods Frequency-domain analysis Speech recognition Ear Implants

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Overview of 3D Human Pose Estimation

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Computer Modeling in engineering & Sciences 2023年第3期134卷 1621-1651页

作者： Jianchu Lin Shuang Li Hong Qin Hongchang Wang Ning Cui Qian Jiang Haifang Jian Gongming Wang Huaiyin Institute of Technology Huai’an223000China Jiangsu Outlook Shenzhou Big Data Technology Co. Ltd.Nanjing210002China Institute of Semiconductors Chinese Academy of SciencesBeijing100083China Center of Materials Science and Optoelectronics Engineering&School of Integrated Circuits University of Chinese Academy of SciencesBeijing100049China Inspur Software Group Company Ltd.Jinan250104China China GreatWall Industry Corporation Beijing100054China China GreatWall Industry Corporation Navigation Co. Ltd.Beijing100144China

3D human pose estimation is a major focus area in the field of computer vision,which plays an important role in practical *** article summarizes the framework and research progress related to the estimation of monocular RGB images and *** overall perspective ofmethods integrated with deep learning is *** image-based and video-based inputs are proposed as the analysis *** this viewpoint,common problems are *** diversity of human postures usually leads to problems such as occlusion and ambiguity,and the lack of training datasets often results in poor generalization ability of the *** methods are crucial for solving such *** image-based input,the multi-view method is commonly used to solve occlusion ***,the multi-view method is analyzed *** referring to video-based input,the human prior knowledge of restricted motion is used to predict human *** addition,structural constraints are widely used as prior ***,weakly supervised learningmethods are studied and discussed for these two types of inputs to improve the model generalization *** problem of insufficient training datasets must also be considered,especially because 3D datasets are usually biased and ***,emerging and popular datasets and evaluation indicators are *** characteristics of the datasets and the relationships of the indicators are explained and ***,this article can be useful and instructive for researchers who are lacking in experience and find this field *** addition,by providing an overview of 3D human pose estimation,this article sorts and refines recent studies on 3D human pose *** describes kernel problems and common useful methods,and discusses the scope for further research.

关键词： 3D human pose estimation monocular camera deep learning multi-view indicator

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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