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Reinforcement learning of non-additive joint steganographic embedding costs with attention mechanism

science China(Information sciences) 2023年第3期66卷 273-286页

作者： Weixuan TANG Bin LI Weixiang LI Yuangen WANG Jiwu HUANG Institute of Artificial Intelligence and Blockchain Guangzhou University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science and Cyber Engineering Guangzhou University

Image steganography is the art and science of secure communication by concealing information within digital images. In recent years, the techniques of steganographic cost learning have developed rapidly. Although the existing methods can learn satisfactory additive costs, the interplay of different pixels' embedding impacts has not been considered, so the potential of learning may not be fully exploited. To overcome this limitation, in this paper, a reinforcement learning paradigm called Jo Po L(joint policy learning) is proposed to extend the idea of additive cost learning to a non-additive situation. Jo Po L aims to capture the interactions within pixel blocks by defining embedding policies and evaluating contributions of embedding impacts on a block level rather than a pixel level. Then, a policy network is utilized to learn optimal joint embedding policies for pixel blocks through interactions with the environment. Afterwards,these policies can be converted into joint embedding costs for practical message embedding. The structure of the policy network is designed with an effective attention mechanism and incorporated with the domain knowledge derived from traditional non-additive steganographic methods. The environment is responsible for assigning rewards according to the impacts of the sampled joint embedding actions, which are evaluated by the gradient information of a neural network-based steganalyzer. Experimental results show that the proposed non-additive method Jo Po L significantly outperforms the existing additive methods against both feature-based and CNN-based steganalzyers over different payloads.

关键词： information hiding non-additive steganography steganalysis cost learning image processing

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Detecting cross-site scripting attacks using machine learning: A systematic review

Detecting cross-site scripting attacks using machine learnin...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Baniya, D. Amrita Chaudhary, A. Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

In today s age, many daily tasks are performed through the Internet using various web applications. While using the web application, the information and data are stored in the database of the network which can easily be attacked by the attacker. The attacker used different types of attack patterns to steal the victim s information or any details like their bank information, and business details. Among the different types of attacks over the web application vulnerabilities, the most prominent category of attack is a cross-site scripting (XSS) attack. In the XSS attack, an attacker transmits the script to the victim s web Browser. As the victims are unaware that they were being attacked leading to stealing the valuable information like personal details, bank details, and cookies. Using the cookies of the victim, the attacker can easily access their account without using a username and password. Early, different types of approaches have been done to shoot out the vulnerabilities related to XSS. This paper purports to be a survey on XSS. The primary contribution of this study to the current state-of-the-art XSS is a systematic review of various types of research done to detect XSS vulnerabilities using Machine Learning. This paper also provides the background related to XSS, techniques, difficulties and future guidelines in the field of XSS to provide researchers with significant knowledge for further work. © 2024 The Author(s).

关键词： Machine learning

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Vision-based Moving Target Tracking of Certain Target for Quadruped Robots

Vision-based Moving Target Tracking of Certain Target for Qu...

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2022 International Symposium on Biomimetic Intelligence and robotics, ISBIR 2022

作者： Wang, Kexin Chen, Teng Chen, Xin Zhang, Guoteng Li, Yibin Rong, Xuewen Center for Robotics School of Control Science and Engineering Shandong University China

Due to the high flexibility of quadruped robots compared with some traditional robots, it has become an important branch in the field of mobile robot research. Target detection and tracking technology is important for the environment perception and autonomous motion tasks of quadruped robots. To achieve effective tracking of a specific target, the premise is that the target is accurately detected. In this article, a target detection algorithm based on deep learning for quadruped robot person tracking is proposed. After the targets are detected, a simple but accurate selection strategy is used to extract the certain target to be tracked from other targets. The visual distance and angle information from the target is used to realize the autonomous tracking. In the part of experiment, we conducted experiments based on the changeable outdoor environment, which included interference factors such as light changes, people crossing, obstruction by obstacles. During the experiments, the SDUQuad-48 quadruped robot can simultaneously realize the stable tracking of a specific moving target based on our algorithm with a speed of 0.8 m/s. © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Proceedings of International Symposium on Biomimetic Intelligence and robotics.

关键词： Target tracking

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SLARD: A Chinese Superior Legal Article Retrieval Dataset 31

SLARD: A Chinese Superior Legal Article Retrieval Dataset

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31st International Conference on Computational Linguistics, COLING 2025

作者： Chen, Zhe Ren, Pengjie Sun, Fuhui Wang, Xiaoyan Li, Yunjun Zhao, Siwen Yang, Tengyi School of Information Science and Engineering Shandong University China School of Computer Science and Technology Shandong University China Information Technology Service Center of People's Court

ISBN: (纸本)9798891761964

Retrieving superior legal articles involves identifying relevant legal articles that hold higher legal effectiveness. This process is crucial in legislative work because superior legal articles form the legal basis for drafting new laws. However, most existing legal information retrieval research focuses on retrieving legal documents, with limited research on retrieving superior legal articles. This gap restricts the digitization of legislative work. To advance research in this area, we propose SLARD: A Chinese Superior Legal Article Retrieval Dataset, which filters 2,627 queries and 9,184 candidates from over 4.3 million effective Chinese regulations, covering 32 categories, such as environment, agriculture, and water resources. Each query is manually annotated, and the candidates include superior articles at both the provincial and national levels. We conducted detailed experiments and analyses on the dataset and found that existing retrieval methods struggle to achieve ideal results. The best method achieved a R@1 of only 0.4719. Additionally, we found that existing large language models (LLMs) lack prior knowledge of the content of superior legal articles. This indicates the necessity for further exploration and research in this field. © 2025 Association for Computational Linguistics.

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Multi-DNN Inference 30

RT-Swap: Addressing GPU Memory Bottlenecks for Real-Time Mul...

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30th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS 2024

作者： Kang, Woosung Lee, Jinkyu Lee, Youngmoon Oh, Sangeun Lee, Kilho Chwa, Hoon Sung DGIST Dept. of Electrical Engineering and Computer Science Korea Republic of Dept. of Computer Science and Engineering Korea Republic of Hanyang University Dept. of Robotics Korea Republic of Ajou University Department of Software and Computer Engineering Korea Republic of School of AI Convergence Soongsil University Korea Republic of

ISBN: (纸本)9798350358414

The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-Time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-Time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity. © 2024 IEEE.

关键词： Deep neural networks

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Auto Insurance Fraud Detection with Multimodal Learning

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Data Intelligence 2023年第2期5卷 388-412页

作者： Jiaxi Yang Kui Chen Kai Ding Chongning Na Meng Wang Financial Technological Research Center Zhejiang LabHangzhou 361005China School of Computer Science and Engineering Southeast UniversityNanjing 211189China

In recent years,feature engineering-based machine learning models have made significant progress in auto insurance fraud ***,most models or systems focused only on structural data and did not utilize multi-modal data to improve fraud detection *** solve this problem,we adapt both natural language processing and computer vision techniques to our knowledge-based algorithm and construct an Auto Insurance Multi-modal Learning(AIML)*** then apply AIML to detect fraud behavior in auto insurance cases with data from real scenarios and conduct experiments to examine the improvement in model performance with multi-modal data compared to baseline model with structural data only.A selfdesigned Semi-Auto Feature Engineer(SAFE)algorithm to process auto insurance data and a visual data processing framework are embedded within *** show that AIML substantially improves the model performance in detecting fraud behavior compared to models that only use structural data.

关键词： Auto Insurance Multi-modal Learning Fraud detection Ensemble learning

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Review of permission-based malware detection in Android

Review of permission-based malware detection in Android

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Rawat, Nishant Amrita Singh, Avjeet Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9781032669663

A market study showed that an average of 70% of smartphone users use an android-based smartphone. The Android operating system draws numerous malware threats as a result of its popularity. The statistic reveals that 97% of malware prey on android due to its lack of security. The malware enters the system and after installing the malware application they ask permission from the user to run. Permissions like Read_Phone, Access_Internet, Write_Log, etc give full control of the victim’s phone to the hacker. In recent years more efforts were made to analyze android permissions to detect malware. In this paper, we review some of the already published work in detail which is in the area of malware detection using static analysis. © 2024 The Author(s).

关键词： Machine learning

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Robust STL Control Synthesis under Maximal Disturbance Sets 63

Robust STL Control Synthesis under Maximal Disturbance Sets

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Verhagen, Joris Lindemann, Lars Tumova, Jana Kth Royal Institute of Technology Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science Stockholm Sweden University of Southern California Thomas Lord Department of Computer Science Los AngelesCA United States

ISBN: (纸本)9798350316339

This work addresses maximally robust control synthesis under unknown disturbances. We consider a nonlinear system, subject to a Signal Temporal Logic (STL) specification and jointly synthesize the maximal possible disturbance bounds and the corresponding controllers that ensure the STL specification is satisfied under these bounds. Many works have considered STL satisfaction under given bounded disturbances yet, to the authors' best knowledge, this is the first work that aims to maximize the permissible disturbance set and find corresponding maximally robust controllers. We, therefore, introduce disturbance robustness as a model-based robustness metric for STL planning and control synthesis. We extend the notion of disturbance-robust semantics for STL, which is a property of a specification, dynamical system, and controller, and provide an algorithm for maximally robust controllers satisfying an STL specification using Hamilton-Jacobi reachability. We show its soundness and provide a simulation example with an Autonomous Underwater Vehicle (AUV). © 2024 IEEE.

关键词： Control system synthesis

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Recent Advances in Robot Trajectory Planning in a Dynamic Environment

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Recent Advances in computer science and Communications 2022年第9期15卷 1168-1183页

作者： Zhang, Hongxin Shu, Rongzijun Li, Guangsen School of Mechanical Power and Engineering Harbin University of Science and Technology Harbin China Robotics & ITS Engineering Research Center Harbin University of Science and Technology Harbin150080 China

Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multi-sensor information fusion, the combination of local planning and global planning, and multi-robot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment. © 2022 Bentham science Publishers.

关键词： Trajectories

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