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Face image authentication scheme based on MTCNN and SLT

Multimedia Tools and Applications 2025年 1-43页

作者： Thabit, Rasha Al-Askari, Mohanad A. Mohammed, Dunya Zeki Anaam, Elham Abdulwahab Mahmood, Zainab H. Jabbar, Dina Jamal Salih, Zahraa Aqeel Information System Department College of Computer Sciences and Information Technology University of Al-Anbar Al-Anbar Iraq Computer Engineering Department College of Engineering Al-Iraqia University Baghdad Iraq Electronic and Communications Engineering Department College of Engineering Gilgamesh University Baghdad Iraq Faculty of Computing and Informatics Multimedia University Selangor Cyberjaya Malaysia Department of Cybersecurity Engineering College of Information Engineering Al-Nahrain University Baghdad Iraq Electrical Engineering Technical Collage Middle Technical University Baghdad Iraq Department of Business Administration Alamaal University College Baghdad Iraq

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specific conditions, however, many limitations and challenges can be encountered in this field. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Different block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for different face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efficiency of the proposed scheme in detecting and localizing different face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications. © The Author(s), un

关键词： Authentication

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A Multi-Agent Garage Service Search and Recommendation with Hybrid MLs and LLMs

A Multi-Agent Garage Service Search and Recommendation with ...

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Shuvo, Mahfuzur Rahman Rahman, Ashifur Akuthota, Vishwanath Paul, Tanay Islam, Mahfujul Ashraf, Md Sadi Roy, Prosenjit Reza, Md Tanzim International American University Department of Management Information System United States Chittagong University of Engineering and Technology Department of Computer Science and Engineering Bangladesh TechOptima Hyderabad India Brac University Department of Computer Science and Engineering Bangladesh Prairie View A & M University Department of Computer Science TX United States

ISBN: (纸本)9798331513894

The automobile service industry's explosive growth highlights the need for creative approaches to boost operational effectiveness and user experience. This study introduces a Hybrid Garage Assistance system, integrating Classical Machine Learning (ML) techniques with Generative AI to optimize garage service discovery and analysis. The system employs sophisticated data processing methods, including Term Frequency-Inverse Document Frequency (TF-IDF) vectorization and regex-based service detection, to extract actionable insights from unstructured garage *** to the system are machine learning models Random Forest (RF) and XGBoost (XGB) which achieve high precision and recall in classifying garage services. A hybrid search mechanism, combining cosine similarity with ML-driven predictions, ensures the delivery of highly personalized search results. To further refine decision-making, the system incorporates Generative AI models such as Perplexity for web-based research, Gemini for location-specific analysis, Mistral for email sending and GPT-4 for detailed service recommendations and dall-e for creating user specific parts images. These advanced tools provide users with comprehensive information that enables them to make well-informed decisions about garage *** evaluation of the system is conducted using robust metrics, including precision, recall, F1-score, and system latency. Experimental results reveal a precision of 85%, recall of 70.8%, and an F1-score of 77.2%, demonstrating the efficacy of integrating classical ML with generative AI. The system's average latency of 5.9 seconds ensures a seamless and responsive user *** hybrid framework highlights the potential of blending classical ML and Large Language Models (LLMs) to enhance search and recommendation functionalities, offering a scalable and robust blueprint for future advancements in the automotive service sector. The system's Propose a Multi-Agent system With high accuracy,

关键词： Inverse problems

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A Framework for Deepfake Detection using Convolutional Neural Network and Deep Features

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Procedia computer Science 2025年 258卷 3640-3648页

作者： Soundarya B C Gururaj H L Naveen Kumar C M Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer Science and Business System Malnad College of Engineering Hassan India

With the advancement of Artificial Intelligence, facial recognition has become a crucial biometric feature. Deepfake technology leverages AI and can create hyper-realistic digitally manipulated videos of people appearing to say or do things that never occurred. The emergence of Generative Adversarial Networks (GANs) has further enabled the creation of fake visual content with astonishing realism. This technology has diverse applications, such as in the film industry, where it allows for video recreation without reshooting, creating awareness videos, restoring the voices of those who have lost them, and updating movie scenes at low cost. However, this rapid advancement also presents significant challenges. The proliferation of synthetic images raises severe concerns about their societal impact, particularly in terms of potential misuse for harassment and blackmail. Therefore, developing robust deepfake detection models is imperative. This study evaluates the performance of a proposed ResNet34 model in deepfake detection. We utilize the FaceForensics++ dataset to train and assess the model, incorporating images generated by four popular deepfake techniques. Our experimental results demonstrate that integrating linear ternary patterns (LTP) and edge detection-based features with the modified ResNet34 model achieves superior performance, attaining 97.5% accuracy and surpassing other approaches.

关键词： Deepfake Artificial intelligence Deep learning

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Cocktail Party Effect Using Parallel Intra and Inter Self-attention 3rd

Cocktail Party Effect Using Parallel Intra and Inter Self-at...

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3rd International Conference on Advances in Information and Communication technology, ICTA 2024

作者： Tan, Ha Minh Quoc, Nguyen Kim Vu, Duc-Quang Dang, An Thi, Duyen Nguyen Faculty of Information Technology Nguyen Tat Thanh University Thai Nguyen Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Thai Nguyen Viet Nam Faculty of Computer Science Phenikaa University Ha Noi Viet Nam Thai Nguyen University of Information and Communication Technology Thai Nguyen Viet Nam

ISBN: (纸本)9783031809422

The self-attention architecture is also considered to be a significant contribution in sequence processing tasks. It has the ability to highlight the distinctive features of a sequence, has been very successful in natural language processing and speech separation. In addition, the intra- and inter-architecture of the dual-path bi-directional long-short term memory framework has been demonstrated to be very effective for processing extremely long sequences. Based on these properties, we design a core architecture called intra- and inter-self-attention framework. Parallel models is more efficient and stable than a single model. In this research, we propose a parallel intra- and inter-self-attention framework (PI2SA) for the cocktail party effect task. The experimental results demonstrate that our PI2SA is remarkably effective. It outperforms both traditional self-attention models and a basic intra- and inter-architecture. Our PI2SA achieves competitive results with state-of-the-art deep learning models on the cocktail party effect problem. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep neural networks

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systematic mapping study of tools to identify emotions and personality traits

Discover Artificial Intelligence

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Discover Artificial Intelligence 2025年第1期5卷 1-33页

作者： Islam, Amanul Mohd Noor, Nurul Fazmidar Abdul Rahman, Siti Soraya Department of Computer Science University of Colorado Colorado Springs Colorado Springs USA Department of Computer System and Technology University of Malaya Kuala Lumpur Malaysia Department of Artificial Intelligence University of Malaya Kuala Lumpur Malaysia

Emotions and personality traits are important for human behavior and well-being. In recent years, techniques made possible by machine learning and computer-based tools have provided new ways to identify and understand the psychological aspects of individuals. This systematic Mapping Study, based on a review of research articles from reputable journals, presents tools that use a variety of data sources such as text analysis, face recognition, gestures, and heart rate monitoring. The selected papers were analyzed, and the identified tools were categorized based on methodology, objectives, and application domains. Natural language processing techniques were used to capture emotions from textual data, whereas face recognition proved to be more accurate with deep learning models. Machine learning algorithms show promise given gestures and heart rate analysis related to personality traits and emotional responses. The key findings of this study emphasize the need for standardized validation methods and large-scale studies across diverse populations to enhance the reliability and applicability of these tools. This work provides a foundation for future research and practical applications in emotion and personality identification.

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Moving Target Defense Meets Artificial Intelligence-Driven Network: A Comprehensive Survey

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IEEE Internet of Things Journal 2025年第10期12卷 13384-13397页

作者： Zhang, Tao Kong, Fanyu Deng, Dongshang Tang, Xiangyun Wu, Xuangou Xu, Changqiao Zhu, Liehuang Liu, Jiqiang Ai, Bo Han, Zhu Deng, Robert H. Beijing Jiaotong University School of Cyberspace Science and Technology Beijing100044 China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Anhui243002 China Minzu University of China School of Information Engineering Beijing100081 China Chongqing University College of Computer Science Chongqing400044 China Anhui University of Technology School of Computer Science and Technology Anhui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui243002 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Beijing Jiaotong University School of Electronic and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing100044 China University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Singapore Management University Department of Information Systems Singapore Singapore

Based on emerging Artificial Intelligence (AI) tasks, cloud-edge-terminal architecture can provide powerful computing, intelligent interconnection, and real-time response, which can also be regarded as AI-driven network. Unfortunately, multiple network layers in the AI-driven network usually face various types of network threats such as malicious network reconnaissance, side-channel attacks, and Distributed Denial of Service (DDoS). Traditional security solutions respond to network threats after the occurrence of attacks. To solve this problem, the concept of Moving Target Defense (MTD) has been proposed as a proactive defense mechanism that aims to defend against cyber attacks before they occur. In this paper, we firstly provide a thorough analysis of the threats in the cloud-edge-terminal network. Then we conduct a comprehensive survey to discuss the concept, design principles, and main classifications of MTD. Next, we further introduce the development potential in terms of AI-powered MTD on each network layer. Meanwhile, we also explore how MTD improves the security of AI algorithms. Lastly, we describe the existing challenges and research directions of MTD. The aim of this paper is to provide an in-depth understanding for the readers on how to realize the integration between MTD and AI-driven network. © 2014 IEEE.

关键词： Cloud security

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Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp Pose Selection Skill 6

Skill-Based Edge-Brain Smart Manufacturing: A Case of Grasp ...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Lee, Sukhan Jang, Byungwoo Hyeon, Seokjong Lee, Soojin Lee, Jaesun Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Artificial Intelligence Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of AI System Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Regional Industry Innovation Korea Institute of Industrial Technology Ulsan44413 Korea Republic of

The edge-brain framework (EBF) is a S/W framework that allows manufacturers to easily integrate AI-empowered robot work skills into a legacy production system in an edge computing environment with minimal time and effort. For EBF-based smart manufacturing, is a skill-based task-level robot programming environment that plays a key role. In this paper, we present the grasp pose selection skill as a case study to demonstrate the proposed skill-based edge-brain smart manufacturing. The proposed grasp-pose selection skill determines the optimal 6D grasp pose associated with the target object in a cluttered workspace by taking into consideration grasp stability, object manipulability, and collision-free path efficacy. To achieve a real-time operation of optimal grasp pose selection skill, deep learning networks are designed to compute, in particular, the manipulability and collision-free path efficacy indices. Experiments demonstrate the effectiveness of the proposed real-time grasp pose selection skill developed for EBF-based smart manufacturing. © 2024 The Authors. Published by Elsevier B.V.

关键词： Smart manufacturing

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State-of-the-Art in Feature Selection: Applications of the Slime Mould Algorithm 1st

State-of-the-Art in Feature Selection: Applications of the...

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1st International Conference on Computational Technologies and Electronics, ICCTE 2023

作者： Chatterjee, Taniya Bhakta, Puja Ghosh, Mili Barman, Debaditya Department of Computer Science and Technology University of North Bengal Darjeeling734013 India Department of Computer and System Sciences Visva-Bharati Santiniketan731235 India

ISBN: (纸本)9783031819346

Feature selection makes machine learning models less complex, more accurate, and more interpretable. The Slime Mould Algorithm (SMA) is a bio-inspired technique that leverages the demeanour of slime moulds to solve optimisation problems efficiently. This survey paper aims to deliver an extensive review of the recent research works involving the application of SMA as well as SMA fused with other optimisation methods to develop a solution for the feature selection problems. This involves summarizing and categorizing relevant research papers, methodologies, and findings. The survey paper consolidates and structures the existing knowledge about how the SMA is applied in the context of feature selection. Our work will help researchers and practitioners better comprehend the current advancement in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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CMoS: Customizing Model Structures for Personalized Federated Learning

CMoS: Customizing Model Structures for Personalized Federate...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bingyan Liu Jing Yang Computer Science Beijing University of Posts and Telecommunications Beijing China Department of Computer System and Technology Universiti Malaya Kuala Lumpur Malaysia

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Personalized Federated Learning (PFL) has recently received significant interest due to its capability of generating customized models for data-heterogeneous clients. Previous work focuses on weight adaptation to fit the data distribution while ignoring the correlation between various data distributions and their corresponding model structures. In this paper, we introduce a new paradigm called data-aware structure personalization, aiming to personalize the model structure according to the data distribution of each client during FL. Specifically, we develop CMoS, a framework to Customize Model Structures for heterogeneous client data in a learning-based manner. The key idea is to gradually learn a data-aware sub-structure from the original model at each federated round, which is achieved by introducing a newly designed binary mask and a sparsity loss during local training. Extensive experiments on multiple scenarios demonstrate the effectiveness of CMoS in generating personalized models with superior performance.

关键词： Semiconductor device modeling Training Adaptation models Costs Correlation Federated learning Signal processing Data models Acoustics Speech processing

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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