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A systematic literature review on image splicing detection and localization using emerging technologies

Multimedia Tools and applications 2024年 1-36页

作者： N, Chithra Raj Dutta, Maitreyee Saini, Jagriti National Institute of Technical Teachers’ Training and Research Chandigarh160019 India Eternal RESTEM Himachal Pradesh Mandi175031 India

Computer vision applications involving digital forensic investigations widely use digital images as legal documentary proof. Ensuring the authenticity and reliability of digital images by locating potential tampering is a critical area of concern for forensic applications. Splicing is one of the most commonly used methods for image tampering in digital domains. This systematic literature review (SLR) is conducted using PRISMA guidelines to explore the opportunities and challenges in the image splicing detection and localization (ISDL) domain. A total of 99 empirical papers were selected for an in-depth review from four major databases: IEEE Explore, Science Direct, Springer, and Web of Science. Papers were selected based on specific inclusion and exclusion criteria, focusing solely on those addressing deep learning, machine learning, transfer learning, and quantum computing technologies. The survey was conducted by framing 8 research questions based on ISDL to identify potential answers to its implementation. The synthesis shows that 83.84% of the ISDL studies were based on generic applications, and 73.74% of the studies utilized machine learning models for classification. Furthermore, accuracy, F1-score, and sensitivity were the most preferred evaluation metrics used by 63.64%, 38.38%, and 36.36% of studies, respectively. For ISDL applications, 54.54% of the studies used CASIA TIDE v2.0, and 37.37% incorporated graphics processing units (GPU). This paper presents a thorough synthesis of existing studies in the ISDL domain while highlighting the importance of various emerging technologies in dealing with digital forensics. The outcomes are expected to be useful to industry experts, researchers, and policymakers to establish safe practices in computer vision applications. Graphical Abstract: (Figure presented.) © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Digital forensics

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Micro Expression Recognition Using Convolution Patch in vision Transformer

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IEEE ACCESS 2023年 11卷 100495-100507页

作者： Indolia, Sakshi Nigam, Swati Singh, Rajiv Singh, Vivek Kumar Singh, Manoj Kumar Banasthali Vidyapith Dept Comp Sci Tonk 304022 Rajasthan India Banasthali Vidyapith Ctr Artificial Intelligence Tonk 304022 Rajasthan India Banaras Hindu Univ Dept Comp Sci Varanasi 221005 Uttar Pradesh India

Humans possess an intrinsic ability to hide their true emotions. Micro-expressions are subtle changes in facial muscles that are involuntary by nature and easy to hide. To address these issues, several machine and deep learning models have been proposed in the past few years. Convolution neural network (CNN) is a deep learning method that has widely been adopted in vision-related tasks due to its remarkable performance. However, CNN suffers from overfitting due to a large number of trainable parameters. Additionally, CNN cannot capture global information with respect to an input image. Furthermore, the identification of important regions for the classification of micro-expressions is a challenging task. Self-attention mechanism addresses these issues by focusing on key areas. Furthermore, specific transformers, known as vision transformers are widely explored in vision-related applications. However, existing vision transformers divide an input image into a fixed number of patches due to which local correlation of image pixels is lost. Further, a vision transformer relies on self-attention mechanism which effectively captures global dependencies but does not exploit the local spatial relationships in an image. In this work, we propose a vision transformer based on convolution patches to overcome this problem. The proposed algorithm generates $c $ number of feature maps from input images using $c $ filters through convolution operation. These feature maps are then applied to a transformer model as fixed-size image patches to perform classification. Thus, the proposed architecture leverages advantages of both convolutional layers and transformer, and captures both spatial information and global dependencies respectively, leading to improved performance. The performance of the proposed model is evaluated on three benchmark datasets: CASME-I, CASME-ii, and SAMM and compared with state-of-the-art machine and deep learning models, which generated classification accuracy of

关键词： & nbsp Facial expression recognition deep learning micro-expression recognition self-attention vision transformer

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Boosting comprehensive two-dimensional chromatography with artificial intelligence: Application to food-omics

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TRAC-TRENDS IN ANALYTICAL CHEMISTRY 2024年 174卷

作者： Caratti, Andrea Squara, Simone Bicchi, Carlo Liberto, Erica Vincenti, Marco Reichenbach, Stephen E. Tao, Qingping Geschwender, Daniel Alladio, Eugenio Cordero, Chiara Univ Torino Dipartimento Sci & Tecnol Farmaco Via Pietro Giuria 9 I-10125 Turin Italy Univ Torino Dipartimento Chim Via Pietro Giuria 7 I-10125 Turin Italy Univ Nebraska Lincoln Comp Sci & Engn Dept 104E Avery Hall Lincoln NE 68588 USA GC Image POB 57403 Lincoln NE 68505 USA

The unceasing evolution of analytical instrumentation determines an exponential increase of data production, which in turn boosts new cutting-edge analytical challenges, requiring a progressive integration of artificial intelligence (AI) algorithms into the instrumental data treatment software. machine learning, deep learning, and computer vision are the most common techniques adopted to exploit the information potential of advanced analytical chemistry measures. In this paper, our primary focus is on elucidating the remarkable advantages of leveraging AI tools for comprehensive two-dimensional gas chromatography data (pre)processing. We illustrate how AI techniques can efficiently explore the complex datasets derived from multidimensional platforms combining comprehensive two-dimensional separations with mass spectrometry in the challenging application area of food-omics. Pattern recognition based on image processing, computer vision, and AI smelling are discussed by introducing the principles of operation, reviewing available tools and software solutions, and illustrating their potentials and limitations through selected applications.

关键词： Comprehensive two-dimensional gas chroma-tography Artificial intelligence AI Computer vision Pattern recognition AI smelling Chromatographic fingerprinting GCxGC data processing Food-omics

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An AI Solution for Web Accessibility and images Classification 13

An AI Solution for Web Accessibility and Images Classificati...

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13th International Conference on image processing Theory Tools and applications

作者： Noreskal, Laura Feuilloley, Guillaume Charbel, Simon-Pierre Sogeti Part Capgemini Issy Les Moulineaux France Sogeti Part Capgemini Rennes France

ISBN: (纸本)9798331541859;9798331541842

This article details the research on web accessibility conducted at Capgemini's SogetiLabs. We introduce our project aimed at developing an automatic accessibility audit tool for website images. Our AI solution for web accessibility focuses on distinguishing between informative and decorative images in line with RGAA (Referenciel Gen eral d'Am elioration de l'Acessibilite) recommendations and then generating alternative text for informative images. To achieve this, we have established a comprehensive processing workflow. Additionally, we present initial experiments in image classification using Convolutional Neural Networks (CNNs) and YOLO's (You Only Look Once) model.

关键词： machine Learning Web Accessibility RGAA Computer vision image processing Classification YOLO CNN

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In-Sensor Noise Reduction and Reservoir Computing System Using ZnO Optoelectronic Memristors for Artificial vision applications

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ACS APPLIED ELECTRONIC MATERIALS 2024年第12期6卷 9019-9028页

作者： Wang, Liang Zhang, Le Hua, Shuaibin Chen, Anran Fu, Qiuyun Guo, Xin Huazhong Univ Sci & Technol Sch Mat Sci & Engn State Key Lab Mat Proc & Die & Mould Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Engn Res Ctr Funct Ceram Sch Integrated Circuits Minist Educ Wuhan 430074 Peoples R China

Rapid advancements in artificial intelligence (AI) and the Internet of Things (IoT) demand more efficient data processing than conventional von Neumann architectures offer. In-sensor reservoir computing (RC) addresses this by enabling data processing directly within sensors. Optoelectronic memristors, capable of responding to both electrical and optical inputs, have emerged as a promising solution. We present electronic neurons and opto-synapses made of Pt/Ag/ZnO/Pt/Ti memristors, demonstrating stable threshold switching (with cumulative probability variations of 5.06% for V th) and neuron functions (such as spike encoding and LIF behavior) under electrical stimuli, as well as light-tunable synaptic behaviors (including PPF and STM). This enables the device to perform image sensing and noise reduction. Moreover, we propose an in-sensor noise reduction and RC system that emulates the human vision system, achieving high-precision classification (99.33%) of noisy images. This system offers cost-effective training and efficient processing of optical stimuli, opening innovative avenues for edge computing and machine vision applications.

关键词： Threshold-Switching Memristor Optoelectronic Memristor In-sensor Reservoir Computing Visual Memory Artificial visual system

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Evaluate student achievement by classifying brain structure and its functionality with novel hybrid method

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NEURAL COMPUTING & applications 2024年第7期36卷 3357-3368页

作者： Atas, Pinar Karadayi Istanbul Arel Univ Dept Comp Engn TR-34537 Istanbul Turkiye

In a labor market that demands a workforce well-trained in science, technology, engineering, and mathematics (STEM) subjects, it is required of children to successfully develop their mathematical skills in order to become highly productive adults. Recent developments in computer vision, artificial intelligence, machine learning, and medical imaging techniques give us new opportunities for building intelligent support tools to help us learn more about the neural underpinnings of how children learn math and how that knowledge relates to individual differences in skill. This study examines the brain activities of students during problem-solving by checking brain structure and its functionality. By using powerful techniques in the light of machine learning and image processing, the relationship between success and the background of a child was researched. The aim is to make a solid prediction of the possible future success of the children by observing their brain activities. The children we investigated were asked different questions to get information about their intelligence. In our study, we have tried to find how those questions and answers may affect the future success of a child. For this purpose, a novel hybrid classification model that utilizes cluster analysis, Random Forest, Logistic Regression, and ensemble learning is intended for classification tasks. Our study includes two main stages. Firstly, the image processing techniques were applied to create unique features of brain images. Then, machine learning tecnniques were used to select a set of features, and for getting prediction results our hybrid classification model was applied. In the end, we obtained useful results indicating that there is a complicated connection between the success rate and the history of a child. This novel approach to classification, which combines multiple methods by using a hybrid model, has the potential to be implemented in computational tools for strategic decision support sys

关键词： MRI brain imaging machine learning image processing Brain structure and functionality Hybrid classification

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The JPEG AI Standard: Providing Efficient Human and machine Visual Data Consumption

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IEEE MULTIMEDIA 2023年第1期30卷 100-111页

作者： Ascenso, Joao Alshina, Elena Ebrahimi, Touradj Inst Super Tecn Inst Telecomunicacoes P-1049001 Lisbon Portugal Huawei Technol Duesseldorf GmbH D-80992 Munich Germany Ecole Polytech Fed Lausanne Multimedia Signal Proc Grp CH-1015 Lausanne Switzerland

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Telecommunication Union - Telecommunication Sector (ITU-T) for the development of the first image coding standard based on machine learning (a subset of artificial intelligence), offering a single stream, compact compressed domain representation, targeting both human visualization and machine consumption. The main motivation for this upcoming standard is the excellent performance of tools based on deep neural networks, in image coding, computer vision, and image processing tasks. The JPEG AI aims to develop an image coding standard addressing the needs of a wide range of applications such as cloud storage, visual surveillance, autonomous vehicles and devices, image collection storage and management, live monitoring of visual data, and media distribution. This article presents and discusses the rationale behind the JPEG AI vision, notably how this new standardization initiative aims to shape the future of image coding, through relevant application-driven use cases. The JPEG AI requirements, the JPEG AI history, and current status are also presented, offering a glimpse of the development of the first learning-based image coding standard.

关键词： Performance evaluation Visualization image coding Artificial intelligence Surveillance Transform coding Streaming media

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Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2023年第11-12期126卷 4763-4809页

作者： Eren, Berkay Demir, Mehmet Hakan Mistikoglu, Selcuk Iskenderun Tech Univ Dept Mechatron Engn TR-31200 Hatay Turkiye Iskenderun Tech Univ Dept Mech Engn TR-31200 Hatay Turkiye Univ Illinois Dept Mech & Ind Engn Chicago IL 60607 USA

The welding process, which is an indispensable part of the manufacturing industry, has been in demand for years and continues to attract the attention of researchers. With the transition to Industry 4.0, the welding process got out of the control of the operators and became automated with sensors and artificial intelligence methods, and as a result, it became inevitable for industrial manipulators or robots to enter the production sector. One of the most important details in making the welding process autonomous in manufacturing is the sensors, and among the sensors are the vision sensors. In recent years, it is seen that robotic welding applications are applied very sensitively and successfully when visual sense and artificial intelligence are used together. This study comprehensively reviewed research and development for cutting-edge applications using visual sensors and artificial intelligence for robotic welding applications. The processes that are the subject of intelligent robotic welding applications such as calibration, determination of welding starting point, seam tracking, and welding quality are determined and discussed based on current studies and critical analyzes. The detection, tracking, diagnosis, classification, and prediction performances of various methods of machine learning (ML), which is one of the most used areas in artificial intelligence-based applications, in welding applications are examined comparatively. This review article will help researchers about what should be considered in vision sensor aided robotic welding applications and how to contribute more to studies with artificial intelligence support.

关键词： Robotic welding vision sensing image processing Seam tracking machine learning Deep learning

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Stereo vision Meta-Lens-Assisted Driving vision

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ACS PHOTONICS 2024年第7期11卷 2546-2555页

作者： Liu, Xiaoyuan Li, Wuyang Yamaguchi, Takeshi Geng, Zihan Tanaka, Takuo Tsai, Din Ping Chen, Mu Ku City Univ Hong Kong State Key Lab Terahertz & Millimeter Waves Kowloon Hong Kong 999077 Peoples R China City Univ Hong Kong Dept Elect Engn Kowloon Hong Kong 999077 Peoples R China RIKEN Ctr Adv Photon Innovat Photon Manipulat Res Team Saitama 3510198 Japan RIKEN Cluster Pioneering Res Metamat Lab Saitama 3510198 Japan Tokushima Univ Inst PostLED Photon Tokushima 7708506 Japan City Univ Hong Kong Ctr Biosyst Neurosci & Nanotechnol Hong Kong 999077 Peoples R China Tsinghua Univ Inst Data & Informat Tsinghua Shenzhen Int Grad Sch Shenzhen 518071 Guangdong Peoples R China

Object detection and depth perception are key foundations of object tracking and machine navigation, facilitating a thorough perception and understanding of the surrounding environment. Currently, autonomous vehicles employ complex and bulky systems with high cost and energy consumption to achieve demanding multimodal vision. An imperative exists for the development of compact and reliable technology to enhance the cost-effectiveness and efficiency of autonomous driving systems. Meta-lens, a novel flat optical device, has an artificial nanoantenna array to manipulate the light properties. It is lightweight, ultrathin, and easy to integrate, making it suitable for various applications. We developed a stereo vision meta-lens imaging system for assisted driving vision, a comprehensive perception including imaging, object detection, instance segmentation, and depth information. The compact system comprises a band-pass filter, a stereo vision meta-lens, and a complementary metal oxide semiconductor (CMOS) sensor. In comparison to traditional two-camera-based stereo vision systems, the meta-lens stereo vision imaging system eliminates the need for distortion correction or camera calibration. A tailored data processing pipeline is proposed with an intensity and depth gradient cross-validation optimization mechanism and three deep learning modules for object detection, instance segmentation, and stereo matching foundations. Final assisted driving vision provides multimodal perception by integrating the raw image, instance labels, bounding boxes, segmentation masks in depth pseudo color, and depth information for each detected object. Our assisted driving vision based on a stereo meta-lens system offers a comprehensive perception for scene understanding of machines, benefiting the applications of human-computer interaction, machine navigation, autonomous driving, and augmented reality.

关键词： meta-lens stereo vision depth sensing image segmentation recognition

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Exploring image Transformations with Diffusion Models: A Survey of applications and Implementation Code 9th

Exploring Image Transformations with Diffusion Models: A Sur...

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9th Annual Conference on machine Learning, Optimization and Data science (LOD)

作者： Arellano, Silvia Otero, Beatriz Tous, Ruben Univ Politecn Cataluna Barcelona Spain

ISBN: (纸本)9783031539657;9783031539664

Diffusion Models have become increasingly popular in recent years and their applications span a wide range of fields. This survey focuses on the use of diffusion models in computer vision, specially in the branch of image transformations. The objective of this survey is to provide an overview of state-of-the-art applications of diffusion models in image transformations, including image inpainting, super-resolution, restoration, translation, and editing. This survey presents a selection of notable papers and repositories including practical applications of diffusion models for image transformations. The applications are presented in a practical and concise manner, facilitating the understanding of concepts behind diffusion models and how they function. Additionally, it includes a curated collection of GitHub repositories featuring popular examples of these subjects.

关键词： Diffusion Models image Transformations applications Computer vision Inpainting Restoration Translation Editing Super-resolution

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