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image Manipulation Detection and Classification Using SIFT and SURF with Support Vector Machine Classifier

Image Manipulation Detection and Classification Using SIFT a...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE International conference on

作者： Ghazi Mohamad Ramadan Zamen Latef Naser Sripelli Jagadish Rekha Phadke Muntather Almusawi College of MLT Ahl Al Bayt University Karbala Iraq National University of Science and Technology Dhi Qar Iraq School of Computer Science & Artificial Intelligence SR University Warangal Telangana India Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India The Islamic University Najaf Iraq

Enhance digital trust and integrity by mastering image forgery detection in machine learning. Uncover the hidden truth behind visuals, ensuring authenticity in a world inundated with digital content. Various image manipulation detection machines have been innovated in the past, but these models face limitations like demanding domain expertise, hyperparameter tuning, time-consuming processes, and overlapping issues. Initially employ the histogram equalization to enhance dataset images, reducing unwanted noise and generating synthetic data for class balance. The pre-processed images are then fed into the model for ongoing analysis. In this paper, propose a Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF)-based SVM for image manipulation detection and classification, achieving outstanding performance metrics on the CASIA 1.0 dataset: 0.96 accuracy, 0.8120 IOU score, 0.7710 MCC, 0.8850 F1 score, 0.98 precision, and 0.92 sensitivity. This model surpasses the capabilities of existing models, including the Two-Stream Boundary-aware Network, Discrete Cosine Transformation (DCT)-based multitask learning network, and Multilevel Local Binary pattern Texture Descriptor.

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An Identification Method of image-based for Output pattern of Metamorphic Relation in Burnup Calculation Program 3

An Identification Method of Image-based for Output Pattern o...

引用

3rd International Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Fu, Shengfu Yang, Xiaohua Li, Meng Wang, Fan Eng. and Technol. Res. Ctr. of Software Eval. and Test. for Intellectual Equipment of Hunan Province Hunan Prov. Hengyang421001 China CNNC Key Laboratory on High Trusted Computing Hunan Prov. Hengyang421001 China School of Computer Science and Technology University of South China Hunan Hengyang421001 China

ISBN: (纸本)9781665402675

It is challenging to verify the design and analysis software adequately for nuclear power areas because of the inflated cost, time-consuming benchmark problems development, and few verification examples. This situation is called the test oracle problem. Metamorphic testing cleverly combines the specific properties of physical equations and numerical algorithms with software verification. Without constructing numerical solutions or verification examples, it verifies the software by checking whether the input and output of multiple executions meet the metamorphic relations. Since metamorphic relation is the nature rules of physical models and numerical algorithms, it is not restricted to specific code implementation technology. Therefore, it is no doubt that metamorphic testing is a promising technique to alleviate the test oracle *** relation is the key to metamorphic testing, which consists of input pattern and output pattern. The former can be obtained through manual analysis by industry experts, while the latter does not have a system identification method. An output pattern identification method based on the solution figure has been introduced here. Specifically, the input pattern is received by artificially analyzing the physical property of the program under test. Next, a group of test inputs has been generated according to such patterns. The calculation results are received after those inputs drive the program. Moreover, the function form of the output pattern is guided by the image of the results. Finally, metamorphic relation is constructed by input pattern and output pattern. For a demonstration of this method's details, a nuclide burnup calculation code is employed here, namely NUIT. By investigating the resulting image of 3820 isotopes, their output pattern classification models have been established according to the burnup depth and nuclide density. It includes four categories and seventeen sub-categories. The verification of other burnup

关键词： Verification

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Face Morphing Attack Detection in the Presence of Post-processed image Sources Using Neighborhood Component analysis and Decision Tree Classifier 3rd

Face Morphing Attack Detection in the Presence of Post-proce...

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3rd International conference on Information and Communication Technology and Applications, ICTA 2020

作者： Kenneth, Ogbuka Mary Bashir, Sulaimon Adebayo Abisoye, Opeyemi Aderiike Mohammed, Abdulmalik Danlami Department of Computer Science Federal University of Technology Minna Nigeria

ISBN: (纸本)9783030691424

Recently, Face Morphing Attack Detection (MAD) has gained a great deal of attention as criminals have started to use freely and easily available digital manipulation techniques to combine two or more subject facial images to create a new facial image that can be viewed as an accurate image of any of the individual images that constitute it. Some of these morphing tools create morphed images of high quality which pose a serious threat to existing Face recognition Systems (FRS). In the literatures, it has been identified that FRS is vulnerable to multiform morphing attacks. Based on this vulnerability, several types of research on the detection of this morph attack was conducted using several techniques. Despite the remarkable levels of MAD reported in various literature, so far no suitable solution has been found to handle post-processed images such as images modified after morphing with sharpening operation that can dramatically reduce visible artifacts of morphed photos. In this work, an approach is proposed for MAD before image post-processing and after image post-processing built on a combination of Local Binary pattern (LBP) for extraction of feature, Neighborhood Component analysis (NCA) for selection of features and classification using K-Nearest Neighbor (KNN), Decision Tree Classifier (DTC) and Naïve Bayes (NB) classifier. The outcome gotten by training the different classifiers with feature vectors selected using the NCA algorithm improved the classification accuracy from 90% to 94%, consequently improving the general performance of the MAD. © 2020, Springer Nature Switzerland AG.

关键词： Local binary pattern

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Design and Research on Visual recognition System for Fruit Sorting Collaborative Robot

Design and Research on Visual Recognition System for Fruit S...

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Networking Systems of AI (INSAI), International conference on

作者： Yingchen Ma Hanan Zhu Hongli Jia Chunlai Guo Jiaqi Li Zhe Li College of Intelligent Engineering Harbin Institute of Petroleum Harbin China College of Intelligent Engineering Heilongjiang Agricultural Engineering Vocational College Harbin China IE Technology Research Center Harbin Cambridge University Harbin China

ISBN: (数字)9798350344660

ISBN: (纸本)9798350344677

This study aimed to enhance the efficiency and accuracy of fruit sorting through the development of a collaborative robotic vision recognition system based on neural networks. Leveraging convolutional neural networks (CNNs), a substantial dataset of fruit images was collected, preprocessed, and utilized for training. The core algorithm employed was the neural network, particularly effective in pattern recognition and feature extraction. Integration with the control system of collaborative robots allowed real-time fruit identification and analysis using visual sensors during sorting. The system's design considered optimization and acceleration of neural networks, incorporating advanced deep learning techniques like CNNs and recurrent neural networks. This fusion of collaborative robots, neural networks, and visual recognition technologies significantly improved fruit sorting efficiency and accuracy. The automated sorting process achieved through thoughtful system design and optimization reduces labor costs and enhances productivity. Despite notable progress in the design and research of collaborative robotic and neural network-based vision recognition systems, challenges and areas for improvement persist. Future work may involve further optimizing neural network models for increased precision and robustness and refining collaborative robot control strategies for more complex task collaboration. The collaborative robotic and neural network-based vision recognition system opens new possibilities in the field of fruit sorting, propelling the industry towards intelligent and efficient practices, thereby creating greater economic and societal value.

关键词： Visualization Accuracy image recognition Service robots Machine vision Neural networks Collaborative robots Real-time systems Convolutional neural networks Sorting

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MicrogliaJ: An Automatic Tool for Microglial Cell Detection and Segmentation 11th

MicrogliaJ: An Automatic Tool for Microglial Cell Detection...

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11th iberian conference on pattern recognition and image analysis, IbPRIA 2023

作者： Casado-García, Ángela Carlos, Estefanía Domínguez, César Heras, Jónathan Izco, María Mata, Eloy Pascual, Vico Álvarez-Erviti, Lydia Department of Mathematics and Computer Science University of La Rioja Logroño26004 Spain Piqueras 98 3th floor Logroño26006 Spain

ISBN: (纸本)9783031366154

Microglial cells are now recognized as crucial players in the development of neurodegenerative diseases. The analysis and quantification of microglia changes is necesary to better understand the contribution of microglia in neurodegenerative processes or drug treatments. However, the manual quantification of microglial cells is a time-consuming and subjective tasks;and, therefore, reliable tools that automate this process are desirable. In this paper, we present MicrogliaJ, an imageJ macro, that can measure both the number and area of microglial cells. The automatic procedure implemented in MicrogliaJ is based on classical image processing techniques, and the results can be manually validated by experts with a simple-to-use interface. MicrogliaJ has been tested by experts and it obtains analogous results to those manually produced, but considerably reducing the time required for such analysis. Thanks to this work, the analysis of microglia images will be faster and more reliable, and this will help us to advance our understanding of the behaviour of microglial cells. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Neurodegenerative diseases

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Keywords recognition from EEG Signals on Smart Devices a Novel Approach 3rd

Keywords Recognition from EEG Signals on Smart Devices a Nov...

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

作者： Bedre, Sushil Pandharinath Jha, Subodh Kumar Borde, Prashant Patil, Chandrakant Gawali, Bharati Yannawar, Pravin Vision and Intelligent System Laboratory Dr. Babasaheb Ambedkar University Aurangabad Maharashtra431004 India SCMRL Lab Dr. Babasaheb Ambedkar University Aurangabad Maharashtra431004 India

ISBN: (纸本)9789811604928

The Advancement of communication system has given us the freedom to think beyond traditional communication system and stage is set for thought oriented communication system. There are thousands of thoughts generated and vanished in a timeframe but out of these some prominent thoughts persist and we proceed with the same in our day to day activities. The advancement in Electroencephalogram has provided a chance to see the activity in the human brain in non-invasive manner. The proposed research work presents the method for Digit recognition using the EEG signals acquired and processed on smart devices. The results show the implementation of Computation neural network for the recognition of digits from EEG signals. It was seen that, the 90.64% correct classification was achieved. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Independent component analysis

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Discriminative BULBPH Descriptor with KDA for Palmprint recognition 3rd

Discriminative BULBPH Descriptor with KDA for Palmprint Reco...

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3rd International conference on Computer Vision and image Processing (CVIP)

作者： Tamrakar, Deepti Khanna, Pritee Jabalpur Engn Coll Jabalpur India PDPM Indian Inst Informat Technol Design & Mfg Jabalpur India

ISBN: (纸本)9789813290884;9789813290877

This work proposes Block-wise uniform local binary pattern histogram (BULBPH) followed by kernel discrimination analysis (KDA) as descriptor for palmprint recognition. BULBPH provides distribution of uniform patterns (such as line and wrinkles) in local region and can be better used as palmprint features. KDA is applied on BULBPH to reduce dimension and enhance discriminative capability using chi-RBF kernel. The experiments are conducted on four palmprint databases and performance is compared with related descriptors. It is observed that KDA on BULBPH descriptor achieves more than 99% accuracy with 4.04 decidability index on four palmprint databases.

关键词： Palmprint Blockwise uniform local binary pattern Kernel discriminant analysis

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Detection of Falsary Happening on Social Media Using image Processing: Feature Extraction and Matching 3rd

Detection of Falsary Happening on Social Media Using Image P...

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3rd International conference on Recent Trends in image Processing and pattern recognition, RTIP2R 2020

作者： Tatkare, Kshipra Ashok Devare, Manoj Ramrao Adik Institute of Technology Nerul Navi Mumbai India Amity University Panvel Mumbai India

ISBN: (纸本)9789811605062

In last decade and half, especially generation Z (Gen Z) has witnessed a drastic change in their communication pattern due to paradigm shift in digital media. Social media is the largest medium to connect any part of the world via internet is considered to be the trend changer. Increasing use of mobile internet and easy availability of mobile compatible softwares have changed the way of expression on social media in both apt and inept manner. Forged images known as memes are, nowadays, trending on social media like Facebook, Instagram, Twitter, Whatsapp and what not. These memes on social media are used to appreciate or to troll on particular cause to someone or group of people. The plethora of memes usage leads to image processing where feature extraction and feature matching can be done. Matching of features that are invariant to transformation includes geometric invariance and photometric invariance. This paper presents the comparative analysis of images (memes) matching algorithms of feature, which will definitely helpful for future researchers to identify the best matching technique to be fit in desired area. Authors have also included the implemented results of various traditional matching techniques invariant to translation, rotation, scaling, brightness and exposure. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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Adaptive Scaling for Archival Table Structure recognition 16th

Adaptive Scaling for Archival Table Structure Recognition

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16th IAPR International conference on Document analysis and recognition (ICDAR)

作者： Li, Xiao-Hui Yin, Fei Zhang, Xu-Yao Liu, Cheng-Lin Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing Peoples R China

ISBN: (纸本)9783030865498

Table detection and structure recognition from archival document images remain challenging due to diverse table structures, complex document layouts, degraded image qualities and inconsistent table scales. In this paper, we propose an instance segmentation based approach for archival table structure recognition which utilizes both foreground cell content and background ruling line information. To overcome the influence from inconsistent table scales, we design an adaptive image scaling method based on average cell size and density of ruling lines inside each document image. Different from previous multi-scale training and testing approaches which usually slow down the speed of the whole system, our adaptive scaling resizes each image to a single optimal size which can not only improve overall model performance but also reduce memory and computing overhead on average. Extensive experiments on cTDaR 2019 Archival dataset show that our method can outperform the baselines and achieve new state-of-the-art performance, which demonstrates the effectiveness and superiority of the proposed method.

关键词： Archival document Table detection Table structure recognition Adaptive scaling

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A Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and image Processing Techniques 28th

A Review of Detecting and Quantification of Cracks Using ...

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28th International conference on image Processing, Computer Vision, and pattern recognition, IPCV 2024, and 23rd International conference on Information and Knowledge Engineering, IKE 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

作者： Mokhlis, Shaikha H. Alhomoud, Alanoud A. Alshawi, Reema I. Alhazzani, Shatha K. Alhazzaa, Lamees A. College of Computer and Information Sciences Al-Imam Mohammed ibn Saud Islamic University Riyadh11911 Saudi Arabia

ISBN: (纸本)9783031859328

Crack detection plays a crucial role in ensuring the safety and integrity of various structures, including buildings, bridges, and roadways. Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated crack detection, offering the potential for accurate and efficient analysis. In this review paper, we conduct a brief analysis of CNN architectures employed for crack detection, focusing on their comparative performance, application domains, and future research directions. By examining a collection of 20 relevant studies, we identify and evaluate the utilization frequency of popular CNN architectures, namely AlexNet, GoogleNet, ResNet, VGG-16, YOLO v3, and YOLO v4, in the context of crack detection. This review offers valuable insights into the prevalence, usage patterns, and performance of CNN architectures in crack detection. It serves as a guide for researchers and practitioners in developing effective CNN-based crack detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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