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Splicing Image Forgery Detection by Deploying Deep Learning Model 1

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Splicing Image Forgery Detection by Deploying Deep Learning ...

1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Krishnamoorthy, N. Amuthadevi, C. Geedtha, M.K. Reddy, Poli Lokeshwara Anitha Rani, K.S. Gopinathan, R. Department of Masters in Computer Application SRM Institute of Science and Technology Ramapuram Campus Tamil Nadu Chennai600089 India Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Information Technology Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Tamil Nadu Chennai600062 India Department of Electronics and Communication Engineering Anurag University Telangana Hyderabad500088 India School of CSA REVA University Karnataka Bangalore560064 India Department of Mechatronics Engineering Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore641008 India

ISBN: (纸本)9781665460842

The use of digital images has become more important in numerous industries, including journalism, medical diagnosis, law enforcement, and forensics. Images could be easily modified due to the availability of photo-altering tools and software, which can distort the original information. The availability of low-cost and simple image-editing software has also drastically decreased the time and money needed to engage in image tampering. This destroys the purity of the image, making it vulnerable to misuse by anyone. Copy-move, splicing, and enhancement falsification are prominent methods used to create fake images. Using a combination of two or more images, a new image is created called splicing, which may be shared across many online channels and used to spread information and impact target audiences. The discovery could have beneficial as well as negative results. Consequently, it is important to provide a method that can identify the splicing forgery in an image. The study used a Deep Learning (DL) model named MobileNet to distinguish between authentic and manipulated images. Both the training and testing of the DL model make use of the CASIA data. Resizing and enhancing methods are used to process the collected data. Accuracy, F1 score, and recall are employed to test the reliability of the created DL model. © 2022 IEEE

关键词： Diagnosis

Privacy-Preserving Federated Learning Framework with Resisting Source Inference Attacks in Smart Healthcare

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SSRN

SSRN 2024年

作者： Chenghe, Dong Xu, Guangquan Zhang, Jianhong Bai, Hongpeng Hao, Peng College of Intelligence and Computing Tianjin University Tianjin300354 China School of Big Data Qingdao Huanghai University Qingdao China Tianjin300350 China Department of Electrical and Computer Engineering North China University of Technology Beijing100144 China Key Laboratory of Intelligent Education Technology Application of Zhejiang Province School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

Federated Learning (FL) is a distributed machine learning technology that is extensive applications in smart healthcare, since it allows multiple medical institutions to jointly train medical diagnostic models without sharing their local sensitive dataset with others. Despite its benefits, FL is susceptible to source inference attacks and model poisoning. To avoid these issues, we propose a federated learning framework for smart healthcare that preserves privacy and employs certificateless ring signatures to counteract potential attacks. During the collaborative training process of the FL model, certificateless ring signatures are used to obscure the origin of parameter updates, ensuring the anonymity of the parameter sources. This approach has the advantage of being computationally efficient due to no complicated pairing operations. Furthermore, compared with the previous certificateless ring signature schemes, our proposed scheme is more effective in reducing the success rate of source inference attacks, and is efficient in terms of computation and communication overhead. © 2024, The Authors. All rights reserved.

关键词： Federated learning

Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection

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arXiv 2024年

作者： Gan, Yuhang Xuan, Wenjie Luo, Zhiming Fang, Lei Wang, Zengmao Liu, Juhua Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China Information Technology Co. Ltd. Ningbo China

When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However, mainstream change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations. To this end, this paper explores incorporating semantic priors to improve the ability to detect changes. Firstly, we propose a Semantic-Aware Change Detection network, namely SA-CDNet, which transfers the common knowledge of the visual foundation models (i.e., FastSAM) to change detection. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features. Secondly, we design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes, which brings further increments. Specifically, we construct pseudo-change detection data from public single-temporal remote sensing segmentation datasets for large-scale pre-training, where an extra branch is also introduced for the proxy semantic segmentation task. Experimental results on five challenging benchmarks demonstrate the superiority of our method over the existing stateof- the-art methods. The code is available at SA-CD. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

Quality Prediction of Fruits using Artificial Neural Network and its application in Food science

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Quality Prediction of Fruits using Artificial Neural Network...

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： Archana Saxena Sandeep Kumar Surabhi Saxena Anant Deogaonkar Arti Goyal Shweta Goyal Deptt. of Computer Application Invertis University Bareilly Uttar Pradesh India Deptt. of Computer Science & Engg. Tula’s Institute Dehradun U.K. India School of Management Graphic Era Hill University Bhimtal Campus India School of Management Ramdeobaba University Nagpur India Deptt. of Electrical Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

The nutritional value of fruit has a significant impact on customer satisfaction. Fruit quality and costeffectiveness would enhance customer fulfillment and revenue, according to retailers. The whole supply chain requires rapid and simple techniques for assessing quality. The strawberry is a popular fruit that is valued for its distinctive taste as well as numerous health benefits, including antioxidants and helpful amino acids, which significantly improve the item’s total quality. Therefore, dividing strawberries into desired and undesired groups would help customers and startups decide if the strawberries they buy and sell are desired depending on important factors like color and form. To overcome that, Artificial Neural Networks (ANN) were used in the present research. When Multi-Linear Regression (MLR) was used, unacceptable prediction results were achieved for every variable. In contrast, it was discovered that the interior functionality attributes may be properly anticipated by applying ANNs. This was particularly true for the overall monomeric anthocyanin $(\mathrm{R} 2=0.943)$ and antioxidant potential. The current research demonstrated that utilizing color positions with ANNs may effectively assess strawberry quality.

关键词： Antioxidants Electric potential Supply chains Customer satisfaction Artificial neural networks Color Aerospace electronics Amino acids

Spectral-Spatial Dual-Branch Cross-Enhanced Transformer for Hyperspectral Image Classification

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Spectral-Spatial Dual-Branch Cross-Enhanced Transformer for ...

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hang Zhang Tianming Zhan Le Sun School of Computer Science Nanjing University of Information Science and Technology Nanjing China Jiangsu Modern Intelligent Audit Integrated Application Technology Engineering Research Center Nanjing Audit University Nanjing China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The classification of land cover based on spectral-spatial joint features is a focus in the current domain of hyperspectral image (HSI) classification. However, existing methods might not fully leverage both spectral and spatial characteristics. Additionally, approaches that separately extract and later fuse spectral and spatial features often encounter challenges with suboptimal fusion outcomes. To address these issues, we propose a spectral-spatial dual-branch cross-enhanced transformer method. This approach first utilizes distinct shallow convolutional modules tailored to the attributes of spectral and spatial data for feature extraction. Subsequently, a cross-attention strategy is designed to better align spectral and spatial features and smoothly merge these features through attention operations. Furthermore, by introducing the fused spectral-spatial features, we extend the traditional multi-head self-attention (MHSA) mechanism. This richness in feature sequences aids the model in better capturing long-range dependencies between features. The experimental results on two benchmark datasets validate the superiority of the proposed method.

关键词： Attention mechanisms Fuses Convolution Land surface Geoscience and remote sensing Feature extraction Transformers

A New Racetrack Faims Analyzer for Both High-Resolution and High-Sensitive Analysis

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SSRN

SSRN 2024年

作者： Fu, Shoushuai Wang, Chenlu Li, Junhui Yu, Jiancheng Tang, Keqi Institute of Mass Spectrometry Zhejiang Engineering Research Center of Advanced Mass spectrometry and Clinical Application Ningbo University Ningbo315211 China Zhenhai Institute of Mass Spectrometry Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China School of Material Science and Chemical Engineering Ningbo University Ningbo315211 China

A new racetrack field-asymmetric waveform ion mobility spectrometry (r-FAIMS) analyzer was developed in this study by combining the existing planar FAIMS (p-FAIMS) and cylindrical FAIMS (c-FAIMS). The ion inlet and outlet region of r-FAIMS were consisted of a half of the c-FAIMS, respectively, and these c-FAIMS were further connected by two p-FAIMS to form a racetrack shaped FAIMS. With such FAIMS working electrode configuration, ions entering the r-FAIMS can be focused and separated in the first c-FAIMS section, be further separated in the p-FAIMS section with high-resolution, be focused and separated again in the final c-FAIMS section and eventually enter the mass spectrometer or other analyzers as a compact ion packet. Detailed simulation by using SIMION with the default FAIMS user program showed that the ion focusing effect in the first c-FAIMS section ensures ions entering the following p-FAIMS section as a compact ion packet. This effectively decreases the ion loss caused by Coulomb repulsion and thermal diffusion in p-FAIMS section as compared to the ions being introduced into the p-FAIMS gap randomly in the conventional design. As a result, the ion transmission efficiency of r-FAIMS is at least 3.3-fold higher than the single p-FAIMS under the operating conditions used in this study. The ion trajectory simulation results also showed that the resolving power of r-FAIMS was about the sum of the resolving powers for its c-FAIMS and p-FAIMS sections. The resolving power of r-FAIMS is at least 3.6-fold higher than the single c-FAIMS under the operation conditions used in this study. Therefore, the r-FAIMS can realize both high-resolution and high-sensitive ion mobility separation. © 2024, The Authors. All rights reserved.

关键词： Ions

Learning from partially labeled data for multi-organ and tumor segmentation

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arXiv 2022年

作者： Xie, Yutong Zhang, Jianpeng Xia, Yong Shen, Chunhua School of Computer Science and Engineering Northwestern Polytechnical University China University of Adelaide Australia Alibaba DAMO Academy China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China Zhejiang University China

Medical image benchmarks for the segmentation of organs and tumors suffer from the partially labeling issue due to its intensive cost of labor and expertise. Current mainstream approaches follow the practice of one network solving one task. With this pipeline, not only the performance is limited by the typically small dataset of a single task, but also the computation cost linearly increases with the number of tasks. To address this, we propose a Transformer based dynamic on-demand network (TransDoDNet) that learns to segment organs and tumors on multiple partially labeled datasets. Specifically, TransDoDNet has a hybrid backbone that is composed of the convolutional neural network and Transformer. A dynamic head enables the network to accomplish multiple segmentation tasks flexibly. Unlike existing approaches that fix kernels after training, the kernels in the dynamic head are generated adaptively by the Transformer, which employs the self-attention mechanism to model long-range organ-wise dependencies and decodes the organ embedding that can represent each organ. We create a large-scale partially labeled Multi-Organ and Tumor Segmentation benchmark, termed MOTS, and demonstrate the superior performance of our TransDoDNet over other competitors on seven organ and tumor segmentation tasks. This study also provides a general 3D medical image segmentation model, which has been pre-trained on the large-scale MOTS benchmark and has demonstrated advanced performance over BYOL, the current predominant self-supervised learning method. Code will be available at https://***/DoDNet. © 2022, CC BY-NC-ND.

关键词： Convolution

Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis

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arXiv 2022年

作者： Hu, Shishuai Liao, Zehui Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China

Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR) images remains challenging, due to limited annotations and heterogeneous arteries. In this paper, we propose a semi-supervised label propagation framework to segment lumen, normal vessel walls, and atherosclerotic vessel wall on 3D MR images. By interpolating the provided annotations, we get 3D continuous labels for training 3D segmentation model. With the trained model, we generate pseudo labels for unlabeled slices to incorporate them for model training. Then we use the whole MR scans and the propagated labels to re-train the segmentation model and improve its robustness. We evaluated the label propagation framework on the CarOtid vessel wall SegMentation and atherosclerOsis diagnosiS (COSMOS) Challenge dataset and achieved a QuanM score of 83.41% on the testing dataset, which got the 1-st place on the online evaluation leaderboard. The results demonstrate the effectiveness of the proposed framework. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance

Boundary-Aware Network for Kidney Parsing

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arXiv 2022年

作者： Hu, Shishuai Ye, Yiwen Liao, Zehui Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China

Kidney structures segmentation is a crucial yet challenging task in the computer-aided diagnosis of surgery-based renal cancer. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of kidney structures on computed tomography angiography (CTA) images remains challenging, due to the variable sizes of kidney tumors and the ambiguous boundaries between kidney structures and their surroundings. In this paper, we propose a boundary-aware network (BA-Net) to segment kidneys, kidney tumors, arteries, and veins on CTA scans. This model contains a shared encoder, a boundary decoder, and a segmentation decoder. The multi-scale deep supervision strategy is adopted on both decoders, which can alleviate the issues caused by variable tumor sizes. The boundary probability maps produced by the boundary decoder at each scale are used as attention to enhance the segmentation feature maps. We evaluated the BA-Net on the Kidney PArsing (KiPA) Challenge dataset and achieved an average Dice score of 89.65% for kidney structure segmentation on CTA scans using 4-fold cross-validation. The results demonstrate the effectiveness of the BA-Net. © 2022, CC BY-NC-ND.

关键词： Tumors