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A Review on Digital image Forgery Detection 2

A Review on Digital Image Forgery Detection

引用

2nd International conference on Innovative Practices in Technology and Management, ICIPTM 2022

作者： Pradhan, Shantanu Chauhan, Usha Chauhan, S.P.S. Galgotias University Greater Noida India

ISBN: (纸本)9781665466431

The image forgery methods are utilized to give the specific picture as automated pictures without any mistakes in catching of advanced picture data which shows the first picture. While contrasted with ordinary pictures the altered pictures are hard to discover the manufactured pictures. The picture ought to keep up with the credibility and respectability to get the image from unapproved clients. In this paper, we have analyzed the computerized picture imitation and JPEG is the most widely recognized configuration utilized by the visual pictures and the advanced camera gadgets. These activities are acted in an adobe photograph shop utilizing with the substance of picture security to reestablish some computerized picture with a legitimacy and uprightness to recognize the advanced picture imitation utilizing dynamic and detached methods. © 2022 IEEE.

关键词： image classification

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Intelligent Personalized Nutrition Guidance System Using IoT and Machine Learning Algorithm 2

Intelligent Personalized Nutrition Guidance System Using IoT...

引用

2nd International conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Santhuja, P. Choudri, Subramani Roy Balaji, S. Reddy, Elangovan Guruva Muthulekshmi, M. Department of Computer Science Engineering CMR Technical Campus Telangana Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Information Technology Sri Shanmugha College of Engineering and Technology Tamil Nadu Salem India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food images in new ways to provide personalized solutions for better eating habits. The system uses machine learning, IoT, and image processing techniques to evaluate food image data and extract useful information. IoT devices like smartphones or specialized cameras take images of food and send to the cloud for analysis. The Support Vector Machine (SVM) is a machine learning technique that is integrated with Internet of Things (IoT) technology in this study to propose a novel method for creating a system that uses personalized nutritional suggestions. The SVM algorithm is then used to analyze and analyze this data to find patterns, correlations, and individual dietary requirements. A cloud database is used to process and store all the data related to food consumption. The system analyzes food images and determines the nutrition and caloric content of the food using image processing and segmentation. The system takes up new abilities using an extensive collection of dietary records that have been labeled and include details about the sorts of foods, portion sizes, and nutritional value. The system can predict and identify users' nutritional needs, deficits, and personalized dietary objectives by using this trained SVM model to their nutritional data. © 2023 IEEE.

关键词： Dietetics IoT Machine Learning Nutrition Guidance SVM Algorithm

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Generative data Augmentation of Human Biomechanics 22nd

Generative Data Augmentation of Human Biomechanics

引用

22nd International conference on image Analysis and Processing (ICIAP)

作者： Karason, Halldor Ritrovato, Pierluigi Maffulli, Nicola Tortorella, Francesco Univ Salerno Dept Informat & Elect Engn & Appl Math Salerno Italy Univ Salerno Dept Med Surg & Dent Salerno Italy

ISBN: (纸本)9783031510229;9783031510236

Wearable sensors are miniature and affordable devices used for monitoring human motion in daily life. data-driven models applied to wearable sensor data can enhance the accuracy of movement analysis outside of controlled settings. However, obtaining a large and representative database for training these models is challenging due to the specialised motion laboratories and expensive equipment required. To address this limitation, this study proposes a data augmentation approach using generative deep learning to enhance biomechanical datasets. A novel conditional generative adversarial network (GAN) was developed to synthesise biomechanical data during gait. The GAN takes into account the subject's anthropometric measures to generate data that represents specific body types as well as information about the gait cycle for reconstruction back into the time domain. The proposed model was evaluated for generating biomechanical data of unseen subjects and fine-tuning the model with small percentages (1%, 2% and 5%) of the test dataset. Researchers and practitioners can overcome the limitations of obtaining large training datasets from human participants by synthesising realistic and diverse synthetic data. This paper outlines the methodology and experimental setup for developing and evaluating the GAN and discusses its potential impact on the field of biomechanics and human motion analysis.

关键词： biomechanics deep learning wearable sensors generative adversarial networks

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Application of image Acquisition and Processing System Based on Wavelet Transform in Mechanical Product Modeling Design 2

Application of Image Acquisition and Processing System Based...

引用

2nd International conference on 3D Immersion, Interaction and Multi-Sensory Experiences, ICDIIME 2023

作者： Wan, Libo Wang, Danping Jia, Yanqin Gao, Xin Hu, Xiaolin Liu, Cheng Chengdu Technological University School of Humanities and Design Chengdu Sichuan611730 China Chengdu Technological University School of Automation and Electrical Engineering Chengdu Sichuan611730 China Chengdu Wuyu Industrial Design Co. Ltd Chengdu Sichuan611730 China

ISBN: (纸本)9798350323818

In today's highly competitive market, the product life cycle is shortened, and the product development strategy has changed from product-driven to market-driven. Therefore, enterprises seek to develop new products and pay more and more attention to product style. The modeling design of mechanical products mainly emphasizes meeting the needs of people and society, making beautiful, elegant and exquisite products serve people's life and production activities, and improving the level of material civilization and spiritual civilization of the whole society. In this paper, an image acquisition and processing model based on wavelet transform (WT) algorithm is proposed to define the modeling parameters of mechanical products and automatically generate new product modeling. The simulation results show that this algorithm is more accurate for feature recognition of mechanical product modeling image, which is 25.77% higher than the traditional algorithm, and can locate the edge contour of mechanical product modeling image more accurately. The image data after WT can keep the fine structure of the original image at various resolutions, which provides convenience for further removing other forms of redundant information in the image. This method provides an automatic design system that enables designers to quickly obtain product modeling and its corresponding images or find the image requirements in a short time of ideal modeling. © 2023 IEEE.

关键词： Wavelet transforms

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Evaluating Cyberbullying Detection Algorithm Performance in Text and image Analysis 2

Evaluating Cyberbullying Detection Algorithm Performance in ...

引用

2nd International conference on Machine Learning, Control, and Robotics, MLCR 2023

作者： Ea, Philippe Xiang, Jiahui Salem, Osman Mehaoua, Ahmed Université Paris Cité Centre Borelli Umr 9010 Paris France

ISBN: (纸本)9798350381245

Cyberbullying has become a widespread concern in our society, and its detection has grown in significance. In recent years, machine learning algorithms have exhibited considerable potential in detecting cyberbullying. This paper presents an approach for detecting it employing Natural Language Processing and image analysis methods to analyze both textual and visual content. The study utilized an online comment dataset from Twitter and a self-created image dataset, where labeling was done for cyberbullying and non-cyberbullying, while tweets underwent detailed labeling. Multiple models were tested to determine the most suitable one. The Support Vector Machine model achieved an accuracy rate of 81.4% on our text dataset, according to our experimental results. The model achieved a remarkable performance with Area Under the Curve scores exceeding 0.97 in classifying comments based on age, ethnicity, gender, and religion. For cyberbullying detection in image data, pre-trained models with CNN architecture were utilized. Regrettably, our experiments in this regard failed to produce satisfactory outcomes, possibly due to varying interpretations of visual content among individuals. We will further discuss this issue in our paper. Overall, our approach offers a potentially effective solution for identifying instances of cyberbullying in textual content, which could be utilized in the development of tools and systems aimed at preventing cyberbullying and promoting online safety. © 2023 IEEE.

关键词： image classification

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Secure 'text in image' steganography scheme based on Alexnet and contour-let transform 2

Secure 'text in image' steganography scheme based on Alexnet...

引用

2nd International conference on Artificial Intelligence and Signal Processing, AISP 2022

作者： Srinivasu, Lingamallu Naga Veeramani, Vijayaraghavan Department of Electronics and Communication Engineering Vignan'sFoundation for Science Technolog and Research Vadlamudi Andhra Pradesh Guntur522213 India

ISBN: (纸本)9781665442909

Nowadays, 'text in image' steganography is utilised in a variety of applications like military, surveillance, and remote sensing etc., in order to keep the secret information secure. This paper is presenting the unique steganography algorithm for improving data security, visual quality, quality metrics and withstand the attacks of the stego image. In proposed algorithm, the security of the confidential information is improved by utilizing the SLICE encryption algorithm. It is used to generate the cipher data from the confidential data. The alexnet is introduced in this paper to detect the facial area in human cover image. A total of 1987 images are used to train the network and got maximum accuracy. Contour-let transform is utilized to decomposition of alexnet output. The "random pixel embedding" (RPE) technique is utilized to embed the confidential data in facial area of the sub-band. The combination of alexnet and contour-let transform is used to generate good visual quality of the stego image. The proposed algorithm produces a stego image that has better visual quality, security, metric-values and withstand attacks compared to recent methods. © 2022 IEEE.

关键词： Cryptography

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An Effective image Enhancement Algorithm for Single image Haze Removal Based on Daubechies Wavelet Filter Bank 2nd

An Effective Image Enhancement Algorithm for Single Image H...

引用

2nd International conference on Advances in data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Samantaray, Aswini Kumar Asritha, D. Lakshmi Prasanna, N. Vijaya Lakshmi, T. Sree Jaitha, P. Sai, Siva Department of ECE VFSTR Guntur India

ISBN: (纸本)9789819995301

The primary issues in producing natural images are low contrast and poor quality. A novel approach for image enhancement is proposed in this work employing the discrete wavelet transform, adaptive thresholding, and morphology-based method. To start, pre-processing procedures are used to preserve the fine features of an image. After that, Daubechies-2 (tap-4) wavelet filter bank is used to split the image into high-pass and low-pass subband images. images from the high-pass subband are improved using an adaptive thresholding technique. The morphology-based top-hat transform is used to improve low-pass subband images. Once the high-frequency and low-frequency subimages have been processed, the enhanced image can be obtained by utilizing the inverse DWT approach. PSNR and RMSE are used to assess the extent to which the suggested method performs. Experiments revealed that this technique is excellent at both enhancing an image’s details and effectively preserving its edge features. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image enhancement

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Deep Learning: Convolutional Neural Networks for Medical image Analysis - A Quick Review 2

Deep Learning: Convolutional Neural Networks for Medical Ima...

引用

2nd International conference on Digital Futures and Transformative Technologies, ICoDT2 2022

作者： Sharif, Hanan Rehman, Faisal Rida, Amina Lahore Leads University Department of Computer Science & Information Technology Lahore Pakistan

ISBN: (纸本)9781665498197

Deep learning has quickly evolved over the past several years from a promising to a feasible solution for medical image analysis. This is a great topic for research because more and more people are using medical imaging to treat and diagnose. A significant benefit of deep learning is the ability to use enormous volumes of data to eliminate the painstaking hand-crafting of features, which needs strong domain expertise. This study discusses how convolutional neural networks (CNNs) are utilized in the field, including detection, classification, registration, segmentation, and picture enhancement. It also gives some important information about how CNNs can be used to analyze medical images of the brain, eye, breast, chest, and skin. © 2022 IEEE.

关键词： image classification

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The Status and Challenges of image data Augmentation Algorithms 2

The Status and Challenges of Image Data Augmentation Algorit...

引用

2nd International conference on Robotics, Automation and Intelligent Control, ICRAIC 2022

作者： Wei, Jian Wang, Qinzhao Song, Xiaoqing Zhao, Zixu Army Academy of Armored Forces Beijing China

image data-augmentation algorithms effectively trump the problem of insufficient training samples for deep learning in some application fields, and it is typically for scholars to choose some of them for various computer vision tasks. But as the algorithms develop rapidly, the early proposed classification that the data-augmentation algorithms are sorted into classical ways and generating methods is no more suitable, because such classification misses some other meaningful strategies. Besides, it is frustrating for someone to decide which is the exact method to undertake, though there are too many optional algorithms to choose. Towards the goal of making some suggestions, the paper categorizes image data-augmentation algorithms into three kinds from the perspective of algorithm strategy, and they are matrix transformation algorithm, feature expansion algorithm and model generation based on neural network algorithm. The paper analyzes the typical algorithm principle, performance, application scenarios, research status and future challenges, and forecasts the development trend of data augmentation algorithms. The paper can provide academic reference for data augmentation algorithm in the fields of medicine and military. © Published under licence by IOP Publishing Ltd.

关键词： Deep learning

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The Face Generation for Emotion Analysis Using Deep Neural Networks 2

The Face Generation for Emotion Analysis Using Deep Neural N...

引用

2nd IEEE International conference on Futuristic Technologies, INCOFT 2023

作者： Singh, Preetjot Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350308846

Facial expression recognition (FER) using deep learning from short training sets remains challenging despite its significance to human-machine interactions. This study provides a unique generative adversarial network approach for transforming expressionless face photographs into those depicting a range of emotions to address the problem of insufficient training data for deep learning for FER. A novel GAN architecture is devised, using a generator with two coding devices with two decoders, two discriminators, and a feature extractor, to combat the distortions and overfitting that may result from training GANs on small training datasets. In order to accomplish this, the proposed GAN architecture makes use of the mechanism of a feature map to discover regional include deviations among images in both the source and target domain, which not only allows it to generate appealing facial expression pictures, but also to preserve the initial signs in input neutral pictures of faces. The suggested GAN was utilised to add photographs with up to seven distinct face expressions to the training dataset, which improved the FER accuracy of the deep neural networks tested. © 2023 IEEE.

关键词： convolutional neural network data augmentation facial expression recognition generative adversarial network machine learning synthetic image database

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