Nowadays, the internet has become a typical medium for sharing digital images through web applications or social media and there was a rise in concerns about digital image privacy. Image editing software’s have prepared it incredibly simple to make changes to an image's content without leaving any visible evidence for images in general and medical images in particular. In this paper, the COVID-19 digital x-rays forgery classification model utilizing deep learning will be introduced. The proposed system will be able to identify and classify image forgery (copy-move and splicing) manipulation. Alexnet, Resnet50, and Googlenet are used in this model for feature extraction and classification, respectively. Images have been tampered with in three classes (COVID-19, viral pneumonia, and normal). For the classification of (Forgery or no forgery), the model achieves 0.9472 in testing accuracy. For the classification of (Copy-move forgery, splicing forgery, and no forgery), the model achieves 0.8066 in testing accuracy. Moreover, the model achieves 0.796 and 0.8382 for 6 classes and 9 classes problems respectively. Performance indicators like Recall, Precision, and F1 Score supported the achieved results and proved that the proposed system is efficient for detecting the manipulation in images.
Designing of Application for Detection of Face Mask and Social Distancing Dur...IRJET Journal
This document proposes a system to detect whether people are wearing face masks and maintaining social distancing during the COVID-19 pandemic using computer vision algorithms. The system uses YOLO v3 for object detection to detect people and faces in frames. A CNN model is then used to classify whether faces are wearing masks or not. Social distancing is measured by calculating the Euclidean distance between detected face boxes. The system is intended to help enforce COVID safety protocols and reduce cases by automatically monitoring compliance. It analyzes video frames to label faces as masked or unmasked and issue notifications if people are too close. The proposed application aims to assist governments in controlling the pandemic through machine learning-based social distancing and mask detection.
Medical Image Fusion Using Discrete Wavelet TransformIJERA Editor
Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multimodal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. The domain where image fusion is readily used nowadays is in medical diagnostics to fuse medical images such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and MRA. This paper aims to present a new algorithm to improve the quality of multimodality medical image fusion using Discrete Wavelet Transform (DWT) approach. Discrete Wavelet transform has been implemented using different fusion techniques including pixel averaging, maximum minimum and minimum maximum methods for medical image fusion. Performance of fusion is calculated on the basis of PSNR, MSE and the total processing time and the results demonstrate the effectiveness of fusion scheme based on wavelet transform.
Image Forgery Detection Methods- A ReviewIRJET Journal
This document reviews various methods for detecting image forgery. It begins with an introduction to the topic, explaining the need for image forgery detection techniques due to the widespread manipulation of images online. It then categorizes common types of image manipulation and provides a literature review comparing the accuracy and citations of different detection techniques, such as CNN-based methods, transform-domain methods using DCT and DWT, and methods analyzing JPEG compression artifacts. The review finds that CNN-based methods generally achieve the highest accuracy, around 90-100%, but also notes transform-domain and JPEG-based methods can also achieve reasonably high accuracy ranging from 70-100% depending on the technique and testing parameters.
This document summarizes techniques for detecting tampered or forged digital images. It discusses both active techniques that require prior information like watermarks, and passive/"blind" techniques that can detect forgeries without prior info by analyzing inconsistencies in statistical image properties introduced during tampering. Specific techniques mentioned include detecting inconsistencies in noise levels, color filter array properties, JPEG compression quality, and identifying copy-move or image splicing operations. The document also reviews several papers on techniques like analyzing demosaicing patterns, noise analysis, SIFT feature clustering, and alpha matting.
This document summarizes techniques for detecting tampered digital images. It discusses passive ("blind") methods that detect forgeries by analyzing the statistical properties and digital fingerprints of images without prior knowledge. These techniques examine inconsistencies introduced during tampering that alter the image's noise, compression, color, and other attributes. The document also outlines different types of forgeries like copy-move, splicing, retouching, and techniques using JPEG compression and lighting analysis. It reviews papers on demosaicing regularity detection and noise variation analysis for passive forgery identification.
ROBUSTNESS EVALUATION OF WATERMARKING BASED ON THE HARRIS PRINCIPLEijcisjournal
In the field of health, the messages conveyed by images have a considerable impact on the life of patients. In
order to facilitate decision support on medical imaging, we will present in this paper the method of digital
image watermarking based on the Harris principle whose objective is to hide the data in a medical image in
order to evaluate its robustness. In addition, it ensures that the patient's image is authentic for a better
diagnosis. To carry out this work we decided to start on the basis of two principles namely those of Moravec
and Harris to obtain a solution that meets the need for digital watermarking. They consist in taking a random
image (medical or not) and extract these points of interest and we obtain a mark, then take the original image
of the patient that we add to the mark to obtain a digital image watermark. This watermark is invisible since
all the invisibility properties are respected.
ROBUSTNESS EVALUATION OF WATERMARKING BASED ON THE HARRIS PRINCIPLEijcisjournal
In the field of health, the messages conveyed by images have a considerable impact on the life of patients. In
order to facilitate decision support on medical imaging, we will present in this paper the method of digital
image watermarking based on the Harris principle whose objective is to hide the data in a medical image in
order to evaluate its robustness. In addition, it ensures that the patient's image is authentic for a better
diagnosis. To carry out this work we decided to start on the basis of two principles namely those of Moravec
and Harris to obtain a solution that meets the need for digital watermarking. They consist in taking a random
image (medical or not) and extract these points of interest and we obtain a mark, then take the original image
of the patient that we add to the mark to obtain a digital image watermark. This watermark is invisible since
all the invisibility properties are respected.
ROBUSTNESS EVALUATION OF WATERMARKING BASED ON THE HARRIS PRINCIPLEijcisjournal
In the field of health, the messages conveyed by images have a considerable impact on the life of patients. In
order to facilitate decision support on medical imaging, we will present in this paper the method of digital
image watermarking based on the Harris principle whose objective is to hide the data in a medical image in
order to evaluate its robustness. In addition, it ensures that the patient's image is authentic for a better
diagnosis. To carry out this work we decided to start on the basis of two principles namely those of Moravec
and Harris to obtain a solution that meets the need for digital watermarking. They consist in taking a random
image (medical or not) and extract these points of interest and we obtain a mark, then take the original image
of the patient that we add to the mark to obtain a digital image watermark. This watermark is invisible since
all the invisibility properties are respected
Designing of Application for Detection of Face Mask and Social Distancing Dur...IRJET Journal
This document proposes a system to detect whether people are wearing face masks and maintaining social distancing during the COVID-19 pandemic using computer vision algorithms. The system uses YOLO v3 for object detection to detect people and faces in frames. A CNN model is then used to classify whether faces are wearing masks or not. Social distancing is measured by calculating the Euclidean distance between detected face boxes. The system is intended to help enforce COVID safety protocols and reduce cases by automatically monitoring compliance. It analyzes video frames to label faces as masked or unmasked and issue notifications if people are too close. The proposed application aims to assist governments in controlling the pandemic through machine learning-based social distancing and mask detection.
Medical Image Fusion Using Discrete Wavelet TransformIJERA Editor
Medical image fusion is the process of registering and combining multiple images from single or multiple imaging modalities to improve the imaging quality and reduce randomness and redundancy in order to increase the clinical applicability of medical images for diagnosis and assessment of medical problems. Multimodal medical image fusion algorithms and devices have shown notable achievements in improving clinical accuracy of decisions based on medical images. The domain where image fusion is readily used nowadays is in medical diagnostics to fuse medical images such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and MRA. This paper aims to present a new algorithm to improve the quality of multimodality medical image fusion using Discrete Wavelet Transform (DWT) approach. Discrete Wavelet transform has been implemented using different fusion techniques including pixel averaging, maximum minimum and minimum maximum methods for medical image fusion. Performance of fusion is calculated on the basis of PSNR, MSE and the total processing time and the results demonstrate the effectiveness of fusion scheme based on wavelet transform.
Image Forgery Detection Methods- A ReviewIRJET Journal
This document reviews various methods for detecting image forgery. It begins with an introduction to the topic, explaining the need for image forgery detection techniques due to the widespread manipulation of images online. It then categorizes common types of image manipulation and provides a literature review comparing the accuracy and citations of different detection techniques, such as CNN-based methods, transform-domain methods using DCT and DWT, and methods analyzing JPEG compression artifacts. The review finds that CNN-based methods generally achieve the highest accuracy, around 90-100%, but also notes transform-domain and JPEG-based methods can also achieve reasonably high accuracy ranging from 70-100% depending on the technique and testing parameters.
This document summarizes techniques for detecting tampered or forged digital images. It discusses both active techniques that require prior information like watermarks, and passive/"blind" techniques that can detect forgeries without prior info by analyzing inconsistencies in statistical image properties introduced during tampering. Specific techniques mentioned include detecting inconsistencies in noise levels, color filter array properties, JPEG compression quality, and identifying copy-move or image splicing operations. The document also reviews several papers on techniques like analyzing demosaicing patterns, noise analysis, SIFT feature clustering, and alpha matting.
This document summarizes techniques for detecting tampered digital images. It discusses passive ("blind") methods that detect forgeries by analyzing the statistical properties and digital fingerprints of images without prior knowledge. These techniques examine inconsistencies introduced during tampering that alter the image's noise, compression, color, and other attributes. The document also outlines different types of forgeries like copy-move, splicing, retouching, and techniques using JPEG compression and lighting analysis. It reviews papers on demosaicing regularity detection and noise variation analysis for passive forgery identification.
ROBUSTNESS EVALUATION OF WATERMARKING BASED ON THE HARRIS PRINCIPLEijcisjournal
In the field of health, the messages conveyed by images have a considerable impact on the life of patients. In
order to facilitate decision support on medical imaging, we will present in this paper the method of digital
image watermarking based on the Harris principle whose objective is to hide the data in a medical image in
order to evaluate its robustness. In addition, it ensures that the patient's image is authentic for a better
diagnosis. To carry out this work we decided to start on the basis of two principles namely those of Moravec
and Harris to obtain a solution that meets the need for digital watermarking. They consist in taking a random
image (medical or not) and extract these points of interest and we obtain a mark, then take the original image
of the patient that we add to the mark to obtain a digital image watermark. This watermark is invisible since
all the invisibility properties are respected.
ROBUSTNESS EVALUATION OF WATERMARKING BASED ON THE HARRIS PRINCIPLEijcisjournal
In the field of health, the messages conveyed by images have a considerable impact on the life of patients. In
order to facilitate decision support on medical imaging, we will present in this paper the method of digital
image watermarking based on the Harris principle whose objective is to hide the data in a medical image in
order to evaluate its robustness. In addition, it ensures that the patient's image is authentic for a better
diagnosis. To carry out this work we decided to start on the basis of two principles namely those of Moravec
and Harris to obtain a solution that meets the need for digital watermarking. They consist in taking a random
image (medical or not) and extract these points of interest and we obtain a mark, then take the original image
of the patient that we add to the mark to obtain a digital image watermark. This watermark is invisible since
all the invisibility properties are respected.
ROBUSTNESS EVALUATION OF WATERMARKING BASED ON THE HARRIS PRINCIPLEijcisjournal
In the field of health, the messages conveyed by images have a considerable impact on the life of patients. In
order to facilitate decision support on medical imaging, we will present in this paper the method of digital
image watermarking based on the Harris principle whose objective is to hide the data in a medical image in
order to evaluate its robustness. In addition, it ensures that the patient's image is authentic for a better
diagnosis. To carry out this work we decided to start on the basis of two principles namely those of Moravec
and Harris to obtain a solution that meets the need for digital watermarking. They consist in taking a random
image (medical or not) and extract these points of interest and we obtain a mark, then take the original image
of the patient that we add to the mark to obtain a digital image watermark. This watermark is invisible since
all the invisibility properties are respected
Deep learning based masked face recognition in the era of the COVID-19 pandemicIJECEIAES
The document describes two deep learning models for masked face recognition during the COVID-19 pandemic. The first model is based on the pre-trained MobileNetv2 convolutional neural network. The second model is a new CNN architecture developed for this task. Both models classify faces into three categories: correct mask, incorrect mask, and no mask. The models are evaluated on a masked face detection dataset from Kaggle. The pre-trained and new CNN models are compared to determine the most accurate approach for masked face recognition.
FaceDetectionforColorImageBasedonMATLAB.pdfAnita Pal
The document proposes a method for face detection in color images using MATLAB and a convolutional neural network (CNN) with two layers. It begins with an introduction to face detection technologies and challenges. It then describes the proposed methodology, which involves reading a color image, applying CNN convolutions to detect faces, and fusing the results. Mathematical aspects of CNNs are discussed, including equations for convolutional operations and dimensions. The CNN method combines feature extraction, segmentation, and classification. Finally, the document summarizes how the proposed algorithm was designed in MATLAB to identify faces using a simple learning algorithm and CNNs with fewer layers than traditional networks. The goal is to develop a fast face detection program for color images based on new deep learning standards.
Design secure multi-level communication system based on duffing chaotic map a...IJEECSIAES
Cryptography and steganography are among the most important sciences that have been properly used to keep confidential data from potential spies and hackers. They can be used separately or together. Encryption involves the basic principle of instantaneous conversion of valuable information into a specific form that unauthorized persons will not understand to decrypt it. While steganography is the science of embedding confidential data inside a cover, in a way that cannot be recognized or seen by the human eye. This paper presents a high-resolution chaotic approach applied to images that hide information. A more secure and reliable system is designed to properly include confidential data transmitted through transmission channels. This is done by working the use of encryption and steganography together. This work proposed a new method that achieves a very high level of hidden information based on non-uniform systems by generating a random index vector (RIV) for hidden data within least significant bit (LSB) image pixels. This method prevents the reduction of image quality. The simulation results also show that the peak signal to noise ratio (PSNR) is up to 74.87 dB and the mean square error (MSE) values is up to 0.0828, which sufficiently indicates the effectiveness of the proposed algorithm.
Design secure multi-level communication system based on duffing chaotic map a...nooriasukmaningtyas
This document proposes a new method for secure multi-level communication using duffing chaotic maps and steganography. The method generates a random index vector (RIV) to determine pixel locations for embedding secret data in the least significant bits of an image. Simulation results show the proposed method achieves high quality steganography with a peak signal to noise ratio up to 74.87 dB and low mean squared error of 0.0828, indicating effectiveness. The document also reviews related work in chaotic maps, image steganography techniques, and metrics for evaluating steganography systems based on mean squared error, peak signal to noise ratio, and structural similarity index.
IRJET - A Deep Novel Study on Different CNN Algorithms for Face Skin Disease ...IRJET Journal
This document summarizes a study that used convolutional neural networks (CNNs) to classify facial skin diseases using clinical images. Specifically:
- The study established a dataset of 2656 facial images belonging to 6 common skin diseases from a large Chinese clinical image dataset.
- Five CNN models (ResNet-50, Inception-v3, DenseNet121, Xception, Inception-ResNet-v2) were trained on the dataset with and without transfer learning from other body part images.
- The Inception-ResNet-v2 model achieved the best performance, with a mean recall of 77.0% and precision of 70.8% on the test set when using transfer learning. Some diseases like LE, B
AHP validated literature review of forgery type dependent passive image forge...IJECEIAES
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Contour evolution method for precise boundary delineation of medical imagesTELKOMNIKA JOURNAL
Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.
IRJET - Detection of Skin Cancer using Convolutional Neural NetworkIRJET Journal
This document presents a method for detecting skin cancer using convolutional neural networks. The proposed method involves collecting skin images, preprocessing them by removing noise and segmenting regions of interest, extracting features like asymmetry, border, color, and diameter, performing dimensionality reduction using principal component analysis, calculating dermoscopy scores, and classifying images as malignant or benign using a convolutional neural network (CNN) model. The CNN model achieves 92.5% accuracy in classification. The document provides background on skin cancer and challenges with traditional biopsy methods. It describes the system architecture including data collection, preprocessing, segmentation, feature extraction, and classification steps. Key aspects of CNNs like convolutional, ReLU, pooling, and fully connected layers are also overviewed
A self recovery approach using halftone images for medical imageryiaemedu
This document summarizes a proposed approach for securely transferring medical images over the internet using visual cryptography and halftone images. The approach uses error diffusion techniques to generate a halftone host image from the grayscale medical image. Shadow images are then created from the halftone host image using visual cryptography algorithms. When stacked together, the shadow images reveal the secret medical image. The halftone host image also contains an embedded logo that can be extracted to verify the integrity of the reconstructed image without a trusted third party.
Cellular Neural Networks are used to identify abnormalities in medical images like MRI in real time. The algorithm compares input images to a standard normal image and extracts pixel values that differ, representing abnormalities. It then uses median filtering and an inpainting technique to clean and fill in the extracted abnormality image for clearer viewing. The simple and efficient CNN algorithm allows for fast real-time processing of medical images to aid in quicker diagnosis.
IRJET- A Review Paper on Chaotic Map Image Encryption TechniquesIRJET Journal
This document reviews various techniques for encrypting images using chaotic maps. It begins with an abstract summarizing that image encryption has become important with digital communication growth. Chaotic encryption is an alternative due to chaotic maps' sensitivity to initial conditions and unpredictability. The document then reviews concepts of image encryption, cryptography, and chaotic systems. It surveys several existing image encryption schemes based on chaotic maps and their approaches, such as using the Henon map or selective encryption of image components. The typical architecture of chaos-based image cryptosystems is also described.
Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
Comparing the performance of linear regression versus deep learning on detect...journalBEEI
This document compares the performance of linear regression versus deep learning models for detecting melanoma skin cancer using images. Two machine learning models were developed - one using linear regression for image classification and one using a convolutional neural network (CNN) for object detection. Both models were trained on 600 skin images from a public database and tested on 120 separate images. The testing results showed that the CNN model achieved 70% accuracy compared to 68% for the linear regression model. More importantly, the linear regression model had a 43% false-negative rate, much higher than the CNN's 25% rate. A high false-negative rate could result in delayed treatment and worse health outcomes. Therefore, the document concludes that the CNN model is the best approach for detecting
Image Recognition Expert System based on deep learningPRATHAMESH REGE
The document summarizes literature on image recognition expert systems and deep learning. It discusses two papers:
1. The Low-Power Image Recognition Challenge which established a benchmark for comparing low-power image recognition solutions based on both accuracy and energy efficiency using datasets like ILSVRC.
2. The role of knowledge-based systems and expert systems in automatic interpretation of aerial images. It discusses techniques like semantic networks, frames and logical inference used to solve ill-defined problems with limited information. Frameworks like the blackboard model, ACRONYM and SIGMA are discussed.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
PADDY CROP DISEASE DETECTION USING SVM AND CNN ALGORITHMIRJET Journal
- The document discusses a study on detecting diseases in paddy/rice crops using deep learning algorithms like convolutional neural networks (CNN) and support vector machines (SVM).
- A dataset of rice leaf images was created and a CNN model using transfer learning with MobileNet was developed and trained on the dataset to classify rice diseases.
- The proposed method aims to automatically classify rice disease images to help farmers more accurately identify diseases, as manual identification can be difficult and inaccurate. This could help improve treatment and support farmers.
This document proposes a vision-based approach to detect violations of social distancing using computer vision algorithms. The approach uses inverse perspective mapping to transform frames from surveillance cameras into a bird's eye view representation with real-world coordinates. It then applies Gaussian mixture modeling for background subtraction, Kalman filtering for tracking, and distance calculations to identify instances where two individuals are within 2 meters and therefore not socially distanced. The results show the approach can accurately detect social distancing violations in different scenarios.
Real-time face detection in digital video-based on Viola-Jones supported by c...IJECEIAES
Face detection is a critical function of security (secure witness face in the video) who appear in a scene and are frequently captured by the camera. Recognition of people from their faces in images has recently piqued the scientific community, partly due to application concerns, but also for the difficulty this characterizes for the algorithms of artificial vision. The idea for this research stems from a broad interest in courtroom witness face detection. The goal of this work is to detect and track the face of a witness in court. In this work, a Viola-Jones method is used to extract human faces and then a particular transformation is applied to crop the image. Witness and non-witness images are classified using convolutional neural networks (CNN). The Kanade-Lucas-Tomasi (KLT) algorithm was utilized to track the witness face using trained features. In this model, the two methods were combined in one model to take the advantage of each method in terms of speed and reduce the amount of space required to implement CNN and detection accuracy. After the test, the results of the proposed model showed that it was 99.5% percent accurate when executed in real-time and with adequate lighting.
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
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Design secure multi-level communication system based on duffing chaotic map a...IJEECSIAES
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Design secure multi-level communication system based on duffing chaotic map a...nooriasukmaningtyas
This document proposes a new method for secure multi-level communication using duffing chaotic maps and steganography. The method generates a random index vector (RIV) to determine pixel locations for embedding secret data in the least significant bits of an image. Simulation results show the proposed method achieves high quality steganography with a peak signal to noise ratio up to 74.87 dB and low mean squared error of 0.0828, indicating effectiveness. The document also reviews related work in chaotic maps, image steganography techniques, and metrics for evaluating steganography systems based on mean squared error, peak signal to noise ratio, and structural similarity index.
IRJET - A Deep Novel Study on Different CNN Algorithms for Face Skin Disease ...IRJET Journal
This document summarizes a study that used convolutional neural networks (CNNs) to classify facial skin diseases using clinical images. Specifically:
- The study established a dataset of 2656 facial images belonging to 6 common skin diseases from a large Chinese clinical image dataset.
- Five CNN models (ResNet-50, Inception-v3, DenseNet121, Xception, Inception-ResNet-v2) were trained on the dataset with and without transfer learning from other body part images.
- The Inception-ResNet-v2 model achieved the best performance, with a mean recall of 77.0% and precision of 70.8% on the test set when using transfer learning. Some diseases like LE, B
AHP validated literature review of forgery type dependent passive image forge...IJECEIAES
Nowadays, a lot of significance is given to what we read today: newspapers, magazines, news channels, and internet media, such as leading social networking sites like Facebook, Instagram, and Twitter. These are the primary wellsprings of phony news and are frequently utilized in malignant manners, for example, for horde incitement. In the recent decade, a tremendous increase in image information generation is happening due to the massive use of social networking services. Various image editing software like Skylum Luminar, Corel PaintShop Pro, Adobe Photoshop, and many others are used to create, modify the images and videos, are significant concerns. A lot of earlier work of forgery detection was focused on traditional methods to solve the forgery detection. Recently, Deep learning algorithms have accomplished high-performance accuracies in the image processing domain, such as image classification and face recognition. Experts have applied deep learning techniques to detect a forgery in the image too. However, there is a real need to explain why the image is categorized under forged to understand the algorithm’s validity; this explanation helps in mission-critical applications like forensic. Explainable AI (XAI) algorithms have been used to interpret a black box’s decision in various cases. This paper contributes a survey on image forgery detection with deep learning approaches. It also focuses on the survey of explainable AI for images.
Deep hypersphere embedding for real-time face recognitionTELKOMNIKA JOURNAL
With the advancement of human-computer interaction capabilities of robots, computer vision surveillance systems involving security yields a large impact in the research industry by helping in digitalization of certain security processes. Recognizing a face in the computer vision involves identification and classification of which faces belongs to the same person by means of comparing face embedding vectors. In an organization that has a large and diverse labelled dataset on a large number of epoch, oftentimes, creates a training difficulties involving incompatibility in different versions of face embedding that leads to poor face recognition accuracy. In this paper, we will design and implement robotic vision security surveillance system incorporating hybrid combination of MTCNN for face detection, and FaceNet as the unified embedding for face recognition and clustering.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Contour evolution method for precise boundary delineation of medical imagesTELKOMNIKA JOURNAL
Image segmentation is an important precursor to boundary delineation of medical images. One of the major challenges in applying automatic image segmentation in medical images is the imperfection in the imaging process which can result in inconsistent contrast and brightness levels, and low image sharpness and vanishing boundaries. Although recent advances in deep learning produce vast improvements in the quality of image segmentation, the accuracy of segmentation around object boundaries still requires improvement. We developed a new approach to contour evolution that is more intuitive but shares some common principles with the active contour model method. The method uses two concepts, namely the boundary grid and sparse boundary representation, as an implicit and explicit representation of the boundary points. We tested our method using lumbar spine MRI images of 515 patients. The experiment results show that our method performs up to 10.2 times faster and more flexible than the geodesic active contours method. Using BF-score contour-based metric, we show that our method improves the boundary accuracy from 74% to 84% as opposed to 63% by the latter method.
IRJET - Detection of Skin Cancer using Convolutional Neural NetworkIRJET Journal
This document presents a method for detecting skin cancer using convolutional neural networks. The proposed method involves collecting skin images, preprocessing them by removing noise and segmenting regions of interest, extracting features like asymmetry, border, color, and diameter, performing dimensionality reduction using principal component analysis, calculating dermoscopy scores, and classifying images as malignant or benign using a convolutional neural network (CNN) model. The CNN model achieves 92.5% accuracy in classification. The document provides background on skin cancer and challenges with traditional biopsy methods. It describes the system architecture including data collection, preprocessing, segmentation, feature extraction, and classification steps. Key aspects of CNNs like convolutional, ReLU, pooling, and fully connected layers are also overviewed
A self recovery approach using halftone images for medical imageryiaemedu
This document summarizes a proposed approach for securely transferring medical images over the internet using visual cryptography and halftone images. The approach uses error diffusion techniques to generate a halftone host image from the grayscale medical image. Shadow images are then created from the halftone host image using visual cryptography algorithms. When stacked together, the shadow images reveal the secret medical image. The halftone host image also contains an embedded logo that can be extracted to verify the integrity of the reconstructed image without a trusted third party.
Cellular Neural Networks are used to identify abnormalities in medical images like MRI in real time. The algorithm compares input images to a standard normal image and extracts pixel values that differ, representing abnormalities. It then uses median filtering and an inpainting technique to clean and fill in the extracted abnormality image for clearer viewing. The simple and efficient CNN algorithm allows for fast real-time processing of medical images to aid in quicker diagnosis.
IRJET- A Review Paper on Chaotic Map Image Encryption TechniquesIRJET Journal
This document reviews various techniques for encrypting images using chaotic maps. It begins with an abstract summarizing that image encryption has become important with digital communication growth. Chaotic encryption is an alternative due to chaotic maps' sensitivity to initial conditions and unpredictability. The document then reviews concepts of image encryption, cryptography, and chaotic systems. It surveys several existing image encryption schemes based on chaotic maps and their approaches, such as using the Henon map or selective encryption of image components. The typical architecture of chaos-based image cryptosystems is also described.
Covid Face Mask Detection Using Neural NetworksIRJET Journal
The document describes a study that developed a convolutional neural network (CNN) model using the MobileNetV2 architecture to detect if people in images are wearing face masks properly, improperly, or not at all. The model was trained on a dataset containing these three classes and achieved an accuracy of 97.25% for classifying images. The developed model can be implemented in real-world applications like public transportation stations, hospitals, offices, and schools to help monitor mask compliance and reduce the spread of COVID-19.
Comparing the performance of linear regression versus deep learning on detect...journalBEEI
This document compares the performance of linear regression versus deep learning models for detecting melanoma skin cancer using images. Two machine learning models were developed - one using linear regression for image classification and one using a convolutional neural network (CNN) for object detection. Both models were trained on 600 skin images from a public database and tested on 120 separate images. The testing results showed that the CNN model achieved 70% accuracy compared to 68% for the linear regression model. More importantly, the linear regression model had a 43% false-negative rate, much higher than the CNN's 25% rate. A high false-negative rate could result in delayed treatment and worse health outcomes. Therefore, the document concludes that the CNN model is the best approach for detecting
Image Recognition Expert System based on deep learningPRATHAMESH REGE
The document summarizes literature on image recognition expert systems and deep learning. It discusses two papers:
1. The Low-Power Image Recognition Challenge which established a benchmark for comparing low-power image recognition solutions based on both accuracy and energy efficiency using datasets like ILSVRC.
2. The role of knowledge-based systems and expert systems in automatic interpretation of aerial images. It discusses techniques like semantic networks, frames and logical inference used to solve ill-defined problems with limited information. Frameworks like the blackboard model, ACRONYM and SIGMA are discussed.
Embedded artificial intelligence system using deep learning and raspberrypi f...IAESIJAI
Melanoma is a kind of skin cancer that originates in melanocytes responsible for producing melanin, it can be a severe and potentially deadly form of cancer because it can metastasize to other regions of the body if not detected and treated early. To facilitate this process, Recently, various computer-assisted low-cost, reliable, and accurate diagnostic systems have been proposed based on artificial intelligence (AI) algorithms, particularly deep learning techniques. This work proposed an innovative and intelligent system that combines the internet of things (IoT) with a Raspberry Pi connected to a camera and a deep learning model based on the deep convolutional neural network (CNN) algorithm for real-time detection and classification of melanoma cancer lesions. The key stages of our model before serializing to the Raspberry Pi: Firstly, the preprocessing part contains data cleaning, data transformation (normalization), and data augmentation to reduce overfitting when training. Then, the deep CNN algorithm is used to extract the features part. Finally, the classification part with applied Sigmoid Activation Function. The experimental results indicate the efficiency of our proposed classification system as we achieved an accuracy rate of 92%, a precision of 91%, a sensitivity of 91%, and an area under the curve- receiver operating characteristics (AUC-ROC) of 0.9133.
PADDY CROP DISEASE DETECTION USING SVM AND CNN ALGORITHMIRJET Journal
- The document discusses a study on detecting diseases in paddy/rice crops using deep learning algorithms like convolutional neural networks (CNN) and support vector machines (SVM).
- A dataset of rice leaf images was created and a CNN model using transfer learning with MobileNet was developed and trained on the dataset to classify rice diseases.
- The proposed method aims to automatically classify rice disease images to help farmers more accurately identify diseases, as manual identification can be difficult and inaccurate. This could help improve treatment and support farmers.
This document proposes a vision-based approach to detect violations of social distancing using computer vision algorithms. The approach uses inverse perspective mapping to transform frames from surveillance cameras into a bird's eye view representation with real-world coordinates. It then applies Gaussian mixture modeling for background subtraction, Kalman filtering for tracking, and distance calculations to identify instances where two individuals are within 2 meters and therefore not socially distanced. The results show the approach can accurately detect social distancing violations in different scenarios.
Real-time face detection in digital video-based on Viola-Jones supported by c...IJECEIAES
Face detection is a critical function of security (secure witness face in the video) who appear in a scene and are frequently captured by the camera. Recognition of people from their faces in images has recently piqued the scientific community, partly due to application concerns, but also for the difficulty this characterizes for the algorithms of artificial vision. The idea for this research stems from a broad interest in courtroom witness face detection. The goal of this work is to detect and track the face of a witness in court. In this work, a Viola-Jones method is used to extract human faces and then a particular transformation is applied to crop the image. Witness and non-witness images are classified using convolutional neural networks (CNN). The Kanade-Lucas-Tomasi (KLT) algorithm was utilized to track the witness face using trained features. In this model, the two methods were combined in one model to take the advantage of each method in terms of speed and reduce the amount of space required to implement CNN and detection accuracy. After the test, the results of the proposed model showed that it was 99.5% percent accurate when executed in real-time and with adequate lighting.
Similar to COVID-19 digital x-rays forgery classification model using deep learning (20)
Convolutional neural network with binary moth flame optimization for emotion ...IAESIJAI
Electroencephalograph (EEG) signals have the ability of real-time reflecting brain activities. Utilizing the EEG signal for analyzing human emotional states is a common study. The EEG signals of the emotions aren’t distinctive and it is different from one person to another as every one of them has different emotional responses to same stimuli. Which is why, the signals of the EEG are subject dependent and proven to be effective for the subject dependent detection of the Emotions. For the purpose of achieving enhanced accuracy and high true positive rate, the suggested system proposed a binary moth flame optimization (BMFO) algorithm for the process of feature selection and convolutional neural networks (CNNs) for classifications. In this proposal, optimum features are chosen with the use of accuracy as objective function. Ultimately, optimally chosen features are classified after that with the use of a CNN for the purpose of discriminating different emotion states.
A novel ensemble model for detecting fake newsIAESIJAI
Due the growing proliferation of fake news over the past couple of years, our objective in this paper is to propose an ensemble model for the automatic classification of article news as being either real or fake. For this purpose, we opt for a blending technique that combines three models, namely bidirectional long short-term memory (Bi-LSTM), stochastic gradient descent classifier and ridge classifier. The implementation of the proposed model (i.e. BI-LSR) on real world datasets, has shown outstanding results. In fact, it achieved an accuracy score of 99.16%. Accordingly, this ensemble learning has proven to do perform better than individual conventional machine learning and deep learning models as well as many ensemble learning approaches cited in the literature.
K-centroid convergence clustering identification in one-label per type for di...IAESIJAI
Disease prediction is a high demand field which requires significant support from machine learning (ML) to enhance the result efficiency. The research works on application of K-means clustering supervised classification in disease prediction where each class only has one labeled data. The K-centroid convergence clustering identification (KC3 I) system is based on semi-K-means clustering but only requires single labeled data per class for the training process with the training dataset to update the centroid. The KC3 I model also includes a dictionary box to index all the input centroids before and after the updating process. Each centroid matches with a corresponding label inside this box. After the training process, each time the input features arrive, the trained centroid will put them to its cluster depending on the Euclidean distance, then convert them into the specific class name, which is coherent to that centroid index. Two validation stages were carried out and accomplished the expectation in terms of precision, recall, F1-score, and absolute accuracy. The last part demonstrates the possibility of feature reduction by selecting the most crucial feature with the extra tree classifier method. Total data are fed into the KC3 I system with the most important features and remain the same accuracy.
Plant leaf detection through machine learning based image classification appr...IAESIJAI
Since maize is a staple diet for people, especially vegetarians and vegans, maize leaf disease has a significant influence here on the food industry including maize crop productivity. Therefore, it should be understood that maize quality must be optimal; yet, to do so, maize must be safeguarded from several illnesses. As a result, there is a great demand for such an automated system that can identify the condition early on and take the appropriate action. Early disease identification is crucial, but it also poses a major obstacle. As a result, in this research project, we adopt the fundamental k-nearest neighbor (KNN) model and concentrate on building and developing the enhanced k-nearest neighbor (EKNN) model. EKNN aids in identifying several classes of disease. To gather discriminative, boundary, pattern, and structurally linked information, additional high-quality fine and coarse features are generated. This information is then used in the classification process. The classification algorithm offers high-quality gradient-based features. Additionally, the proposed model is assessed using the Plant-Village dataset, and a comparison with many standard classification models using various metrics is also done.
Backbone search for object detection for applications in intrusion warning sy...IAESIJAI
In this work, we propose a novel backbone search method for object detection for applications in intrusion warning systems. The goal is to find a compact model for use in embedded thermal imaging cameras widely used in intrusion warning systems. The proposed method is based on faster region-based convolutional neural network (Faster R-CNN) because it can detect small objects. Inspired by EfficientNet, the sought-after backbone architecture is obtained by finding the most suitable width scale for the base backbone (ResNet50). The evaluation metrics are mean average precision (mAP), number of parameters, and number of multiply–accumulate operations (MACs). The experimental results showed that the proposed method is effective in building a lightweight neural network for the task of object detection. The obtained model can keep the predefined mAP while minimizing the number of parameters and computational resources. All experiments are executed elaborately on the person detection in intrusion warning systems (PDIWS) dataset.
Deep learning method for lung cancer identification and classificationIAESIJAI
Lung cancer (LC) is calming many lives and is becoming a serious cause of concern. The detection of LC at an early stage assists the chances of recovery. Accuracy of detection of LC at an early stage can be improved with the help of a convolutional neural network (CNN) based deep learning approach. In this paper, we present two methodologies for Lung cancer detection (LCD) applied on Lung image database consortium (LIDC) and image database resource initiative (IDRI) data sets. Classification of these LC images is carried out using support vector machine (SVM), and deep CNN. The CNN is trained with i) multiple batches and ii) single batch for LC image classification as non cancer and cancer image. All these methods are being implemented in MATLAB. The accuracy of classification obtained by SVM is 65%, whereas deep CNN produced detection accuracy of 80% and 100% respectively for multiple and single batch training. The novelty of our experimentation is near 100% classification accuracy obtained by our deep CNN model when tested on 25 Lung computed tomography (CT) test images each of size 512×512 pixels in less than 20 iterations as compared to the research work carried out by other researchers using cropped LC nodule images.
Optically processed Kannada script realization with Siamese neural network modelIAESIJAI
Optical character recognition (OCR) is a technology that allows computers to recognize and extract text from images or scanned documents. It is commonly used to convert printed or handwritten text into machine-readable format. This Study presents an OCR system on Kannada Characters based on siamese neural network (SNN). Here the SNN, a Deep neural network which comprises of two identical convolutional neural network (CNN) compare the script and ranks based on the dissimilarity. When lesser dissimilarity score is identified, prediction is done as character match. In this work the authors use 5 classes of Kannada characters which were initially preprocessed using grey scaling and convert it to pgm format. This is directly input into the Deep convolutional network which is learnt from matching and non-matching image between the CNN with contrastive loss function in Siamese architecture. The Proposed OCR system uses very less time and gives more accurate results as compared to the regular CNN. The model can become a powerful tool for identification, particularly in situations where there is a high degree of variation in writing styles or limited training data is available.
Deep learning based biometric authentication using electrocardiogram and irisIAESIJAI
Authentication systems play an important role in wide range of applications. The traditional token certificate and password-based authentication systems are now replaced by biometric authentication systems. Generally, these authentication systems are based on the data obtained from face, iris, electrocardiogram (ECG), fingerprint and palm print. But these types of models are unimodal authentication, which suffer from accuracy and reliability issues. In this regard, multimodal biometric authentication systems have gained huge attention to develop the robust authentication systems. Moreover, the current development in deep learning schemes have proliferated to develop more robust architecture to overcome the issues of tradition machine learning based authentication systems. In this work, we have adopted ECG and iris data and trained the obtained features with the help of hybrid convolutional neural network- long short-term memory (CNN-LSTM) model. In ECG, R peak detection is considered as an important aspect for feature extraction and morphological features are extracted. Similarly, gabor-wavelet, gray level co-occurrence matrix (GLCM), gray level difference matrix (GLDM) and principal component analysis (PCA) based feature extraction methods are applied on iris data. The final feature vector is obtained from MIT-BIH and IIT Delhi Iris dataset which is trained and tested by using CNN-LSTM. The experimental analysis shows that the proposed approach achieves average accuracy, precision, and F1-core as 0.985, 0.962 and 0.975, respectively.
Hybrid channel and spatial attention-UNet for skin lesion segmentationIAESIJAI
Melanoma is a type of skin cancer which has affected many lives globally. The American Cancer Society research has suggested that it a serious type of skin cancer and lead to mortality but it is almost 100% curable if it is detected and treated in its early stages. Currently automated computer vision-based schemes are widely adopted but these systems suffer from poor segmentation accuracy. To overcome these issue, deep learning (DL) has become the promising solution which performs extensive training for pattern learning and provide better classification accuracy. However, skin lesion segmentation is affected due to skin hair, unclear boundaries, pigmentation, and mole. To overcome this issue, we adopt UNet based deep learning scheme and incorporated attention mechanism which considers low level statistics and high-level statistics combined with feedback and skip connection module. This helps to obtain the robust features without neglecting the channel information. Further, we use channel attention, spatial attention modulation to achieve the final segmentation. The proposed DL based scheme is instigated on publically available dataset and experimental investigation shows that the proposed Hybrid Attention UNet approach achieves average performance as 0.9715, 0.9962, 0.9710.
Photoplethysmogram signal reconstruction through integrated compression sensi...IAESIJAI
The transmission of photoplethysmogram (PPG) signals in real-time is extremely challenging and facilitates the use of an internet of things (IoT) environment for healthcare- monitoring. This paper proposes an approach for PPG signal reconstruction through integrated compression sensing and basis function aware shallow learning (CSBSL). Integrated-CSBSL approach for combined compression of PPG signals via multiple channels thereby improving the reconstruction accuracy for the PPG signals essential in healthcare monitoring. An optimal basis function aware shallow learning procedure is employed on PPG signals with prior initialization; this is further fine-tuned by utilizing the knowledge of various other channels, which exploit the further sparsity of the PPG signals. The proposed method for learning combined with PPG signals retains the knowledge of spatial and temporal correlation. The proposed Integrated-CSBSL approach consists of two steps, in the first step the shallow learning based on basis function is carried out through training the PPG signals. The proposed method is evaluated using multichannel PPG signal reconstruction, which potentially benefits clinical applications through PPG monitoring and diagnosis.
Speaker identification under noisy conditions using hybrid convolutional neur...IAESIJAI
Speaker identification is biometrics that classifies or identifies a person from other speakers based on speech characteristics. Recently, deep learning models outperformed conventional machine learning models in speaker identification. Spectrograms of the speech have been used as input in deep learning-based speaker identification using clean speech. However, the performance of speaker identification systems gets degraded under noisy conditions. Cochleograms have shown better results than spectrograms in deep learning-based speaker recognition under noisy and mismatched conditions. Moreover, hybrid convolutional neural network (CNN) and recurrent neural network (RNN) variants have shown better performance than CNN or RNN variants in recent studies. However, there is no attempt conducted to use a hybrid CNN and enhanced RNN variants in speaker identification using cochleogram input to enhance the performance under noisy and mismatched conditions. In this study, a speaker identification using hybrid CNN and the gated recurrent unit (GRU) is proposed for noisy conditions using cochleogram input. VoxCeleb1 audio dataset with real-world noises, white Gaussian noises (WGN) and without additive noises were employed for experiments. The experiment results and the comparison with existing works show that the proposed model performs better than other models in this study and existing works.
Multi-channel microseismic signals classification with convolutional neural n...IAESIJAI
Identifying and classifying microseismic signals is essential to warn of mines’ dangers. Deep learning has replaced traditional methods, but labor-intensive manual identification and varying deep learning outcomes pose challenges. This paper proposes a transfer learning-based convolutional neural network (CNN) method called microseismic signals-convolutional neural network (MS-CNN) to automatically recognize and classify microseismic events and blasts. The model was instructed on a limited sample of data to obtain an optimal weight model for microseismic waveform recognition and classification. A comparative analysis was performed with an existing CNN model and classical image classification models such as AlexNet, GoogLeNet, and ResNet50. The outcomes demonstrate that the MS-CNN model achieved the best recognition and classification effect (99.6% accuracy) in the shortest time (0.31 s to identify 277 images in the test set). Thus, the MS-CNN model can efficiently recognize and classify microseismic events and blasts in practical engineering applications, improving the recognition timeliness of microseismic signals and further enhancing the accuracy of event classification.
Sophisticated face mask dataset: a novel dataset for effective coronavirus di...IAESIJAI
Efficient and accurate coronavirus disease (COVID-19) surveillance necessitates robust identification of individuals wearing face masks. This research introduces the sophisticated face mask dataset (SFMD), a comprehensive compilation of high-quality face mask images enriched with detailed annotations on mask types, fits, and usage patterns. Leveraging cutting-edge deep learning models—EfficientNet-B2, ResNet50, and MobileNet-V2—, we compare SFMD against two established benchmarks: the real-world masked face dataset (RMFD) and the masked face recognition dataset (MFRD). Across all models, SFMD consistently outperforms RMFD and MFRD in key metrics, including accuracy, precision, recall, and F1 score. Additionally, our study demonstrates the dataset's capability to cultivate robust models resilient to intricate scenarios like low-light conditions and facial occlusions due to accessories or facial hair.
Transfer learning for epilepsy detection using spectrogram imagesIAESIJAI
Epilepsy stands out as one of the common neurological diseases. The neural activity of the brain is observed using electroencephalography (EEG). Manual inspection of EEG brain signals is a slow and arduous process, which puts heavy load on neurologists and affects their performance. The aim of this study is to find the best result of classification using the transfer learning model that automatically identify the epileptic and the normal activity, to classify EEG signals by using images of spectrogram which represents the percentage of energy for each coefficient of the continuous wavelet. Dataset includes the EEG signals recorded at monitoring unit of epilepsy used in this study to presents an application of transfer learning by comparing three models Alexnet, visual geometry group (VGG19) and residual neural network ResNet using different combinations with seven different classifiers. This study tested the models and reached a different value of accuracy and other metrics used to judge their performances, and as a result the best combination has been achieved with ResNet combined with support vector machine (SVM) classifier that classified EEG signals with a high success rate using multiple performance metrics such as 97.22% accuracy and 2.78% the value of the error rate.
Deep neural network for lateral control of self-driving cars in urban environ...IAESIJAI
The exponential growth of the automotive industry clearly indicates that self-driving cars are the future of transportation. However, their biggest challenge lies in lateral control, particularly in urban bottlenecking environments, where disturbances and obstacles are abundant. In these situations, the ego vehicle has to follow its own trajectory while rapidly correcting deviation errors without colliding with other nearby vehicles. Various research efforts have focused on developing lateral control approaches, but these methods remain limited in terms of response speed and control accuracy. This paper presents a control strategy using a deep neural network (DNN) controller to effectively keep the car on the centerline of its trajectory and adapt to disturbances arising from deviations or trajectory curvature. The controller focuses on minimizing deviation errors. The Matlab/Simulink software is used for designing and training the DNN. Finally, simulation results confirm that the suggested controller has several advantages in terms of precision, with lateral deviation remaining below 0.65 meters, and rapidity, with a response time of 0.7 seconds, compared to traditional controllers in solving lateral control.
Attention mechanism-based model for cardiomegaly recognition in chest X-Ray i...IAESIJAI
Recently, cardiovascular diseases (CVDs) have become a rapidly growing problem in the world, especially in developing countries. The latter are facing a lifestyle change that introduces new risk factors for heart disease, that requires a particular and urgent interest. Besides, cardiomegaly is a sign of cardiovascular diseases that refers to various conditions; it is associated with the heart enlargement that can be either transient or permanent depending on certain conditions. Furthermore, cardiomegaly is visible on any imaging test including Chest X-Radiation (X-Ray) images; which are one of the most common tools used by Cardiologists to detect and diagnose many diseases. In this paper, we propose an innovative deep learning (DL) model based on an attention module and MobileNet architecture to recognize Cardiomegaly patients using the popular Chest X-Ray8 dataset. Actually, the attention module captures the spatial relationship between the relevant regions in Chest X-Ray images. The experimental results show that the proposed model achieved interesting results with an accuracy rate of 81% which makes it suitable for detecting cardiomegaly disease.
Efficient commodity price forecasting using long short-term memory modelIAESIJAI
Predicting commodity prices, particularly food prices, is a significant concern for various stakeholders, especially in regions that are highly sensitive to commodity price volatility. Historically, many machine learning models like autoregressive integrated moving average (ARIMA) and support vector machine (SVM) have been suggested to overcome the forecasting task. These models struggle to capture the multifaceted and dynamic factors influencing these prices. Recently, deep learning approaches have demonstrated considerable promise in handling complex forecasting tasks. This paper presents a novel long short-term memory (LSTM) network-based model for commodity price forecasting. The model uses five essential commodities namely bread, meat, milk, oil, and petrol. The proposed model focuses on advanced feature engineering which involves moving averages, price volatility, and past prices. The results reveal that our model outperforms traditional methods as it achieves 0.14, 3.04%, and 98.2% for root mean square error (RMSE), mean absolute percentage error (MAPE), and R-squared (R2 ), respectively. In addition to the simplicity of the model, which consists of an LSTM single-cell architecture that reduced the training time to a few minutes instead of hours. This paper contributes to the economic literature on price prediction using advanced deep learning techniques as well as provides practical implications for managing commodity price instability globally.
1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
A deep learning-based approach for early detection of disease in sugarcane pl...IAESIJAI
In many regions of the nation, agriculture serves as the primary industry. The farming environment now faces a number of challenges to farmers. One of the major concerns, and the focus of this research, is disease prediction. A methodology is suggested to automate a process for identifying disease in plant growth and warning farmers in advance so they can take appropriate action. Disease in crop plants has an impact on agricultural production. In this work, a novel DenseNet-support vector machine: explainable artificial intelligence (DNet-SVM: XAI) interpretation that combines a DenseNet with support vector machine (SVM) and local interpretable model-agnostic explanation (LIME) interpretation has been proposed. DNet-SVM: XAI was created by a series of modifications to DenseNet201, including the addition of a support vector machine (SVM) classifier. Prior to using SVM to identify if an image is healthy or un-healthy, images are first feature extracted using a convolution network called DenseNet. In addition to offering a likely explanation for the prediction, the reasoning is carried out utilizing the visual cue produced by the LIME. In light of this, the proposed approach, when paired with its determined interpretability and precision, may successfully assist farmers in the detection of infected plants and recommendation of pesticide for the identified disease.
Signature verification based on proposed fast hyper deep neural networkIAESIJAI
Many industries have made widespread use of the handwittern signature verification system, including banking, education, legal proceedings, and criminal investigation, in which verification and identification are absolutely necessary. In this research, we have developed an accurate offline signature verification model that can be used in a writer-independent scenario. First, the handwitten signature images went through four preprocessing stages in order to be suitable for finding the unique features. Then, three different types of features namely principal component analysis (PCA) as appearance-based features, gray-level co-occurrence matrix (GLCM) as texture-features, and fast Fourier transform (FFT) as frequency-features are extracted from signature images in order to build a hybrid feature vector for each image. Finally, to classify signature features, we have designed a proposed fast hyper deep neural network (FHDNN) architecture. Two different datasets are used to evaluate our model these are SigComp2011, and CEDAR datasets. The results collected demonstrate that the suggested model can operate with accuracy equal to 100%, outperforming several of its predecessors. In the terms of (precision, recall, and F-score) it gives a very good results for both datasets and exceeds (1.00, 0.487, and 0.655 respectively) on Sigcomp2011 dataset and (1.00, 0.507, and 0.672 respectively) on CEDAR dataset.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectorsDianaGray10
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
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COVID-19 digital x-rays forgery classification model using deep learning
1. IAES International Journal of Artificial Intelligence (IJ-AI)
Vol. 12, No. 4, December 2023, pp. 1821~1827
ISSN: 2252-8938, DOI: 10.11591/ijai.v12.i4.pp1821-1827 1821
Journal homepage: http://ijai.iaescore.com
COVID-19 digital x-rays forgery classification model using
deep learning
Eman I. Abd El-Latif1
, Nour Eldeen Khalifa2
1
Department of Mathematics and Computer Science, Faculty of Science, Benha University, Benha, Egypt
2
Information Technology Department, Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt
Article Info ABSTRACT
Article history:
Received Aug 10, 2022
Revised Jan 12, 2023
Accepted Mar 10, 2023
Nowadays, the internet has become a typical medium for sharing digital
images through web applications or social media and there was a rise in
concerns about digital image privacy. Image editing software’s have prepared
it incredibly simple to make changes to an image's content without leaving
any visible evidence for images in general and medical images in particular.
In this paper, the COVID-19 digital x-rays forgery classification model
utilizing deep learning will be introduced. The proposed system will be able
to identify and classify image forgery (copy-move and splicing) manipulation.
Alexnet, Resnet50, and Googlenet are used in this model for feature extraction
and classification, respectively. Images have been tampered with in three
classes (COVID-19, viral pneumonia, and normal). For the classification of
(Forgery or no forgery), the model achieves 0.9472 in testing accuracy. For
the classification of (Copy-move forgery, splicing forgery, and no forgery),
the model achieves 0.8066 in testing accuracy. Moreover, the model achieves
0.796 and 0.8382 for 6 classes and 9 classes problems respectively.
Performance indicators like Recall, Precision, and F1 Score supported the
achieved results and proved that the proposed system is efficient for detecting
the manipulation in images.
Keywords:
COVID-19
Deep learning
Forgery detection
Image forgery
Medical image forgery
This is an open access article under the CC BY-SA license.
Corresponding Author:
Eman I. Abd El-Latif
Department of Mathematics and Computer Science, Faculty of Science, Benha University
Benha, Egypt
Email: eman.mohamed@fsc.bu.edu.eg
1. INTRODUCTION
There was widespread fear that the Severe Acute Respiratory Syndrome (SARS) virus had publishing
around the world by the end of 2003, owing to its alarmingly high infection rates in Asia and outbreaks in the
Middle East, as well as in nations such as Russia that had never seen it previously [1], [2]. This prompted
individuals to raise awareness of viruses, which have developed into important hazards in the twenty-first
century. The World Health Organization (WHO) designated 2019-nCov (COVID-19) as the coronavirus of the
year [3]. Several of the researches devoted to various problems connected to COVID-19 and solved by area of
computer science for example expecting COVID-19 symptoms with several kinds of pneumonia utilizing X-
rays scans [4], examining the function of new technologies in fighting the COVID-19 pandemic [5],
discovering the effects of coronavirus on power industry [6] and more. The majority of papers focus on
categorization and classification COVID-19 CT and X-ray images [7]–[10]. The purpose of this research is to
detect and classify different types of forgery in the COVID-19 dataset while the medical images were being
transmitted from one location to another.
Data transmission through the Internet has become important for numerous fields to share data such
as medicine, education, and digital forensics. Medical images can be transmitted and delivered through the
2. ISSN: 2252-8938
Int J Artif Intell, Vol. 12, No. 4, December 2023: 1821-1827
1822
Internet to allow the diagnosis among medical staff and access to the history of the patient from any place.
There is various software used for changing the content of an image to create a forged one. This type of change
is called image forgery. Forged images display that the alteration in the image cannot be noticed by a visual
check. Therefore, checking the authentication of medical image content has become vital because any alteration
in the medical images can cause a wrong diagnosis.
There are two approaches employed in image forgery detection: active and passive approaches
[11], [12]. The active technique is categorized into two approaches: digital signature and watermarking. In
these techniques, a watermark and the signature are embedded into images during the pre-processing stage.
The most public types of passive approaches are image splicing and copy-move (CM). In CM technique, a
fragment of the image is copied and embedded into another area in the same image [13], [14]. The splicing
technique used fragments of different images and pastes them into another image [15], [16]. The existing
algorithms achieve acceptable performance in detecting passive image forgery. However, they cannot achieve
high detection accuracy with a small forgery region.
The proposed model's primary goal is to notice the splicing and copy-move manipulation in
COVID-19, viral pneumonia, and normal images. Deep transfer learning (DTL) presents an outstanding
performance in different computer vision problems included image classification [17], and semantic
segmentation [18]. Deep learning is a type of multi-layer neural network, in which every layer makes the output
from the preceding convolution layer available to the next layer as an input. It can extract complex features
from medical images automatically. Deep transfer learning (DTL) can be used on images used in medicine to
detect a CM and splicing forgery that the naked eye cannot see. Alexnet [19], Googlenet [20], and Resnet [21]
use learned features from training images and then classify the image. The rest of the paper is ordered. Section
2 presents the related work of forgery image detection. The proposed algorithm introduces in section 3. Section
4 contains the experimental results. The conclusions show in the final section.
2. RELATED WORKS
Various algorithms are proposed in this section to deal with image forgery. First, we will discuss the
numerous splicing techniques and then copy-move techniques. In [22], a method for noticing splicing forgery
depending on Haar wavelet transform (HWT) and uniform local binary pattern (ULBP) is presented. First, the
RGB image is transformed into the YCbCr model and then HWT is applied to produce the four sub-bands. For
every band, ULBP is computed. The final vector is concatenated from all sub-bands. For classification, support
vector machine (SVM) is used.
An algorithm in [23] is focused on convolutional neural network (CNN) and HWT is suggested to
identify the spliced images. HWT is applied after CNN is used to extract features. Finally, SVM is used to
classify images. An algorithm for detecting the alternating in the image is suggested in [24]. It is focused on
using LBP and discrete cosine transform (DCT). For each block in the chrominance component, LBP and DCT
are applied. For detection, SVM is used.
Ulutas et al. [25] presented a passive image algorithm to recognize the forged areas on medical
images. LBP rotation invariant and scale-invariant feature transform (SIFT) are applied to extract the key points
from the medical images. By matching the key points, forged regions are detected. In [26], CNN and error
level analysis (ELA) are used to discover forgery in COVID-19 medical images by detecting the noise pattern.
The algorithm achieves an accuracy of 92% for detecting image is forged or not.
The algorithm in [27] is used Markov features for extracting features from two domains: DWT and
LBP. Then, features are combined from both domains and fed to SVM for classification. Six benchmark
datasets are used to evaluate the algorithm. In [28], an algorithm is based on feature matching and CNN to
detect CM forgery. In CNN, many convolution and pooling layers are utilized for feature extraction and then
apply characterization among original and tampered images. To identify a CM forgery in [29], DWT and DCT
are used for feature extraction. Apply DWT to the image first, and then divide it into blocks. DCT is used for
all block, and the correlation coefficients are compared.
3. THE PROPOSED MODEL ARCHITECTURE
In this paper, a DLT approach is employed to identify the features of tampered regions. Splice and
copy-move are image forgery techniques that are difficult to tell apart from genuine ones. Many algorithms are
developed to detect image forgery. The existing algorithms suffer from low accuracy. Deep learning offers a
solution for digital image authentication because it extracts complex features from an image. The model relies
on three DTL models Alexnet, Googlenet, and Resnet50 to make features extraction and classification
processes at the same time as illustrated in Figure 1. These models need the least training time than other
pre-trained DLT models. Algorithm 1 shows the steps of the proposed model. The architecture of Alexnet is
3. Int J Artif Intell ISSN: 2252-8938
COVID-19 digital x-rays forgery classification model using deep learning (Eman I. Abd El-Latif)
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consisting of eight layers, the first five layers are convolutional, and the remaining layers are fully connected.
After the first two convolutional layers, there is a max-pooling layer in size 3x3. The remainder of the
convolution layers is connected to fully connected layers. After every convolution layer, an activation function
is utilized called rectified linear unit (ReLU) nonlinearity. Different filters are used in each convolution layer.
For example, 96 kernels of size 11 × 11 × 3 are used in the first layer.
Figure 1. Proposed model architecture
ResNet stands for residual network, and it has many versions, Rsenet50 is one of these versions.
Resnet50 has used 50 neural network layers with 48 convolution layers and two pooling layers. It consists of
five stages every stage with a convolution layer and Individuality block. Each block contains three convolution
layers, and every Individuality block has three convolution layers.
There are three versions of Inception Networks, which are called inception versions 1, 2, and 3. The
GoogleNet or Inception V1 consists of 22 layers deep, 27 pooling layers, and 9 inception Layers and it is
proposed in 2014. The inception layer is a collection of all 1×1 convolutional layers, 3×3 convolutional layer,
and 5×5 convolutional layer to reduce the size of parameters in the network. The output of inception
is merged and sent to the next layer. At the end of the network, global average pooling is used to reduce the
number of trainable parameters.
Algorithm 1: The Suggested Model Algorithm
Input: COVID-19 Database and the Tampered Dataset
Output: Classification of the copy-move and splicing forgery of three classes {COVID-
19, Viral Pneumonia, Normal}
1. Copy Move (CM) forgery is created by copying and pasting a portion of an image from each
class.
2. A portion of each class is copied and pasted into the various images to produce splicing
forgery.
3. Download DTL models: Alexnet, Resnet50, and Googlenet
4. Train the proposed model with two, three, six, and nine classes
5. For every image in the dataset
6. Scale the input image to its default DTL aspects.
7. Provide the images to the DTL model for extraction and classification of features.
8. End
4. DATASET CHARACTERISTICS
The COVID-19 Radiography database utilized for training and testing is taken from the open-source
platform [30]. The dataset included three classes: 3,616 COVID-19, 10,129 normal and 1,345 viral pneumonia
images. The following operations are applied to the COVID-19, normal, and viral pneumonia images to create
the tampered images.
In the first operation, a region from each class of the original image is copied and pasted into the same
image to make a copy-move forgery. An area from the medical images is copied and pasted into other regions
in the different images to generate splicing forgery images. The dataset is available online on Mendeley data
[31]. the dataset consists of {COVID-19 2,000 images, CM COVID-19 2,000 images, S COVID-19 2,000
images, Viral Pneumonia 1,340 images, CM Viral Pneumonia 1,340 images, S Viral Pneumonia 850 images,
Normal 2,000 images, CM Normal 2,000 images, S Normal 2,000 images}. Figure 2 shows the original images
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as shown in Figure 2(a), the result of copy-move as shown in Figure 2(b), and splicing forgery as shown in
Figure 2(c) of the COVID-19 image, viral Pneumonia, and normal respectively.
Figure 2. Sample images from the Radiography database, (a) original images, (b) Copy-move, and
(c) Splicing forgery techniques
5. EXPERIMENTAL RESULTS
This section presents the results of the conducted experiments and the metrics used to evaluate the
performance of the proposed model. For each experiment, a computer with 32 GB of RAM and an Intel Xeon
processor was utilized. The system contains an NVIDIA TITAN XP Graphics Card. The development of
experiments was GPU-specific to the software package MATLAB R2021b. The following hyperparameters
were applied to all experimental outcomes during the training and testing phases:
− Model DTL: Alexnet-Googlenet-Resnet50
− Training: 80%, Testing: 20%.
− Optimizer: Adamboost
− Momentum: 0.9
− Learning Rate: 0.001
− Epochs: 40
− Batch size: 32
− Early stopping: 5 epochs
5.1. Evaluation metrics
The experimental results of the algorithm are measured using different metrics such as accuracy,
precision, F-Measure, and recall. When dealing with data that is not balanced, precision and recall are better
suited for identifying a model's errors. The predictive performance of a model is summarized by the F-score,
which is the harmonic mean of precision and recall. The definitions are presented from (1) to (4),
Testing Accuracy =
TPos+TNeg
(TPos+FPos)+( TNeg+FNeg)
(1)
Precision =
TPos
(TPos+FPos)
(2)
Recall =
TruePos
(TPos+𝐹𝑁𝑒𝑔)
(3)
F1 Score = 2 ∗
Precision∗Recall
(Precision+Recall)
(4)
where TPos is the total number of true positive samples, TNeg is the total number of true negative samples,
FalsePos is the total number of false positive samples, and FalseNeg is the total number of false negative
samples from a confusion matrix.
5. Int J Artif Intell ISSN: 2252-8938
COVID-19 digital x-rays forgery classification model using deep learning (Eman I. Abd El-Latif)
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5.2. Results and discussion
Four classification experiments were conducted to evaluate the performance of the proposed model.
The first classification experiment includes two classes (Forgery or no forgery). Table 1 shows the
classification results obtained from Alexnet, Google net, and Resnet50. As shown in Table 1, Resnet50
achieves the highest accuracy possible in the recall, precision, F-score, and testing accuracy if it is compared
to the other DTL models.
Table 1. Testing accuracy and performance metrics for the first classification experiment (Forgery or
no forgery) using different DTL models
Recall Precision F Score Testing Accuracy
Alexnet 0.8955 0.9232 0.9091 0.9109
Googlenet 0.9222 0.9463 0.9341 0.9363
Resnet50 0.9347 0.9544 0.9445 0.9472
The second classification experiment was dedicated to three classes, and they are (Copy-move
forgery, splicing forgery, or no forgery). The testing accuracies are 80.66% in Resnet50, 77.73% in Googlenet,
and 66.96 % in Alexnet as shown in Table 2. The results proved the effectiveness of Resnet50 in detecting
forged images same as in the first classification experiment.
Table 2. Testing accuracy and performance metrics for the second classification experiment for 3 classes
(CM forgery, S forgery or no forgery) using different DTL models
Recall Precision F Score Testing Accuracy
Alexnet 0.6888 0.6616 0.6749 0.6696
Googlenet 0.7882 0.7732 0.7807 0.7773
Resnet50 0.8123 0.8045 0.8084 0.8066
To test the ability of the proposed model, different forgeries techniques for the different main classes
are proposed. The Third classification experiment was conducted on six classes, and they are {CM forgery in
COVID-19, splicing in COVID-19, CM forgery in Viral Pneumonia, splicing in Viral Pneumonia, CM forgery
in Normal, splicing in Normal} as presented in Table 3. The classification testing accuracy was 79.6% using
Resnet50 which is the highest testing accuracy possible.
Table 3. Testing accuracy and performance metrics for the third classification experiment for 6 classes using
different DTL models
Recall Precision F Score Testing Accuracy
Alexnet 0.7179 0.712 0.715 0.7072
Googlenet 0.7668 0.7598 0.7633 0.7607
Resnet50 0.7913 0.7864 0.7888 0.7960
The Fourth classification experiment was dedicated to classifying different nine classes, and they are
CM forgery in COVID-19, Splicing in COVID-19, COVID-19, CM forgery in Viral Pneumonia, splicing in
Viral Pneumonia, Viral Pneumonia, CM forgery in Normal, Splicing in Normal, Normal). In Table 4, the
testing accuracies were 83.82% in Resnet50, 77.16% in Googlenet, and 68.15 % in Alexnet. The results proved
the effectiveness of Resnet50 in detecting forged images same as in the first, second, and third classification
experiments.
Table 4. Testing accuracy and performance metrics for the fourth classification experiment for 9 classes
using different DTL models
Recall Precision F Score Testing Accuracy
Alexnet 0.6844 0.7003 0.6923 0.6815
Googlenet 0.7715 0.798 0.7846 0.7716
Resnet50 0.8304 0.8382 0.8343 0.8382
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6. CONCLUSION
Image splicing and copy-move forgery are well-known techniques in the forgery domain. The spliced
image was carried out by copying and pasting some portions from one image into other images. In this paper,
a proposed model for identifying two techniques in image forgery is proposed. To achieve good results, the
proposed algorithm used three DLTs that extract features from images. The selected dataset consisted of three
classes (COVID-19, Viral pneumonia, and Normal) class and we made two operations in images to generate
CM and splicing forgery. We used the difference between the normal, viral, and COVID-19 images to train the
model. The proposed model can efficiently identify image splicing and copy-move forgery of images. The
proposed algorithm achieved a relatively high detection accuracy of 94.72% of Resnet50 for the classification
of two classes. The model accomplished 80.66% in testing accuracy for three classes (Copy-move forgery,
splicing forgery, and no forgery). Moreover, the model achieves 79.60% and 83.82% for the 6 and 9 classes
classification respectively. Performance indicators such as recall, precision, and F1 Score supported the
obtained results and proved that the proposed model was efficient for detecting manipulation in digital medical
images.
Compliance with Ethical Standards:
Ethical statement: This material is the author's original work, which has not been previously published
elsewhere. The paper is not currently being considered for publication elsewhere.
Author contribution: All authors contributed to the study's conception and design. Material preparation was
performed by Eman Ibrahim. The first draft of the manuscript was written by Eman Ibrahim, and all authors
commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Data availability statement: The data that support the findings of this study are available from author Eman
Ibrahim, upon reasonable request.
Funding: There was no external funding for this research.
Conflict of interest: The corresponding author certifies that there is no conflict of interest on behalf of all
authors.
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BIOGRAPHIES OF AUTHORS
Eman I. Abd El-Latif received the M.Sc. and Ph.D. degree in computer science,
at Faculty of Science, Benha University, Egypt, in 2016 and 2020 respectively. She is
currently working a lecturer at computer science and mathematics department, Benha
University, Egypt. Her areas of research include Digital Forensics, Security (Encryption-
Steganography) and image processing. She can be contacted at email:
eman.mohamed@fsc.bu.edu.eg.
Nour Eldeen Khalifa received his B.Sc., M.Sc. and Ph.D. degree in 2006, 2009
and 2013 respectively, all from Cairo University, Faculty of Computers and Artificial
Intelligence, Cairo, Egypt. He also had a Professional M.Sc. Degree in Cloud Computing in
2018. He authored/coauthored more than 40 publications and 2 edited books. He had more
than 2000 citations. He reviewed several papers for international journals and conferences
including (Scientific Reports, IEEE IoT, Neural Computing, and Artificial Intelligence
Review). Currently, he is an associate professor at Faculty of Computers and Artificial
Intelligence, Cairo University. His research interests include wireless sensor networks,
cryptography, multimedia, network security, machine, and deep learning. He can be
contacted at email: nourmahmoud@cu.edu.eg.