10.ATTENDANCE CAPTURE SYSTEM USING FACE RECOGNITION.docxVenkat Projects
This document describes a proposed attendance capture system using face recognition. The existing manual attendance systems are inefficient, but current automated systems using biometrics like face recognition have issues with accuracy and efficiency. The proposed system would use a PRISMA review method and facial recognition algorithms like machine learning and deep learning to capture and identify faces, record attendance, and store the data in a database. This system aims to provide high accuracy and efficiency compared to existing solutions.
6.A FOREST FIRE IDENTIFICATION METHOD FOR UNMANNED AERIAL VEHICLE MONITORING ...Venkat Projects
This document proposes a new method for identifying forest fires using video images captured by unmanned aerial vehicles (UAVs). The current methods have poor real-time performance and low efficiency. The proposed method uses motion detection, background modeling, and texture/wavelet energy features to identify fire regions in UAV videos. It was tested on 9 sample images, achieving effective fire identification. This provides a better solution for remote forest fire monitoring and resource protection.
The document discusses techniques for invisibly watermarking images to protect copyright. It proposes improving the accuracy and efficiency of an existing watermarking system that embeds an m-sequence spread spectrum signal as the watermark. The improved system embeds watermark bits in 8x8 pixel blocks with spacing between blocks to increase security and enable localization of any alterations to the image. This achieves higher accuracy and efficiency than the previous system.
10.ATTENDANCE CAPTURE SYSTEM USING FACE RECOGNITION.docxVenkat Projects
This document describes a proposed attendance capture system using face recognition. The existing manual attendance systems are inefficient, but current automated systems using biometrics like face recognition have issues with accuracy and efficiency. The proposed system would use a PRISMA review method and facial recognition algorithms like machine learning and deep learning to capture and identify faces, record attendance, and store the data in a database. This system aims to provide high accuracy and efficiency compared to existing solutions.
6.A FOREST FIRE IDENTIFICATION METHOD FOR UNMANNED AERIAL VEHICLE MONITORING ...Venkat Projects
This document proposes a new method for identifying forest fires using video images captured by unmanned aerial vehicles (UAVs). The current methods have poor real-time performance and low efficiency. The proposed method uses motion detection, background modeling, and texture/wavelet energy features to identify fire regions in UAV videos. It was tested on 9 sample images, achieving effective fire identification. This provides a better solution for remote forest fire monitoring and resource protection.
The document discusses techniques for invisibly watermarking images to protect copyright. It proposes improving the accuracy and efficiency of an existing watermarking system that embeds an m-sequence spread spectrum signal as the watermark. The improved system embeds watermark bits in 8x8 pixel blocks with spacing between blocks to increase security and enable localization of any alterations to the image. This achieves higher accuracy and efficiency than the previous system.
OPTIMISED STACKED ENSEMBLE TECHNIQUES IN THE PREDICTION OF CERVICAL CANCER US...Venkat Projects
The document discusses a study that used machine learning techniques to predict cervical cancer diagnosis. It used a cervical cancer risk factors dataset containing 858 records and 32 risk factors. It applied SMOTE to address data imbalance and the Firefly algorithm for feature selection, reducing the features to 15, 13, 11 and 11 for different diagnosis tests. It then used ensemble models like XGBoost, AdaBoost and Random Forest for classification, achieving the highest accuracy of 98.83% for the Hinselmann test using XGBoost with the selected features. The proposed models showed improved performance over other studies in cervical cancer prediction.
The document lists 50 major Python projects across different domains including deep learning (DL), machine learning (ML), blockchain, and Django. The projects deal with topics such as computer vision, NLP, healthcare, finance, and more. They involve building models for tasks like image classification, object detection, sentiment analysis, fraud detection, and process automation using techniques like convolutional neural networks, random forests, and blockchain. Venkat Java Projects provides services for developing such projects.
The document lists 50 Python project ideas from the domain of machine learning, deep learning, blockchain, and other technologies. The projects cover a wide range of applications including currency recognition, predicting rainfall, detecting extremist groups, fish disease detection, epilepsy detection, online inventory management, sign language recognition, blockchain applications for farming, object detection for the visually impaired, graphical password authentication, and more. The document provides the project title and domain for each idea.
The document lists 50 potential Python projects covering various domains including artificial intelligence, machine learning, deep learning, natural language processing and blockchain. The projects involve applying techniques such as neural networks, computer vision, sentiment analysis and more to tasks like detecting fake profiles, automating government services, analyzing COVID-19 data, predicting crop yields, recommending movies and detecting network attacks.
2021 python projects list
A BI-OBJECTIVE HYPER-HEURISTIC SUPPORT VECTOR MACHINES FOR BIG DATA CYBER-SECURITY
AN ARTIFICIAL INTELLIGENCE AND CLOUD BASED COLLABORATIVE PLATFORM FOR PLANT DISEASE IDENTIFICATION, TRACKING AND FORECASTING FOR FARMERS
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
9.data analysis for understanding the impact of covid–19 vaccinations on the ...Venkat Projects
9.data analysis for understanding the impact of covid–19 vaccinations on the society
In this paper author analysing vaccines dataset to forecast required vaccines compare to manufacturing or available vaccines and by using this forecasting manufacturers may increase and decrease their manufacturing quantity. This forecasting can impact society by taking decision on manufacturing vaccines and if in society more cases occurred then forecasting will be high and by seeing forecasting manufacturers may increase production.
Vaccines are manufacturing by multiple manufacturers such as JOHNSON AND JOHNSON, PFIZER and many more. In this forecasting will take all manufacturers and their production quantity as well as usage of vaccines and based on this Machine Learning algorithm called Decision Tree will forecast require vaccines for next 30 days
To implement this project we are using vaccines dataset to train decision tree algorithm and then this algorithm will predict require vaccines quantity for next 30 days. This dataset is saved inside ‘Dataset’ folder and below screen showing some records from dataset
6.iris recognition using machine learning techniqueVenkat Projects
This document describes an iris recognition project that uses a CNN model trained on the CASIA iris image dataset to recognize people. The CNN model is trained by extracting iris features from the CASIA images using Hough circle detection and achieves 100% accuracy on the training data. Graphs show the loss decreasing and accuracy increasing over epochs during training. The trained model can then be used to recognize people in new iris images by predicting the person ID. It correctly identifies test images from both outside the dataset and from within the CASIA images.
5.local community detection algorithm based on minimal clusterVenkat Projects
The document summarizes a thesis project on a local cluster-based community detection algorithm. It was submitted by Regalla Sairam Reddy to the University College of Engineering Kakinada in partial fulfillment of a Master of Computer Applications degree. The thesis was supervised by Dr. M.H.M Krishna Prasad and examines using a minimal cluster approach to detect local communities more effectively in complex networks compared to algorithms that start from a single initial node. The document includes declarations by the student and supervisor, as well as acknowledgments and outlines of the problem identification, methodology, technologies used, implementation, and conclusion.
4.detection of fake news through implementation of data science applicationVenkat Projects
This document describes a project that uses an LSTM recurrent neural network to detect fake news. It trains the LSTM model on a dataset of past news labeled as genuine or fake. The news texts are converted to TF-IDF vectors using n-grams before training. The trained model achieves 69.49% accuracy on the test data at predicting whether a new piece of news text is genuine or fake. Screenshots demonstrate data preprocessing, model training and evaluation, and testing the model on new news texts.
an efficient spam detection technique for io t devices using machine learningVenkat Projects
The document proposes a machine learning framework to detect spam on IoT devices. It evaluates five machine learning models on a dataset of IoT device inputs and features to compute a "spamicity score" for each device. This score indicates how trustworthy a device is based on various parameters. The results show the proposed technique is effective at spam detection compared to existing approaches.
efficient io t management with resilience to unauthorized access to cloud sto...Venkat Projects
1) Existing cloud-based IoT management systems have limitations regarding storage demands, computation costs, and preventing unauthorized access through illegal key sharing.
2) The proposed system addresses these by removing storage dependency on access policies, outsourcing computation to clouds, and strictly forbidding unauthorized access via illegal key sharing using a novel CPABE construction and user-specific transformation keys.
3) The system architecture involves hardware requirements of an i3 processor, 40GB hard disk and 2GB RAM, and software requirements of a Windows OS, Java/J2EE coding, MySQL database, and the Netbeans IDE.
benchmarking image retrieval diversification techniques for social mediaVenkat Projects
The document discusses image retrieval diversification techniques for social media. It introduces benchmarking datasets and evaluation frameworks developed for the MediaEval benchmarking campaign to evaluate diversification of image search results for social media queries. The datasets include images and metadata from Flickr with relevance and diversity annotations. The frameworks analyze crucial aspects of diversifying social image search results, such as capabilities of existing systems and the impact of features like deep learning, user credibility and query types. Modules of the frameworks include datasets with pre-computed visual and text descriptors, ground truth relevance and diversity annotations, and methodology to evaluate diversification results.
trust based video management framework for social multimedia networksVenkat Projects
The document proposes a framework for managing trusted video content on social multimedia networks. The framework aims to ensure secure delivery of videos while optimizing computing resources. It involves assigning trust levels to users based on history, using an intelligent agent to automatically publish or reject content, and checking video integrity during streaming. The framework has modules for social networking, secure video storage and streaming, video integrity checking, incentivizing user reviews, calculating user trust levels, and a voting system. It analyzes user behavior and interactions to compute trust scores and help make publishing decisions. The goal is to increase trust in social networks while achieving efficient costs.
collaborative content delivery in software defined heterogeneous vehicular ne...Venkat Projects
The document proposes a collaborative content delivery scheme for software-defined heterogeneous vehicular networks (SD-HetVNETs) consisting of cellular base stations (CBSs) and roadside units (RSUs). It defines utility models for CBS, RSU, and vehicles to motivate cooperation. A double auction game is used to achieve agreement between CBS and RSU for multicast-assisted content delivery to maximize their utilities. Simulations show the scheme enhances utilities of all participants and network efficiency. Hardware requirements include a Pentium IV 2.4 GHz system with 40 GB hard disk and 512 MB RAM, while software requirements include Windows, Java/J2EE, MySQL, and NetBeans 8.1.
dynamic network slicing and resource allocation in mobile edge computing systemsVenkat Projects
The document proposes a framework for dynamic network slicing and resource allocation in mobile edge computing systems to maximize an operator's revenue while considering traffic variations. It develops the Dynamic Network Slicing and Resource Allocation (DNSRA) algorithm that jointly optimizes slice request admission and resource allocation without any prior knowledge of traffic. The DNSRA tackles the coupled optimization problem through variable decoupling and develops closed-form solutions for user association and CPU allocation along with approximation methods for power allocation and subcarrier assignment. A heuristic algorithm is also developed for dynamic slice request admission. Simulation results show the DNSRA can balance revenue and delay while increasing operator revenue over existing schemes.
security enhanced content sharing in social io t a directed hypergraph based ...Venkat Projects
The document proposes a secure content sharing scheme for social IoT that leverages users' social trust to avoid attacks from untrusted users. It models the relationships between users, content, and social groups as a graph to dynamically extract social trust over time. A hierarchical game model is formulated to optimize user pairing and channel selection as sub-problems. Specifically, user pairing is modeled as a matching game and channel selection as a directed hypergraph game. An algorithm is designed to find the optimal Nash equilibrium for these sub-games and thereby the global optimum. Simulation results show the proposed scheme can enhance security without sacrificing quality of experience.
An effecient spam detection technique for io t devices using machine learningVenkat Projects
This document proposes a machine learning framework to detect spam on IoT devices. It evaluates five machine learning models on a dataset of IoT device inputs and features to compute a "spamicity score" for each device. This score indicates how trustworthy a device is based on various parameters. Feature engineering techniques like principal component analysis and an entropy-based filter are used to select important features and reduce data dimensionality for the models. The results show this proposed technique can effectively detect spam compared to other existing approaches.
OPTIMISED STACKED ENSEMBLE TECHNIQUES IN THE PREDICTION OF CERVICAL CANCER US...Venkat Projects
The document discusses a study that used machine learning techniques to predict cervical cancer diagnosis. It used a cervical cancer risk factors dataset containing 858 records and 32 risk factors. It applied SMOTE to address data imbalance and the Firefly algorithm for feature selection, reducing the features to 15, 13, 11 and 11 for different diagnosis tests. It then used ensemble models like XGBoost, AdaBoost and Random Forest for classification, achieving the highest accuracy of 98.83% for the Hinselmann test using XGBoost with the selected features. The proposed models showed improved performance over other studies in cervical cancer prediction.
The document lists 50 major Python projects across different domains including deep learning (DL), machine learning (ML), blockchain, and Django. The projects deal with topics such as computer vision, NLP, healthcare, finance, and more. They involve building models for tasks like image classification, object detection, sentiment analysis, fraud detection, and process automation using techniques like convolutional neural networks, random forests, and blockchain. Venkat Java Projects provides services for developing such projects.
The document lists 50 Python project ideas from the domain of machine learning, deep learning, blockchain, and other technologies. The projects cover a wide range of applications including currency recognition, predicting rainfall, detecting extremist groups, fish disease detection, epilepsy detection, online inventory management, sign language recognition, blockchain applications for farming, object detection for the visually impaired, graphical password authentication, and more. The document provides the project title and domain for each idea.
The document lists 50 potential Python projects covering various domains including artificial intelligence, machine learning, deep learning, natural language processing and blockchain. The projects involve applying techniques such as neural networks, computer vision, sentiment analysis and more to tasks like detecting fake profiles, automating government services, analyzing COVID-19 data, predicting crop yields, recommending movies and detecting network attacks.
2021 python projects list
A BI-OBJECTIVE HYPER-HEURISTIC SUPPORT VECTOR MACHINES FOR BIG DATA CYBER-SECURITY
AN ARTIFICIAL INTELLIGENCE AND CLOUD BASED COLLABORATIVE PLATFORM FOR PLANT DISEASE IDENTIFICATION, TRACKING AND FORECASTING FOR FARMERS
10.sentiment analysis of customer product reviews using machine learniVenkat Projects
10.sentiment analysis of customer product reviews using machine learning In this project author is detecting sentiments from amazon reviews by using various machine learning algorithms such as SVM, Decision Tree and Naïve Bayes. In all 3 algorithms SVM is giving better accuracy and to train this algorithms author has used AMAZON reviews dataset and this dataset is saved inside ‘Amazon_Reviews_dataset’ folder. Below screen shot show example reviews from dataset
9.data analysis for understanding the impact of covid–19 vaccinations on the ...Venkat Projects
9.data analysis for understanding the impact of covid–19 vaccinations on the society
In this paper author analysing vaccines dataset to forecast required vaccines compare to manufacturing or available vaccines and by using this forecasting manufacturers may increase and decrease their manufacturing quantity. This forecasting can impact society by taking decision on manufacturing vaccines and if in society more cases occurred then forecasting will be high and by seeing forecasting manufacturers may increase production.
Vaccines are manufacturing by multiple manufacturers such as JOHNSON AND JOHNSON, PFIZER and many more. In this forecasting will take all manufacturers and their production quantity as well as usage of vaccines and based on this Machine Learning algorithm called Decision Tree will forecast require vaccines for next 30 days
To implement this project we are using vaccines dataset to train decision tree algorithm and then this algorithm will predict require vaccines quantity for next 30 days. This dataset is saved inside ‘Dataset’ folder and below screen showing some records from dataset
6.iris recognition using machine learning techniqueVenkat Projects
This document describes an iris recognition project that uses a CNN model trained on the CASIA iris image dataset to recognize people. The CNN model is trained by extracting iris features from the CASIA images using Hough circle detection and achieves 100% accuracy on the training data. Graphs show the loss decreasing and accuracy increasing over epochs during training. The trained model can then be used to recognize people in new iris images by predicting the person ID. It correctly identifies test images from both outside the dataset and from within the CASIA images.
5.local community detection algorithm based on minimal clusterVenkat Projects
The document summarizes a thesis project on a local cluster-based community detection algorithm. It was submitted by Regalla Sairam Reddy to the University College of Engineering Kakinada in partial fulfillment of a Master of Computer Applications degree. The thesis was supervised by Dr. M.H.M Krishna Prasad and examines using a minimal cluster approach to detect local communities more effectively in complex networks compared to algorithms that start from a single initial node. The document includes declarations by the student and supervisor, as well as acknowledgments and outlines of the problem identification, methodology, technologies used, implementation, and conclusion.
4.detection of fake news through implementation of data science applicationVenkat Projects
This document describes a project that uses an LSTM recurrent neural network to detect fake news. It trains the LSTM model on a dataset of past news labeled as genuine or fake. The news texts are converted to TF-IDF vectors using n-grams before training. The trained model achieves 69.49% accuracy on the test data at predicting whether a new piece of news text is genuine or fake. Screenshots demonstrate data preprocessing, model training and evaluation, and testing the model on new news texts.
an efficient spam detection technique for io t devices using machine learningVenkat Projects
The document proposes a machine learning framework to detect spam on IoT devices. It evaluates five machine learning models on a dataset of IoT device inputs and features to compute a "spamicity score" for each device. This score indicates how trustworthy a device is based on various parameters. The results show the proposed technique is effective at spam detection compared to existing approaches.
efficient io t management with resilience to unauthorized access to cloud sto...Venkat Projects
1) Existing cloud-based IoT management systems have limitations regarding storage demands, computation costs, and preventing unauthorized access through illegal key sharing.
2) The proposed system addresses these by removing storage dependency on access policies, outsourcing computation to clouds, and strictly forbidding unauthorized access via illegal key sharing using a novel CPABE construction and user-specific transformation keys.
3) The system architecture involves hardware requirements of an i3 processor, 40GB hard disk and 2GB RAM, and software requirements of a Windows OS, Java/J2EE coding, MySQL database, and the Netbeans IDE.
benchmarking image retrieval diversification techniques for social mediaVenkat Projects
The document discusses image retrieval diversification techniques for social media. It introduces benchmarking datasets and evaluation frameworks developed for the MediaEval benchmarking campaign to evaluate diversification of image search results for social media queries. The datasets include images and metadata from Flickr with relevance and diversity annotations. The frameworks analyze crucial aspects of diversifying social image search results, such as capabilities of existing systems and the impact of features like deep learning, user credibility and query types. Modules of the frameworks include datasets with pre-computed visual and text descriptors, ground truth relevance and diversity annotations, and methodology to evaluate diversification results.
trust based video management framework for social multimedia networksVenkat Projects
The document proposes a framework for managing trusted video content on social multimedia networks. The framework aims to ensure secure delivery of videos while optimizing computing resources. It involves assigning trust levels to users based on history, using an intelligent agent to automatically publish or reject content, and checking video integrity during streaming. The framework has modules for social networking, secure video storage and streaming, video integrity checking, incentivizing user reviews, calculating user trust levels, and a voting system. It analyzes user behavior and interactions to compute trust scores and help make publishing decisions. The goal is to increase trust in social networks while achieving efficient costs.
collaborative content delivery in software defined heterogeneous vehicular ne...Venkat Projects
The document proposes a collaborative content delivery scheme for software-defined heterogeneous vehicular networks (SD-HetVNETs) consisting of cellular base stations (CBSs) and roadside units (RSUs). It defines utility models for CBS, RSU, and vehicles to motivate cooperation. A double auction game is used to achieve agreement between CBS and RSU for multicast-assisted content delivery to maximize their utilities. Simulations show the scheme enhances utilities of all participants and network efficiency. Hardware requirements include a Pentium IV 2.4 GHz system with 40 GB hard disk and 512 MB RAM, while software requirements include Windows, Java/J2EE, MySQL, and NetBeans 8.1.
dynamic network slicing and resource allocation in mobile edge computing systemsVenkat Projects
The document proposes a framework for dynamic network slicing and resource allocation in mobile edge computing systems to maximize an operator's revenue while considering traffic variations. It develops the Dynamic Network Slicing and Resource Allocation (DNSRA) algorithm that jointly optimizes slice request admission and resource allocation without any prior knowledge of traffic. The DNSRA tackles the coupled optimization problem through variable decoupling and develops closed-form solutions for user association and CPU allocation along with approximation methods for power allocation and subcarrier assignment. A heuristic algorithm is also developed for dynamic slice request admission. Simulation results show the DNSRA can balance revenue and delay while increasing operator revenue over existing schemes.
security enhanced content sharing in social io t a directed hypergraph based ...Venkat Projects
The document proposes a secure content sharing scheme for social IoT that leverages users' social trust to avoid attacks from untrusted users. It models the relationships between users, content, and social groups as a graph to dynamically extract social trust over time. A hierarchical game model is formulated to optimize user pairing and channel selection as sub-problems. Specifically, user pairing is modeled as a matching game and channel selection as a directed hypergraph game. An algorithm is designed to find the optimal Nash equilibrium for these sub-games and thereby the global optimum. Simulation results show the proposed scheme can enhance security without sacrificing quality of experience.
An effecient spam detection technique for io t devices using machine learningVenkat Projects
This document proposes a machine learning framework to detect spam on IoT devices. It evaluates five machine learning models on a dataset of IoT device inputs and features to compute a "spamicity score" for each device. This score indicates how trustworthy a device is based on various parameters. Feature engineering techniques like principal component analysis and an entropy-based filter are used to select important features and reduce data dimensionality for the models. The results show this proposed technique can effectively detect spam compared to other existing approaches.
An effecient spam detection technique for io t devices using machine learning
Image Forgery Detection Based on Fusion of Lightweight Deep Learning Models.docx
1. VENKAT PROJECTS
Email:venkatjavaprojects@gmail.com Mobile No: +91 9966499110
Website: www.venkatjavaprojects.com What‘s app: +91 9966499110
Image Forgery Detection Based on Fusion of Lightweight Deep
Learning Models
ABSTARCT :
Capturing images has been increasingly popular in recent years, owing to the widespread
availability of cameras. Images are essential in our daily lives because they contain a wealth of
information, and it is often required to enhance images to obtain additional information. A
variety of tools are available to improve image quality; nevertheless, they are also frequently
used to falsify images, resulting in the spread of misinformation. This increases the severity and
frequency of image forgeries, which is now a major source of concern. Numerous traditional
techniques have been developed over time to detect image forgeries. In recent years,
convolutional neural networks (CNNs) have received much attention, and CNN has also
influenced the field of image forgery detection. However, most image forgery techniques based
on CNN that exist in the literature are limited to detecting a specific type of forgery (either image
splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting
the presence of unseen forgeries in an image is required. In this paper, we introduce a robust
deep learning based system for identifying image forgeries in the context of double image
compression. The difference between an image’s original and recompressed versions is used to
train our model. The proposed model is lightweight, and its performance demonstrates that it is
faster than state-of-the-art approaches. The experiment results are encouraging, with an overall
validation accuracy of 92.23%.
2. VENKAT PROJECTS
Email:venkatjavaprojects@gmail.com Mobile No: +91 9966499110
Website: www.venkatjavaprojects.com What‘s app: +91 9966499110
EXISTING SYSTEM :
CNNs, which are inspired by the human visual system, are designed to be non-linear
interconnected neurons. They have already demonstrated extraordinary potential in a variety of
computer vision applications, including image segmentation and object detection. They may be
beneficial for a variety of additional purposes, including image forensics. With the various tools
available today, image forgery is fairly simple to do, and because it is extremely dangerous,
detecting it is crucial. When a fragment of an image is moved from one to another, a variety of
artifacts occur due to the images’ disparate origins. While these artifacts may be undetectable to
the naked eye, CNNs may detect their presence in faked images. Due to the fact that the source
of the forged region and the background images are distinct, when we recompress such images,
the forged is enhanced differently due to the compression difference. We use this concept in the
proposed approach by training a CNN-based model to determine if an image is genuine or a fake.
DISADVANTAGES OF EXISTING SYSTEM :
1) Less accuracy
2)low Efficiency
3. VENKAT PROJECTS
Email:venkatjavaprojects@gmail.com Mobile No: +91 9966499110
Website: www.venkatjavaprojects.com What‘s app: +91 9966499110
PROPOSED SYSTEM:
The working of the proposed technique, which has been explained here. We take the forged
image A (images shown in Figure 1b tamper images), and then recompress it; let us call the
recompressed image as Arecompressed (images shown in Figure 1c are recompressed forged
images). Now we take the difference of the original image and the recompressed image, let us
call it Adi f f (images shown in Figure 1e are the difference of Figure 1b,c, respectively). Now
due to the difference in the source of the forged part and the original part of the image, the forged
part gets highlighted in Adi f f (as we can observe in Figure 1d,e, respectively). We train a CNN-
based network to categorize an image as a forged image or a genuine one using Adi f f as our
input features (we label it as a featured image). Figure 2 gives the pictorial view of the overall
working of the proposed method.
ADVANTAGES OF PROPOSED SYSTEM :
1) High accuracy
2)High efficiency
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