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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68451
Volume:
10 |
10 |
Oct 2026
Pages:
1-14
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Application
St. Joseph's First Grade College
In recent years, face recognition systems have become an integral part of many security systems. However, such systems can be easily fooled by attackers using spoofing techniques, which involves presenting a fake face, such as a printed photo or a video recording, to the system. This vulnerability has made face spoofing detection an important research topic in the field of computer vision. In this project, we propose a face spoofing detection system using a Convolutional Neural Network (CNN) algorithm. The system first extracts features from the input image using a CNN, and then classifies the image as either real or fake based on these features. We train our model using a large dataset of real and fake face images, and evaluate its performance using several metrics, including accuracy, precision, recall, and F1 score. Our experiments show that the proposed system achieves high accuracy in detecting face spoofing attacks, with an overall accuracy of 95%. The system also exhibits good performance in detecting various types of spoofing attacks, such as printed photos, video recordings, and 3D masks.
KEYWORDS
The project is implemented using python which is an object oriented programming language and procedure oriented programming language. Object oriented programming is an approach that provides a way of modularizing a program by creating a partitioned memory area of both data and function that can be used as a template for creating copies of such modules on demand. This project is implemented using python programming language. Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Python is often described as a "batteries included" language due to its comprehensive standard library. The machine Learning techniques are used in this project. Implementation of software refers to the final installation of the package in its real environment, to the satisfaction of the intended users and the operation of the system. The people are not sure that the software is meant to make their job easier. The active user must be aware of the benefits of using the system. Before going ahead and viewing the system, the user must know that for viewing the result, the server program should be running on the server. If the server object is not running on the server, the actual processes will not take place. Spoofing detection is binary classification means it has only two targets that is either fake or Real.in this project giving an input as RGB image and training rgb image using cnn is a natural task as CNN more works better for Image datasets. To Build this application the first and the most important step is to gather data. For this application we are going to collect the spoofing datasets from kaggle.com. After preprocessing data is passed as input to the CNN architecture.
The project is implemented using python which is an object oriented programming language and procedure oriented programming language. Object oriented programming is an approach that provides a way of modularizing a program by creating a partitioned memory area of both data and function that can be used as a template for creating copies of such modules on demand. This project is implemented using python programming language. Python is dynamically typed and garbage-collected. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming. Python is often described as a "batteries included" language due to its comprehensive standard library. The machine Learning techniques are used in this project. Implementation of software refers to the final installation of the package in its real environment, to the satisfaction of the intended users and the operation of the system. The people are not sure that the software is meant to make their job easier. The active user must be aware of the benefits of using the system. Before going ahead and viewing the system, the user must know that for viewing the result, the server program should be running on the server. If the server object is not running on the server, the actual processes will not take place. Spoofing detection is binary classification means it has only two targets that is either fake or Real.in this project giving an input as RGB image and training rgb image using cnn is a natural task as CNN more works better for Image datasets. To Build this application the first and the most important step is to gather data. For this application we are going to collect the spoofing datasets from kaggle.com. After preprocessing data is passed as input to the CNN architecture.
Publication Date
10/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
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