Fake image detection using deep learning
Objective
The primary objective is to develop a deep learning model that can accurately and efficiently classify images as deep fakes or real. By leveraging Convolutional Neural Networks (CNN), VGG16, and MobileNet architectures, the project aims to improve detection accuracy and reduce false positives, thus offering a more reliable solution for identifying deep fakes.
Abstract
Deep Fake and Real Image Classification using Deep Learning is an emerging field focusing on distinguishing between computer-generated (deep fake) and authentic images. The rapid advancement in technology has made it increasingly challenging to detect these deep fakes, as they become more realistic. This project employs deep learning techniques to address this challenge, aiming to develop an effective and efficient model for accurate classification.
Keywords: Deep Learning, Image Classification, Deep Fake Detection, Convolutional Neural, Networks (CNN), VGG16 Architecture, MobileNet, Resnet-50, Architecture, Artificial Intelligence (AI), Digital Media Integrity, Image Processing.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
H/W CONFIGURATION:
Processor - I3/Intel Processor
Hard Disk - 160GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
RAM - 8GB
S/W CONFIGURATION:
• Operating System : Windows 7/8/10
• Server side Script : HTML, CSS, Bootstrap & JS
• Programming Language : Python
• Libraries : Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy
• IDE/Workbench : PyCharm
• Technology : Python 3.6+
• Server Deployment : Xampp Server
Related Projects
Paper Publishing
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
