Harnessing AI for Deep Fake Detection in Images
Objective
The primary objective is to design, implement, and evaluate a reliable deep fake detection system for images, utilizing the power of VGG16 and MobileNet CNNs. Specific goals include training the models on extensive datasets, fine-tuning for optimal performance, and validating their effectiveness against a wide array of deep fake manipulations. Another objective is to assess the scalability and real-time applicability of the system, striving for a practical solution that aligns with the dynamic nature of online content. By achieving these objectives, the project aims to contribute significantly to the advancement of AI-driven tools for countering image-based misinformation and fortifying the integrity of visual communication in the digital era.
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 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
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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, and not projects of or by IEEE.
