Harnessing AI for Deep Fake Detection in Images

Project Code :TCMAPY1099


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.


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



Processor - I3/Intel Processor

Hard Disk - 160GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA



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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