Fake Image Detection: The aim of the project is to detect images that have been digitally manipulated. Realistic image forgeries involve a combination of splicing, resampling, region removal, smoothing and other manipulation methods. ... Our method is agnostic to the type of manipulation and classifies an image as tampered or untampered. Completed with a accuracy of 60% Tools Used: Python, CNN, Keras, Django, HTML & CSS
Fake Image Detection: The aim of the project is to detect images that have been digitally manipulated. Realistic image forgeries
involve a combination of splicing, resampling, region removal, smoothing and other manipulation methods.
Our method is agnostic to the type of manipulation and classifies an image as tampered or untampered. Completed with a accuracy of 60%
Python, CNN, Keras, Django, HTML & CSS