Image Forgery Detection with Machine Learning...

Image Forgery Detection with Machine Learning: Leveraging CNN and ELA

Author Name : Sreenivasan S, Nallakukala Dilli Babu, Harsh Raj, Pratyush Banerjee

ABSTRACT Images are frequently altered or manipulated to serve the interests of specific individuals or groups. Since images are often regarded as evidence of truth or reality, their manipulation can significantly contribute to the spread of misinformation. This is particularly concerning in the context of fake news and publications that exploit manipulated images to mislead audiences. Such deceptive practices can have far-reaching consequences, including influencing public opinion, swaying political support, or even impacting stock prices for the benefit of those disseminating the false information. Detecting image manipulation is a complex task that requires substantial datasets and robust computational models capable of analyzing every pixel in an image. To achieve reliable detection, the process demands not only accuracy but also efficiency and adaptability, especially for practical, real-world applications. This challenge can be effectively addressed by leveraging big data and deep learning technologies