Enhanced Forgery Detection in Digital Images Using Deep Learning

Enhanced Forgery Detection in Digital Images Using Deep Learning

CH. Lakshmi Kumari1, Bellam Saikumar2, Tharigoppula Shivaram3

1Assistant Professor, Mahatma Gandhi Institute of Technology

2,3UG Student, Mahatma Gandhi Institute of Technology

Abstract- In today’s digital era, social media platforms rely heavily on the sharing of images, making them a primary mode of communication and information dissemination. However, this prevalence has also given rise to the misuse of images through malicious tampering, such as creating fake or misleading information. Image forgery, a process that manipulates an image to deceive viewers, has significant implications for spreading false information, cybercrime, and even legal disputes. To counter this challenge, deep learning has proven to be a strong solution for image forgery detection. Convolutional Neural Networks (CNNs), a type of deep learning model, are especially good at identifying subtle inconsistencies in images. CNNs can analyze images at a pixel level, examining pixel-by-pixel data to identify unnatural patterns, inconsistencies, or tampering artifacts. CNN-based models can detect typical forgery types like splicing, copy-move, and image retouching by extracting and analyzing features such as edges, textures, and lighting inconsistencies.

Of numerous detection methods, Error Level Analysis (ELA) with Convolutional Neural Network (CNN) architectures has proven to be a viable method for detecting image tampering. The flexibility of CNNs to be trained on vast datasets of forged and real images enables proper classification and even the localization of tampered areas. This positions deep learning as a strong and effective weapon against the increasing threat of image forgery, facilitating increased trust in digital media.

Keywords: Image forgery, Pixel-level analysis, Splicing, Copy-move, Image retouching, Error Level Analysis (ELA), Convolutional Neural Networks (CNNs), Tampering artifacts.