Detecting Digital Forgery Images using Transf...

Detecting Digital Forgery Images using Transfer Learning Technique

Author Name : Ranganath, Miss. Arpita Swamy, Miss. Anisha Elmalkar, Miss. Akshitha

ABSTRACT In today's digital age, social media platforms rely heavily on digital images for information sharing. However, the widespread use of image manipulation tools poses significant threats, enabling the dissemination of fake information. This highlights the need for advanced techniques to detect digital image forgery. Traditional approaches often target specific forgery types, such as image splicing or copy-move, but are limited in addressing diverse real-world scenarios where multiple forgeries coexist. This research explores state-of-the-art convolutional neural networks (CNNs) like InceptionV3, VGG16, DenseNet, and more, utilizing the CASIA dataset. A hybrid model combining Xception and NASNet Mobile is proposed, demonstrating improved detection accuracy and robustness. The findings suggest that hybrid models hold great potential for combating forgery, paving the way for secure information exchange across digital platforms