Detecting facial image forgeries with transfer learning techniques - Zanje, Nishigandha N.; Bongale, Anupkumar M; Dharrao, Deepak (Institute of Advanced Engineering and Science, 2024)
Digital images have become ubiquitous in our daily lives, appearing on our smartphone screens and online websites. They are widely used in numerous industries, including media, forensic and criminal investigations, medicine, and more. The ease of access to consumer photo editing tools has made it simple to manipulate images. However, such altered images pose a serious risk in fields where image authenticity is crucial, making it challenging to confirm the reliability of digital images. Digital image fraud involves altering an image's meaning without leaving any obvious signs. In this study, we present three convolutional neural network-based transfer learning techniques “CNN” classification of facial image forgeries, using VGG-19, InceptionV3, and DenseNet201. Among these methods, DenseNet201 achieved the highest accuracy of 99%, followed by InceptionV3 at 94% and VGG-19 at 84%.