Image Manipulation Detection Using Residual and Dense Connection-based Deep Learning Models

Digital image forgery poses a security threat by manipulating personal data shared on open networks in recent years. In this study, the performance of deep learning models with residual or dense connections, with or without transfer learning, for the classification problem of multiple image manipulations with dual JPEG compression, has been investigated. Transfer learning has produced higher and more stable results at different compression rates. The average accuracy values of the proposed model with dense connections were obtained as 0.9920, 0.9953, and 0.9907 for compression quality factors of 75, 85, and 95, respectively. These values were higher compared to similar studies in the literature. © 2024 IEEE.