Robust Manipulation Detection Scheme for Post-JPEG Compressed Images using CNN

Abstract

Due to the popularity of JPEG as a compression standard, the importance of detecting manipulation in JPEG images has increased. Many data-driven approaches are implemented to detect manipulation of uncompressed scenario; however, the performance degraded significantly in re-compressed images due to lossy compression. To achieve this, a deep residual framework is designed to provide the highest-quality manipulation detection in re-compressed images (MDRNet). The framework undergoes three folds: noise residual extraction, feature extraction, and classification. Firstly, the noise residual extraction stage significantly expands the front-end detector, including three residual blocks with skip connections that can generate noise residuals by suppressing the image content and enhancing the manipulation traces. Next, two efficient residual blocks with cross feature learning strategy to obtain the deep manipulation features and fed to fully connected layers for classification. Experiments are performed on ten alterations followed by different quality factors to enhance forgery detection in the real-world scenario. Further, the proposed MDRNet achieves superior performance compared to the state-of-the-art baselines, precisely the most challenging case with lossy post-JPEG compression.