A novel method for resampling detection in highly compressed JPEG images through BAR using a deep learning technique

Abstract

As JPEG has become a prevalent compression standard, identifying manipulations in JPEG images has become increasingly necessary. However, many of the inspected images are highly compressed, making it challenging for forensics tools and methodologies to detect clues of resampling. To overcome this issue, a new method has been proposed that uses a deep convolutional neural network (CNN) to detect image resampling by removing blocking artifacts (BAR) from recompressed images without altering resampling clues. The proposed framework has three stages: noise residual extraction, feature extraction, and classification. Initially, the image content and BAR are suppressed, and noise residuals from recompressed images are extracted using ten convolutional layers without pooling layers. The feature extraction phase then learns resampling features from the noise residuals in an adaptive manner. Deep resampling characteristics are processed through the fully connected layer to identify whether the input image is double compressed and resized (DCR) or double compressed and unresized (DCUR). The proposed deep CNN has been tested on heterogeneous datasets such as RAISE and DRESDEN, identifying resampling with a mean accuracy of 91.85% and 90.21% in cross-dataset settings. In addition, detecting downscaling with more than an accuracy of 87.98% in highly recompressed images indicates that it outperforms state-of-the-art approaches. Furthermore, the proposed method yields notable results in estimating the resampling factor in recompressed images.