NVMS-Net: A Novel Constrained Noise-View Multiscale Network for Detecting General Image Processing Based Manipulations

Ensuring the authenticity and integrity of digital images is a major concern in multimedia forensics, driving research on universal schemes for detecting diverse image manipulations (or processing operations). Although some prior works have addressed general-purpose image manipulation detection, they have been evaluated under constrained environments. Developing an approach that effectively identifies multiple manipulations in real-world scenarios remains a challenge. To address this issue, in this article, we have designed a novel constrained noise-view based multiscale network (NVMS-Net) that jointly exploits constrained noise-view and multiscale feature learning for multiple image processing operation detection. Our NVMS-Net includes four stages: constrained noise-view, generalizable feature learning, multiscale feature learning, and classification. First, a noise extraction layer (NEL) is employed to suppress the image content information for the extraction of noise features. The statistical modeling of these noise features is further improved by statistical modeling layer (SML) to achieve a better noise-view of image tampering artifacts. Then, we use multiple convolution-batch normalization-ReLU (CBR) blocks to learn generalizable noise features that are sensitive to image manipulations. Further, a multiscale aggregation module (MSAM) is proposed that leverages image tampering features at multiple scales through the aggregation of low and high-level features. The extensive experimental results show that the NVMS-Net consistently outperforms the existing approaches on different dataset settings. Importantly, NVMS-Net shows better performance in real-world scenarios, particularly against antiforensic techniques and adversarial attacks. The proposed NVMS-Net provides an overall accuracies of 98.21% and 98.60% on BOSSBase and Dresden datasets, respectively.