ERINet: efficient and robust identification network for image copy-move forgery detection and localization: Efficient and robust identification network.

Images can be maliciously manipulated to hide content or duplicate certain objects. Detecting an elaborate copy-move forgery is very challenging for both humans and machines, and current methods cannot detect copy-move images with the precision required, especially for pixel-level tampered images, which is a challenge for the current existing methods. In this paper we present our own dataset (CMF58K) - the first pixel-level copy-move dataset, which consists of 580,000 images covering copy-move tampered objects in various life scenes with more than 32 object classes. Furthermore, we propose a network for detecting and locating copy-move forgeries: Efficient and Robust Identification Network (ERINet). It mainly includes four main modules: the efficient feature pyramid network (EFPN), the residual receptive field block (RRFB), the hierarchical decoding identification (HDI), and the cascaded group-reversal attention (GRA) blocks. Considering the inevitable external factors of rotation, scaling, blurring, compression and noise can hide traces of tampering and increase the difficulty of detection, we applied MaxBlurPool to our network and obtained a strong robustness. ERINet outperforms various state-of-the-art manipulation detection baselines on four image manipulation datasets. The inference speed is ∼ 49 fps on a single GPU without I/O time on the test dataset. [ABSTRACT FROM AUTHOR]