Feature enhancement and supervised contrastive learning for image splicing forgery detection

Image forgery detection remains a challenging task due to the variation in the scale of the tampered areas and the forensic clues that are obscured by various post-processing operations. Most existing deep learning methods rely only on single-scale high-level features and ignore the correlations of the pixels in intra-image and inter-image. Due to this limitation, previous methods are considered unsuitable for multi-scale splicing forgery detection. To fill this gap, we propose a novel model for improving multi-scale splicing forged regions localization by utilizing multi-level multi-scale feature enhancement and pixel-level supervised contrastive learning. First, we introduce a multi-level multi-scale feature enhancement module (MFEM) to integrate the multi-level information and capture the multi-scale global contextual representation by embedding an improved atrous spatial pyramid pooling (ASPP) mechanism into the non-local module. It strengthens the capability of the model to sense multi-scale tampered regions. Second, the pixel-level supervised contrastive learning mechanism is designed to separately cluster the pixel representations of tampered and real regions within and across images. It improves intra-class compactness and inter-class separability of the pixel embedding space significantly and enhances feature expression capabilities. Third, we design a multi-loss progressive learning (MPL) strategy to integrate the complementary advantages of multi-loss functions to optimize the scale and position parameters of the tampered regions during the training process. Extensive experiments have shown that the proposed model outperforms state-of-the-art methods. It can effectively detect and segment multi-scale tampered regions, even for noisy and JPEG-compressed images.