A lightweight image splicing tampering localization method based on MobileNetV2 and SRM

Abstract

The architectures of many state-of-the-art local tempering detection models are complexity, and the training process of those models is also time-consuming. Therefore, this paper constructs a lightweight local tampering detection method based on the convolutional network MobileNetV2 and a dual-stream network. Specifically, the algorithm first improves the MobileNetv2, which not only reduces the multiple of its downsampling operator to retain richer traces of image tampering, but also introduces the dilated convolution in it to expand the receptive field of feature maps. The dual-stream network uses RGB stream to extract image tampering features such as strong contrast difference and unnatural tampered boundaries, and implements spatial rich model (SRM) stream to extract image tampered area and noise features of real area. Finally, the features extracted from two streams are fused through an improved attention mechanism called parallel convolutional block attention module (CBAM), which can improve the sensitivity of the model to important features in RGB and SRM. The experimental results show that the proposed algorithm still has higher positioning accuracy than some existing algorithms, while achieving lightweight.