Screen-shot and Demoiréd image identification based on DenseNet and DeepViT

Abstract

How to identify the screen-shot image is an important branch of image source forensics. Although the physical feature of moiré patterns may be left after LCD recapture operation, this feature may also be well concealed with the development of deep learning-based techniques for demoiréing operation. In this paper, to solve the forensic problem of screen-shot and demoiréd image operation, we select several typical demoiréing algorithms to generate datasets for training and detecting demoiréd images. Meanwhile, we also perform the task of identifying specific demoiréing algorithms. DenseNet can extract fine demoiréd image features because of its enhanced feature reuse and improved model performance. The re-attention mechanism is also added to our designed network to extract the image’s global features and avoid the self-attention collapse problem in ViT. Therefore, we design a network based on DenseNet cascaded with DeepViT structure to detect the demoiréd image and identify its corresponding demoiréing algorithm. In a large number of comparison experiments of various typical image recapture detection methods, our method achieves the best detection results and network performance.