HDNet: A dual-stream network with progressive fusion for image hazing detection

Abstract

Image hazing refers to artificially adding haze to a clear image to produce a natural haze effect. However, it might also be used as an image forgery for malicious purposes such as misleading tourists or spreading fake weather information. Up to the present, there are still no works specially designed for fake hazy image detection. Inspired by the fact that almost all artificial hazy images are synthesized based on the atmospheric scattering model (ASM), we observe that though synthetic and real hazy images are very difficult to be differentiated by naked eyes, there still exist some ASM-related imprints in synthesized hazy images. Specifically, the subtle differences between synthetic hazy images and real hazy ones are image saturation and haze distribution. In this work, we propose a dual-stream image hazing detection network, namely HDNet, to distinguish synthetic hazy images from natural hazy ones. One stream explicitly learns features from the image saturation channel, and the other one learns haze distribution features from the dark channel and gray level co-occurrence matrix. Moreover, to fuse well the learned features from two streams, we propose a progressive attention mechanism. A stage-wise learning strategy is exploited for network training, in which the learning process is divided into four sub-tasks that are completed stage-by-stage. Extensive experimental results prove that the proposed HDNet achieves desirable detection accuracy and robustness.