Global Contrast Enhancement Detection via Deep Multi-Path Network

Identifying global contrast enhancement in an image is an important task in forensics estimation. Several previous methods analyze the "peak-gap" fingerprints in graylevel histograms. However, images in real scenarios are often stored in the JPEG format with middle/low compression quality, resulting in less obvious "peak-gap" effect and then unsatisfactory performance. In this paper, we propose a novel deep Multi-Path Network (MPNet) based approach to learn discriminative features from graylevel histograms. Specifically, given the histograms, their high-level peaks and gaps information can be exploited effectively after several shared convolutional layers in the network, even in middle/low quality compressed images. Moreover, the proposed multi-path module is able to focus on dealing with specific forensics operations for more robustness on image compression. The experiments on three challenging datasets (i.e., Dresden, RAISE and UCID) demonstrate the effectiveness of the proposed method compared to existing methods.