High dynamic range image forensics using cnn
This addresses a forensic challenge in multimedia for detecting manipulated HDR images, but it is incremental as it applies an existing deep learning method to a new domain.
The paper tackles the problem of identifying the source of high dynamic range (HDR) images by distinguishing those generated from multiple low dynamic range (LDR) images from those expanded from a single LDR image using inverse tone mapping, achieving efficiency in classification as shown by comparison with traditional statistical methods.
High dynamic range (HDR) imaging has recently drawn much attention in multimedia community. In this paper, we proposed a HDR image forensics method based on convolutional neural network (CNN).To our best knowledge, this is the first time to apply deep learning method on HDR image forensics. The proposed algorithm uses CNN to distinguish HDR images generated by multiple low dynamic range (LDR) images from that expanded by single LDR image using inverse tone mapping (iTM). To do this, we learn the change of statistical characteristics extracted by the proposed CNN architectures and classify two kinds of HDR images. Comparision results with some traditional statistical characteristics shows efficiency of the proposed method in HDR image source identification.