High dynamic range image forensics using cnn

TL;DR

This paper introduces a novel CNN-based method for HDR image forensics, capable of distinguishing images generated from multiple LDR sources versus single LDR images expanded by inverse tone mapping, demonstrating improved accuracy over traditional methods.

Contribution

First application of deep learning to HDR image forensics, proposing a CNN architecture for source identification of HDR images.

Findings

CNN outperforms traditional statistical methods in HDR source classification

Proposed method effectively distinguishes HDR images from different generation processes

Demonstrates the potential of deep learning in multimedia forensics

Abstract

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.