Fake Colorized Image Detection
Yuanfang Guo, Xiaochun Cao, Wei Zhang, Rui Wang

TL;DR
This paper introduces two novel forensic methods to detect colorized images by analyzing statistical differences in hue, saturation, dark, and bright channels, addressing a new challenge in digital image forensics.
Contribution
The paper presents the first techniques specifically designed to identify colorized images, leveraging statistical inconsistencies in multiple color channels.
Findings
Both methods outperform existing techniques in detecting colorized images.
The proposed methods are effective against multiple state-of-the-art colorization algorithms.
Detection accuracy remains high across diverse datasets.
Abstract
Image forensics aims to detect the manipulation of digital images. Currently, splicing detection, copy-move detection and image retouching detection are drawing much attentions from researchers. However, image editing techniques develop with time goes by. One emerging image editing technique is colorization, which can colorize grayscale images with realistic colors. Unfortunately, this technique may also be intentionally applied to certain images to confound object recognition algorithms. To the best of our knowledge, no forensic technique has yet been invented to identify whether an image is colorized. We observed that, compared to natural images, colorized images, which are generated by three state-of-the-art methods, possess statistical differences for the hue and saturation channels. Besides, we also observe statistical inconsistencies in the dark and bright channels, because the…
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