# Fake Colorized Image Detection

arXiv: https://arxiv.org/abs/1801.02768 · HTML: https://mlsanity.com/paper/1801.02768/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2018.

## Built on by (most cited first, 7 of 7)

- [Generalizable AI-Generated Image Detection Based on Fractal Self-Similarity in the Spectrum](https://mlsanity.com/paper/2503.08484/) (2025)
- [Fake Visual Content Detection Using Two-Stream Convolutional Neural Networks](https://mlsanity.com/paper/2101.00676/) (2021)
- [Joint Learning of Deep Texture and High-Frequency Features for Computer-Generated Image Detection](https://mlsanity.com/paper/2209.03322/) (2022)
- [Incorporating Ensemble and Transfer Learning For An End-To-End Auto-Colorized Image Detection Model](https://mlsanity.com/paper/2309.14478/) (2023)
- [Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services](https://mlsanity.com/paper/2403.12045/) (2023)
- [Online Misinformation Detection in Live Streaming Videos](https://mlsanity.com/paper/2503.12627/) (2025)
- [Leveraging Hierarchical Image-Text Misalignment for Universal Fake Image Detection](https://mlsanity.com/paper/2511.00427/) (2025)

## Abstract (arXiv)

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 colorization process will inevitably affect the dark and bright channel values. Based on our observations, i.e., potential traces in the hue, saturation, dark and bright channels, we propose two simple yet effective detection methods for fake colorized images: Histogram based Fake Colorized Image Detection (FCID-HIST) and Feature Encoding based Fake Colorized Image Detection (FCID-FE). Experimental results demonstrate that both proposed methods exhibit a decent performance against multiple state-of-the-art colorization approaches.