ORCID

Abstract

In today’s digital era, the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation. Unfortunately, this progress has also given rise to the misuse of manipulated images across various domains. One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images. This paper offers a comprehensive survey of existing methodologies for detecting image tampering, shedding light on the diverse approaches employed in the field of contemporary image forensics. The methods used to identify image forgery can be broadly classified into two primary categories: classical machine learning techniques, heavily reliant on manually crafted features, and deep learning methods. Additionally, this paper explores recent developments in image forensics, placing particular emphasis on the detection of counterfeit colorization. Image colorization involves predicting colors for grayscale images, thereby enhancing their visual appeal. The advancements in colorization techniques have reached a level where distinguishing between authentic and forged images with the naked eye has become an exceptionally challenging task. This paper serves as an in-depth exploration of the intricacies of image forensics in the modern age, with a specific focus on the detection of colorization forgery, presenting a comprehensive overview of methodologies in this critical field.

Publication Date

2025-07-30

Publication Title

Computers, Materials and Continua

Volume

84

Issue

3

ISSN

1546-2218

Acceptance Date

2025-07-19

Deposit Date

2025-08-08

Funding

We thank the anonymous reviewers for their valuable suggestions that improved the quality of this article. This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A3049788).

Keywords

Image colorization, convolutional neural network, deep learning, digital image forgery, generative adversarial network, image forensic, machine learning

First Page

4195

Last Page

4221

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