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Image Inpainting Forgery Detection: A Review.

Adrian-Alin Barglazan¹, Remus Brad¹, Constantin Constantinescu¹

¹Faculty of Engineering, Computer Science, "Lucian Blaga" University of Sibiu, 550024 Sibiu, Romania.

Journal of Imaging

|February 23, 2024

Summary

This review examines machine learning-based image inpainting and forgery detection methods for object removal. It highlights artifacts from inpainting and assesses current detection techniques, datasets, and their limitations.

Keywords:

forensic forgery image inpainting object removal detection

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Area of Science:

Computer Vision
Machine Learning
Digital Forensics

Background:

Advancements in machine learning have improved image restoration but also enabled sophisticated image manipulation.
The internet's proliferation of manipulated multimedia information necessitates robust detection methods.

Purpose of the Study:

To comprehensively review inpainting algorithms and forgery detection techniques for object removal from digital images.
To analyze conventional and neural network-based methods, identify inpainting artifacts, and assess state-of-the-art detection technologies.

Main Methods:

Review of texture synthesis and neural network-based inpainting techniques.
Analysis of artifacts introduced by object removal via inpainting.
Assessment of current forgery detection methods and relevant datasets.

Main Results:

Inpainting techniques, while improving image quality, introduce detectable artifacts.
Existing detection methods vary in effectiveness against different inpainting strategies.
Comparative analysis of methods reveals current capabilities and constraints.

Conclusions:

Understanding inpainting artifacts is crucial for effective forgery detection.
The study provides a comprehensive overview of the state-of-the-art in detecting object removal in images.
Further research is needed to address the limitations of current detection methods.