Multi-Domain Information-Theoretic Features and Kolmogorov Complexity for Lightweight Image Splicing Detection

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0201229" target="_blank" >RIV/00216305:26220/26:0201229 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1109/icumt67815.2025.11268583" target="_blank" >http://dx.doi.org/10.1109/icumt67815.2025.11268583</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/icumt67815.2025.11268583" target="_blank" >10.1109/icumt67815.2025.11268583</a>

Result language

angličtina

Original language name

Multi-Domain Information-Theoretic Features and Kolmogorov Complexity for Lightweight Image Splicing Detection

Original language description

Image splicing is a prevalent form of digital forgery that challenges the reliability of visual content across forensic, legal, and media platforms. In this study, a novel and lightweight image splicing detection framework has been proposed grounded in multi-domain information-theoretic principles. Unlike deep learning-based methods that require large datasets and high-end GPU resources, the proposed approach leverages handcrafted entropy, mutual information, and complexity-based features that are computationally efficient and interpretable. The proposed framework extracts 20 features from spatial, cross-channel, and multi-scale domains—highlighting entropy variations, statistical dependencies, and information complexity. Notably, Kolmogorov complexity approximation, edge entropy, and multi-scale mutual information are incorporated as discriminative indicators of tampering. The model is evaluated on the Columbia Image Splicing Detection Dataset using ten classical machine learning algorithms. A maximum AUC-ROC of 0.934, cross-validation performance of 84.29%(±4.19%) and test accuracy of 85.32% with a strong F1-score of 0.8571 were achieved with classical machine learning classifiers, demonstrating competitive performance without deep models. Feature importance analysis further improves interpretability by ranking the most significant contributors. The results validate proposed framework as a reliable, resource-efficient, and explainable alternative to complex end-to-end deep learning pipelines for splicing detection.

Czech name

—

Czech description

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Type

O - Miscellaneous

CEP classification

—

OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

<a href="/en/project/CK04000027" target="_blank" >CK04000027: Traffic controll system of new generation (SENDER)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů