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Updated: Jan 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Multi-scale and deeply supervised network for image splicing localization.

Sheng Qin^1,2, Ce Liang^1,2, Yuling Luo^1,2

¹Guangxi Key Lab of Brain-Inspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin, China.

Frontiers in Artificial Intelligence

|October 13, 2025

Summary

This summary is machine-generated.

Detecting tampered images is crucial for national security. A new multi-scale network accurately locates image splicing tampering using deep supervision and global feature extraction.

Keywords:

deep learning encoder–decoder image forensics image splicing multi-scale

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Area of Science:

Computer Vision
Digital Forensics
Image Processing

Background:

Malicious image tampering poses risks to media integrity and national security.
Existing methods for image splicing tampering localization lack sufficient global information interaction.
Accurate detection and localization of tampered regions are necessary.

Purpose of the Study:

To propose an effective method for localizing image splicing tampering.
To address the limitations of existing methods by incorporating global information.
To develop a network capable of pixel-wise prediction of tampered regions.

Main Methods:

A multi-scale, deeply supervised image splicing tampering localization network based on an encoder-decoder architecture.
Utilizing different levels of feature maps for deep supervision of splicing locations.
Incorporating a multi-scale feature extraction module for expanded global view.

Main Results:

The proposed network achieved F1 scores of 0.891 on the CASIA dataset and 0.864 on the COLUMB dataset.
The model demonstrated accurate localization of tampered regions.
The multi-scale feature extraction module improved the differentiation between tampered and non-tampered areas.

Conclusions:

The proposed multi-scale, deeply supervised network is effective for image splicing tampering localization.
The integration of global information and multi-scale features enhances detection accuracy.
The method provides a robust solution for identifying maliciously tampered images.