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Image Forensics in the Encrypted Domain.

Yongqiang Yu^1,2, Yuliang Lu^1,2, Longlong Li^1,2

¹College of Electronic Engineering, National University of Defense Technology, Hefei 230037, China.

Entropy (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

Detecting forged encrypted images is crucial. This study introduces image forensics in the encrypted domain (IFED) and a deep learning model, the lightweight enhanced forensic network (LEFN), to identify copy-move attacks in encrypted images.

Keywords:

copy–move detection deep learning encrypted domain image forensics multimedia security

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Area of Science:

Digital Forensics
Image Processing
Cryptography

Background:

Traditional forensic tools struggle with encrypted multimedia.
Encryption by forgers poses a significant challenge to digital forensics.
Image Forensics in the Encrypted Domain (IFED) is an emerging and critical research area.

Purpose of the Study:

To formally introduce and define Image Forensics in the Encrypted Domain (IFED).
To address the challenge of detecting copy-move forgery in encrypted images using permutation encryption.
To propose a novel deep learning-based solution for automatic IFED.

Main Methods:

Formal definition and evaluation metrics for IFED were established.
A classic permutation encryption technique was employed to simulate forged encrypted images.
A lightweight enhanced forensic network (LEFN) based on deep learning was developed for forgery detection.

Main Results:

The proposed LEFN model demonstrates effectiveness in detecting copy-move alterations in encrypted images.
Extensive experiments validated the performance and robustness of the LEFN scheme.
The study provides a foundational framework for IFED.

Conclusions:

The developed LEFN offers a viable solution for automatic image forensics in the encrypted domain.
IFED is essential for combating sophisticated digital forgeries.
This research advances the field of digital forensics for encrypted multimedia.