Citation

Thabit, Rasha and Al-Askari, Mohanad A. and Mohammed, Dunya Zeki and Anaam, Elham Abdulwahab and Mahmood, Zainab H. and Jabbar, Dina Jamal and Salih, Zahraa Aqeel (2025) Face image authentication scheme based on MTCNN and SLT. Multimedia Tools and Applications. ISSN 1573-7721

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Abstract

DeepFakes and face image manipulation methods have been widely distributed in the last few years and several techniques have been presented to check the authenticity of the face image and detect manipulation if exists. Most of the available manipulation detection techniques have been successfully applied to reveal one type of manipulation under specifc conditions, however, many limitations and challenges can be encountered in this feld. To overcome some limitations and challenges, this paper presents a new face image authentication (FIA) scheme based on Multi-Task Cascaded Conventional Neural Networks (MTCNN) and watermarking in Slantlet transform (SLT) domain. The proposed FIA scheme has three main algorithms that are face detection and selection, embedding, and extraction algorithms. Diferent block sizes have been used to divide the image into non-overlapping blocks followed by classifying them into two groups that are blocks from face area (FA) and blocks from the remaining area (RA) of the image. In the embedding algorithms, the authentication information is generated from FA blocks and embedded in the RA blocks. In the extraction algorithms, the embedded information is extracted from RA blocks and compared with the calculated data from FA blocks to reveal manipulations and localize the manipulated blocks if exist. Extensive experiments have been conducted to evaluate the performance of the proposed FIA scheme for diferent face images. The experimental work included tests for payload, capacity, visual quality, time complexity, and localization of manipulations. The results proved the efciency of the proposed scheme in detecting and localizing diferent face image manipulations such as attributes attacks, retouching attacks, expression swap, face swap, and morphing attacks. The proposed scheme overcomes many limitations and it is 100% accurate in localizing the tampered blocks which makes it a better candidate for practical applications.

Item Type:	Article
Uncontrolled Keywords:	Deep Fakes detection , face manipulation detection
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	28 Mar 2025 03:47
Last Modified:	28 Mar 2025 03:47
URII:	http://shdl.mmu.edu.my/id/eprint/13645

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