AuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models

TytułAuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models
Publication TypeJournal Article
Rok publikacji2023
AutorzyDaoui A, Yamni M, Altameem T, Ahmad M, Hammad M, Plawiak P, Tadeusiewicz R, El-Latif AAAbd
JournalSensors
Volume23
ISSN1424-8220
Abstract

Color face images are often transmitted over public channels, where they are vulnerable to tampering attacks. To address this problem, the present paper introduces a novel scheme called Authentication and Color Face Self-Recovery (AuCFSR) for ensuring the authenticity of color face images and recovering the tampered areas in these images. AuCFSR uses a new two-dimensional hyperchaotic system called two-dimensional modular sine-cosine map (2D MSCM) to embed authentication and recovery data into the least significant bits of color image pixels. This produces high-quality output images with high security level. When tampered color face image is detected, AuCFSR executes two deep learning models: the CodeFormer model to enhance the visual quality of the recovered color face image and the DeOldify model to improve the colorization of this image. Experimental results demonstrate that AuCFSR outperforms recent similar schemes in tamper detection accuracy, security level, and visual quality of the recovered images.

URLhttps://www.mdpi.com/1424-8220/23/21/8957
DOI10.3390/s23218957

Historia zmian

Data aktualizacji: 15/12/2023 - 15:27; autor zmian: Paweł Pławiak (pplawiak@iitis.pl)