01633nas a2200217   4500000000100000000000100001008004100002100001700043700001800060700001900078700001800097700001900115700001800134700002500152700001900177245011600196856004600312490000700358520103600365022001401401       2023                            d1 aAchraf Daoui1 aMohamed Yamni1 aTorki Altameem1 aMusheer Ahmad1 aMohamed Hammad1 aPawel Plawiak1 aRyszard Tadeusiewicz1 aAhmed El-Latif00aAuCFSR: Authentication and Color Face Self-Recovery Using Novel 2D Hyperchaotic System and Deep Learning Models  uhttps://www.mdpi.com/1424-8220/23/21/89570 v233 a<p>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.</p>
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