AuSR1 : Authentication and self-recovery using a new image inpainting technique with LSB shifting in fragile image watermarking

Aminuddin, Afrig and Ernawan, Ferda (2022) AuSR1 : Authentication and self-recovery using a new image inpainting technique with LSB shifting in fragile image watermarking. Journal of King Saud University - Computer and Information Sciences. pp. 1-19. ISSN 1319-1578. (In Press) (In Press)

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With the rapid development of multimedia technology, editing and manipulating digital images have become more accessible than ever. This paper proposed color image authentication based on blind fragile image watermarking for tamper detection and self-recovery named AuSR1. The AuSR1 divides each channel of the cover image into non-overlapping blocks with the size of 2 × 2 pixels. The authentication data is embedded into the original block location, while the recovery data is embedded into the distant location from the original location based on the block mapping algorithm. The watermark data is then embedded into the 2 LSB to achieve high quality of the recovered image under tampering attacks. In addition, the permutation algorithm is applied to ensure the security of the watermark data. The AuSR1 utilizes a three-layer authentication algorithm to achieve a high detection rate. The experimental results show that the scheme produced a PSNR value of 45.57 dB and an SSIM value of 0.9972 of the watermarked images. Furthermore, the AuSR1 detected the tampered area of the images with a high precision value of 0.9943. In addition, the recovered image achieved a PSNR value of 27.64 dB and an SSIM value of 0.9339 on a 50% tampering rate.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Blind fragile watermarking; Image authentication; Image inpainting; Self-embedding; Self-recovery
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > TA Engineering (General). Civil engineering (General)
Faculty/Division: Faculty of Computing
Depositing User: Mr Muhamad Firdaus Janih@Jaini
Date Deposited: 14 Apr 2022 04:00
Last Modified: 14 Apr 2022 04:00
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