Self-Recovery based Dual Layer Fragile Image Watermarking

Ensuring the validity and recoverability of digital images is vital in contexts where visual data integrity is paramount. In order to address this need, this work proposed a dual-layer fragile watermarking system for safe image authentication, precise tamper localization, and self-recovery. The proposed framework produces watermark information utilizing a 4D-hypercomplex number transformation (4D-HCNT) grounded on quaternionic algebra, in conjunction with the Fast Fourier Transform (FFT) and logarithmic differencing mapping (LDM). In level-1, authentication bits are encoded utilizing a hybrid singular value decomposition (SVD) method combined with Rivest-Shamir-Adleman (RSA) cryptographic protocol to facilitate dependable integrity verification. In the level-2, recovery bits are integrated by the combination of discrete wavelet transform (DWT) and principal component analysis (PCA), enhancing imperceptibility while preserving robustness against distortions. Tamper detection is accomplished by contrasting retrieved watermark bits with regenerated watermark bits, facilitating accurate identification of altered areas. Following detection of tampering, the compromised areas are restored via a hybrid inpainting technique couples with 255-color range scaling (255-CRS) for adaptive restoration. Experimental findings indicate exceptional visual quality, evidenced by an average peak-signal-to-noise-ration (PSNR) of 52.21 dB, and structural similarity index matrix (SSIM) of 0.9990. Moreover, this framework has also achieved an average MSE, UIQI, AD, NCC, LMSE, and NAE of 0.2961, 0.9931, 0.3586, 0.9843, 0.7698, and 0.0062 respectively. The framework sustains a payload capacity of 1 bit per pixel (bpp) and attains a high-fidelity recovery for images with distortion levels of up to 50%. Overall, the proposed framework delivers efficient image authentication, precise tamper localization, and dependable self-recovery, surpassing current methods in computing efficiency and perceptual quality.