Lossless robust image watermarking by using polar harmonic transform

Robust Watermarking Algorithm against the Geometric Attacks based on Non-Subsampled Shearlet Transform and Harris-Laplace Detector

“…e redundancy of analysis is very large [12,13,18] Based on curvelets It is so efficient for natural images whose edge lines are dominated by curves Curvelets have high redundancy [14,19] Based on contourlets Contourlets inherits the benefits from the ridgelet transform and the curvelet transform Contourlets have no translation invariance [15,16,[20][21][22][23] Based on shearlet Shearlet possesses the characteristics of multi-scale and the ability to capture the geometry of multidimensional data e discrete shearlet transform may produce the pseudo-Gibbs phenomenon [24,25] Based on PHFMs PHFMs are image rotation invariant It would lead to higher numerical errors when processing smaller images [26,27] Based on Harris detection Harris feature points have good rotation invariance Harris feature points are short of the scale invariance and affine invariance [28] Based on Harris-Laplace detection…”

“…e robustness of this algorithm against the geometric attacks has been improved by relying on the geometric invariance of an accurate PHFMs. Hu and Xiang took advantage of the polarity harmonic transformation of reversible and robust watermarking techniques to achieve the lossless robust watermarking [25]. However, in the method of polar harmonic invariant domain, since the interpolation caused by the invariant domain increases the synchronization error, the watermark embedding and the watermark detection are not aligned.…”

A Domain-independent Dual-image based Robust Reversible Watermarking

“…Otherwise, the hidden secret data could be fully extracted even if the reversibility is lost. In recent years, several RRW schemes are proposed [30][31][32][33][34][35][36][37][38][39][40][41][42] based on a single image as the carrier, which could mainly be classified into three categories: (1) histogram rotation (HR) based schemes [30][31], (2) schemes based on the modifications of traditional HS [32][33], (3) two-stage RRW framework based schemes [34][35][36][37][38][39][40][41][42]. The HR based methods [30,31] accomplish robust lossless watermarking by slightly rotating the centroid vectors of two random regions in the non-overlapping blocks.…”

“…The method could further increase the robustness of the watermark by combining the SLT and the singular values decomposition (SVD). To improve the robustness for resisting most geometric attacks (e.g., rotation and scaling), Hu et al proposed novel RRW schemes [37,39] to achieve the robustness to resist the geometric attacks by employing the polar harmonic transform (PHT) and Zernike moments (ZMs). Because the embedding phases of the two stages are performed on the same domain, the watermark would be influenced by the reversible embedding stage.…”

Color image watermarking using spatio-chromatic complex Hadamard transform in sequency domain

“…The results are obtained imperceptible as visual context and algorithm bears a number of attacks over traditional procedures (Hosny et al , 2021). A new watermarking approach has been designed to insert the secret bits in the moments of the polar harmonic transform and lossless image recovered without any attack and discussed the algorithm bear against geometrical deformations (Hu and Xiang, 2021). Most of the earlier works related to digital image watermarking grayscale images were focused (Agarwal et al , 2015; Li et al , 2013; Wei and Ngan, 2009; Zong et al , 2015).…”