Robust Reversible Watermarking Based on Local Bessel–Fourier Moments and Adaptive Embedding

Robust Reversible Watermarking methods can achieve watermark extraction and lossless image restoration without being attacked. Existing Robust Reversible Watermarking methods based on image moments mainly achieve robustness against geometric attacks such as rotation and scaling by embedding watermarks in the global inscribed circle of the image. However, these methods cannot deal with cropping, which is a practical application scenario of geometric attacks. To address this limitation, this paper proposes a new multi-region embedding Robust Reversible Watermarking framework that resists cropping attacks through the deep coupling of the feature points domain and the local Bessel–Fourier moments domain. Experimental results show that the proposed method can not only resist cropping attacks but also has good robustness against other typical attacks.