A High-Performance Robust Reversible Data Hiding Algorithm Based on Polar Harmonic Fourier Moments

Aiming at the problem of most Robust Reversible Data Hiding (RRDH) schemes failing to anti geometric deformation attacks, a new RRDH algorithm based on Polar Harmonic Fourier Moments (PHFMs) is presented in this paper, thereby enhancing both the robustness of the embedded data and perceptual quality of the data-embedded image. Firstly, by leveraging the anti-geometric transformation and high-fidelity features of PHFMs, the image is transformed into its frequency domain for RRDH. Then, a quantitation index modulation (QIM) algorithm is designed to embed secret data into the integer part of PHFMs coefficients. By minimizing the differences between the secret-data-embedded image and the original image, the amount of compensation data is reduced. Meanwhile, a two-dimensional RDH scheme is further adopted to embed the compensation data, thus reducing the distortion of the full data-embedded image. Finally, the robustness of the embedded data and the fidelity of the full data-embedded image are both improved. The combination of PHFMs transformation and two-dimensional RDH enables the proposed RRDH algorithm to achieve high visual quality and strong resistance capability against geometric transformation attacks. Extensive experimental results demonstrate that the proposed RRDH algorithm outperforms other state-of-the-art techniques.