An adaptive watermarking for remote sensing images based on maximum entropy and discrete wavelet transformation

Abstract

Most frequency-domain remote sensing image watermarking algorithms embed watermarks at random locations, which have negative impact on the watermark invisibility. In this study, we propose an adaptive watermarking scheme for remote sensing images that considers the information complexity to select where to embed watermarks to improve watermark invisibility without affecting algorithm robustness. The scheme converts remote sensing images from RGB to YCbCr color space, performs two-level DWT on luminance 𝑌𝑌, and selects the high frequency coefficient of the low frequency component ( 𝐻𝐻𝐻𝐻 2 𝑌𝑌 ) as the watermark embedding domain. To achieve adaptive embedding, 𝐻𝐻𝐻𝐻 2 𝑌𝑌 is divided into several 8*8 blocks, the entropy of each sub-block is calculated, and the block with the maximum entropy is chosen as the watermark embedding location. During embedding phase, the watermark image is also decomposed by two-level DWT, and the resulting high frequency coefficient (𝐻𝐻𝐻𝐻 2 𝑊𝑊 ) is then embedded into the block with maximum entropy using 𝛼𝛼-blending. The experimental results show that the watermarked remote sensing images have high fidelity, indicating good invisibility. Under varying degrees of geometric, cropping, filtering, and noise attacks, the proposed watermarking can always extract high identifiable watermark images. Moreover, it is extremely stable and impervious to attack intensity interference.