A Watermarking Algorithm for Remote Sensing Image Based on DFT and Watermarking Segmentation

Li, Li Li; Sun, Jin

doi:10.4028/www.scientific.net/amr.433-440.2504

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…The method's resilience was all-encompassing, standing up well to geometric attacks and noise attacks, etc. Li et al [22] performed pretreatment normalization on remote sensing images, selected an invariant region for DFT, split the watermark into two parts, and embedded these two parts in the phase and magnitude of the DFT, respectively. This method is resistant to compression and geometric attacks.…”

Section: Introductionmentioning

confidence: 99%

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

2024

KSII TIIS

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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.

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Section: Introductionmentioning

confidence: 99%

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

2024

KSII TIIS

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“…Frequency domain–based techniques, such as the commonly used discrete cosine transform (DCT), discrete wavelet transform (DWT), and discrete Fourier transform (DFT), utilize different transforms to embed watermarks in the modified frequency coefficients. However, these methods cannot describe local structures efficiently due to a lack of the phase information; thus, they are vulnerable to geometric manipulations.…”

Section: Introductionmentioning

confidence: 99%

“…8 Thus, due to the influence difference of the operations for the colour remote sensing image, these statistical feature-based watermarking methods that focus on the single band are sensitive to image manipulations. 9 Frequency domain-based techniques, such as the commonly used discrete cosine transform (DCT), 10 discrete wavelet transform (DWT), 11 and discrete Fourier transform (DFT), 12 utilize different transforms to embed watermarks in the modified frequency coefficients. However, these methods cannot describe local structures efficiently due to a lack of the phase information 13,14 ; thus, they are vulnerable to geometric manipulations.…”

mentioning

confidence: 99%

Digital watermarking scheme for colour remote sensing image based on quaternion wavelet transform and tensor decomposition

Che

Luo

et al. 2019

Math Methods in App Sciences

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Digital watermarking is important for protecting the intellectual property of remote sensing images. Unlike watermarking in ordinary colour images, in colour remote sensing images, watermarking has an important requirement: robustness. In this paper, a robust nonblind watermarking scheme for colour remote sensing images, which considers both frequency and statistical pattern features, is constructed based on the quaternion wavelet transform (QWT) and tensor decomposition. Using the QWT, not only the abundant phase information can be used to preserve detailed host image features to improve the imperceptibility of the watermark, but also the frequency coefficients of the host image can provide a stable position to embed the watermark. To further strengthen the robustness, the global statistical feature structure acquired through the tensor Tucker decomposition is employed to distribute the watermark's energy among different colour bands. Because both the QWT frequency coefficients and the tensor decomposition global statistical feature structure are highly stable against external distortion, their integration yields the proposed scheme, which is robust to many image manipulations. A simulation experiment shows that our method can balance the trade‐off between imperceptibility and robustness and that it is more robust than the traditional QWT and discrete wavelet transform (DWT) methods under many different types of image manipulations.

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A Robust Remote Sensing Image Watermarking Algorithm Based on Region-Specific SURF

Bhatti

Yuan

et al. 2022

Lecture Notes in Networks and Systems

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A Watermarking Algorithm for Remote Sensing Image Based on DFT and Watermarking Segmentation

Cited by 10 publications

References 7 publications

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

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

Digital watermarking scheme for colour remote sensing image based on quaternion wavelet transform and tensor decomposition

A Robust Remote Sensing Image Watermarking Algorithm Based on Region-Specific SURF

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