Secure and robust watermarking algorithm for remote sensing images based on compressive sensing

Tong, Deyu; Ren, Na; Zhu, Chenchen

doi:10.1007/s11042-018-7014-1

Cited by 15 publications

(4 citation statements)

References 41 publications

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“…In addition, they further use Cyclic Redundancy Check (CRC) and block truncation coding (BTC) for modification location and recovery. In addition, Reversible watermarking is also used for sensitive applications such as military imagery [28] and remote sensing imagery [29], [30]. For example, the lossless watermarks have being incorporated as an integrity protection mechanism into the ISSE guard [31].…”

Section: B Reversible Watermarking Schemementioning

confidence: 99%

A Novel Hybrid Reversible-Zero Watermarking Scheme to Protect Medical Image

Dai

Lian

et al. 2022

IEEE Access

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The verification of copyright and authenticity for medical images is critical in telemedical applications. Watermarking is a key technique for protecting medical images and can be mainly divided into three categories: region of interest (ROI) lossless watermarking, reversible watermarking and zerowatermarking. However, ROI lossless watermarking causes biases on diagnosis. Reversible watermarking can hardly provide a continuous verification function and may face verification disputes after image recovering. Zero-watermarking requires third-party storage which may cause additional security problems. To address these issues, a hybrid reversible-zero watermarking (HRZW) is proposed in this paper to effectively combine the complementary advantages of reversible watermarking and zero-watermarking. In our scheme, a novel hybrid structure is designed including a zero-watermarking component and a reversible watermarking component. In the first component, ownership share is generated by mapping nearest neighbor grayscale residual (NNGR) based features and watermark information. In the second component, the generated ownership share is embedded reversibly based on Slantlet Transform, Singular Value Decomposition and Quantization Index Modulation (SLT-SVD-QIM). Experimental results demonstrate that our proposed scheme not only yields remarkable watermarking imperceptibility, distinguishability and robustness, but also provides continuous verification function without any dispute or third-party storage, which outperforms existing watermarking schemes for medical images.

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Section: B Reversible Watermarking Schemementioning

confidence: 99%

A Novel Hybrid Reversible-Zero Watermarking Scheme to Protect Medical Image

Dai

Lian

et al. 2022

IEEE Access

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“…Discrete Fourier transform (DFT), discrete wavelet transform (DWT), and discrete cosine transform (DCT) are typical frequency domain transform techniques [13,19]. Tong et al [20] used compressed sensing to pre-process the watermark information by dividing the remote sensing image into robust and variable domains using DWT and embedding the watermark information into the variable domain through a mapping mechanism, which is robust to cropping and noise attacks. Yuan et al [21] applied a three-dimensional DWT to the divided remote sensing images and embedded watermark information by means of a coefficient quantization.…”

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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“…Digital watermarking technology is considered an effective solution for protecting the copyright of 3D models (Pham et al 2018;Lee et al 2023). In the research of geospatial data security, digital watermarking technology has been primarily applied to vector geographic data (Hou et al 2018;Zhu et al 2021;Wang et al 2023;Ren et al 2020) and remote sensing image (Qin et al 2021;Fares et al 2020;Tong et al 2019). However, the research on data protection methods for 3D models is still in its early stages.…”

Section: Introductionmentioning

confidence: 99%

Precision Controllable Reversible Watermarking Algorithm for Oblique Photography 3D Models

Wang,

Zhang,

Zhang

et al. 2023

Preprint

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The existing reversible watermarking algorithms for the oblique photography 3D model can only restore the original model or not, which fails to meet the diverse needs of different users. Therefore, we propose a reversible watermarking algorithm for the oblique photography 3D model in this paper, aiming to achieve controlled model error recovery. Firstly, according to its global stability, the feature points of the oblique photography 3D model are extracted by utilizing the mean angle between vertex normals. Secondly, a mapping relationship is established based on the ratio of distances between feature points and non-feature points. Then, the vertices are grouped, with each group consisting of one feature point corresponding to several non-feature points. Finally, a spherical coordinate system is constructed with the feature point as the origin of each group. The watermark information is embedded by modifying the radius of the coordinate system. In the process of extracting the watermark, we extracted the watermark from different positions within the radius. This method recovers the model by obtaining different radii so that the method can achieve controlled data error. In addition, the experimental results have demonstrated that this algorithm can achieve controlled recovery of model error and exhibits good robustness against attacks such as translation, rotation, cropping, and simplification.

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Secure and robust watermarking algorithm for remote sensing images based on compressive sensing

Cited by 15 publications

References 41 publications

A Novel Hybrid Reversible-Zero Watermarking Scheme to Protect Medical Image

A Novel Hybrid Reversible-Zero Watermarking Scheme to Protect Medical Image

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

Precision Controllable Reversible Watermarking Algorithm for Oblique Photography 3D Models

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