Zero Watermarking Algorithm for Vector Geographic Data Based on the Number of Neighboring Features

Zhou, Qian-Mei; Zhu, Chenchen; Ren, Na; Chen, Weitong; Gong, Weiteng

doi:10.3390/sym13020208

Cited by 14 publications

(10 citation statements)

References 26 publications

(34 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Both watermark generation and watermark extraction belong to the procedure of watermark construction but occur at different times [32]. In the stage of watermark generation, the watermark is constructed from the original data to register in the IPR repository.…”

Section: Watermark Generation and Extractionmentioning

confidence: 99%

“…Figure 4b shows the edges of the data. The projection of the data is Gauss-Krüger projection [35,36] Meanwhile, it is necessary to introduce the threshold of the NC, an empirical value [32]. When the NC is larger than or equal to the predefined threshold, the suspicious data corresponding to W is considered infringing data.…”

Section: Datasetsmentioning

confidence: 99%

“…First, in the parameter settings, we set the watermark length N to 32 and the threshold of NC to 0.75. It should be noted that the threshold of NC is an empirical value, so its value refers to the literature [32]. Wei's method [37] (pp.…”

Section: Experiments Design and Implementationmentioning

confidence: 99%

See 2 more Smart Citations

Zero Watermarking for the TIN DEM Data Based on the Edge Length

Zhou

Zhu

Ren

2021

IJGI

Self Cite

View full text Add to dashboard Cite

How to keep the fidelity of the digital elevation model (DEM) data is a crucial problem in the current watermarking research, as the watermarked DEM data need to preserve their accuracy. We proposed a zero watermarking method for the triangulated irregular network (TIN) DEM data. It takes full advantage of the characteristics of the edge length in the TIN DEM data. First, the radio of the edge lengths is quantified to the watermark index, and then the comparison of the edge lengths is quantified to the watermark bit. Finally, the watermark is constructed by combing the watermark bits according to the watermark indices with the help of the majority voting mechanism. In the method, the TIN DEM data are only used to construct the watermark, not to be embedded by the watermark. Therefore, the data quality is preserved to the greatest extent. Experiments verify the theoretical achievements of this method and demonstrate the method is lossless to the TIN DEM data. Simulation results also show that the method has good robustness on translation, rotation, scaling, and cropping attacks.

show abstract

Section: Watermark Generation and Extractionmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

See 1 more Smart Citation

Zero Watermarking for the TIN DEM Data Based on the Edge Length

Zhou

Zhu

Ren

2021

IJGI

Self Cite

View full text Add to dashboard Cite

show abstract

“…The zero-watermarks are invisible. In addition, it can address the noticeable conflict between imperceptibility and robustness in the traditional watermarking methods [ 13 , 14 , 15 ]. The zero-watermarking algorithms for digital images could be applied either in the spatial domain [ 16 ] or the frequency domain [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Khafaga

Karim

Darwish

et al. 2022

Sensors

View full text Add to dashboard Cite

Copyright protection of medical images is a vital goal in the era of smart healthcare systems. In recent telemedicine applications, medical images are sensed using medical imaging devices and transmitted to remote places for screening by physicians and specialists. During their transmission, the medical images could be tampered with by intruders. Traditional watermarking methods embed the information in the host images to protect the copyright of medical images. The embedding destroys the original image and cannot be applied efficiently to images used in medicine that require high integrity. Robust zero-watermarking methods are preferable over other watermarking algorithms in medical image security due to their outstanding performance. Most existing methods are presented based on moments and moment invariants, which have become a prominent method for zero-watermarking due to their favorable image description capabilities and geometric invariance. Although moment-based zero-watermarking can be an effective approach to image copyright protection, several present approaches cannot effectively resist geometric attacks, and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms rely on traditional moment computation, which suffers from numerical error accumulation, leading to numerical instabilities, and time consumption and affecting the performance of these moment-based zero-watermarking techniques. In this paper, we derived multi-channel Gaussian–Hermite moments of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve lossless copyright protection of the color medical images. We performed experiments where their outcomes ensure the robustness of the proposed zero-watermarking algorithms against various attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior robustness, security, and time computation.

show abstract

“…Compared with traditional watermarking technology, zero-watermarking technology is more suitable for high-precision vector geographic data. At the same time, the zero-watermarking technology also balances the contradiction between watermark invisibility and robustness [24,25].…”

Section: Introductionmentioning

confidence: 99%

A zero-watermarking algorithm for vector geographic data based on feature invariants

Wang

Zhang

et al. 2022

Earth Sci Inform

View full text Add to dashboard Cite

Most researches on zero-watermarking algorithms for vector geographic data focus on improving the robustness against geometrical attacks, compression attacks and object attacks. However, there are few zero-watermarking algorithms against projection transformation. We proposed a zerowatermarking algorithm for vector geographic data based on feature invariants. After any projection transformation of vector geographic data, the number of vertices and relative storage order of objects does not change. Therefore, the number of vertices and relative storage order of objects can be considered as the feature invariants. Firstly, according to relative storage order of objects, the watermark bit is determined by comparing the number of vertices between any two objects. Secondly, the watermark index is calculated by the number of vertices of two objects. Then, a feature matrix is constructed combining the watermark bit and the watermark index. Finally, the XOR operation is performed between the feature matrix and the scrambled watermark image to generate the zerowatermark image. The experiments show that the watermark can be detected from the vector geographic data after any projection transformation. And this algorithm can effectively against geometrical attacks, object attacks and precision reduction attacks, showing superior performance compared with previous algorithms.

show abstract

Zero Watermarking Algorithm for Vector Geographic Data Based on the Number of Neighboring Features

Cited by 14 publications

References 26 publications

Zero Watermarking for the TIN DEM Data Based on the Edge Length

Zero Watermarking for the TIN DEM Data Based on the Edge Length

Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

A zero-watermarking algorithm for vector geographic data based on feature invariants

Contact Info

Product

Resources

About