Color image zero-watermarking based on fast quaternion generic polar complex exponential transform

Yang, Heng; Qi, Shuren; Niu, Panpan; Wang, Xiangyang

doi:10.1016/j.image.2019.115747

Cited by 58 publications

(41 citation statements)

References 36 publications

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“…Furthermore, the QPHFM-(quaternion polar harmonic Fourier moments-) based scheme by Xia et al [23] utilizes wavelet numerical integration to improve the moment accuracy and robustness and uses QR codes to encode watermarks for large payload and security. Yang et al [24] used the DFT to calculate QGPCET (quaternion generic polar complex exponential transform) moment to lower the computational complexity. Additionally, to solve the low discrimination issue, the scheme utilizes the characteristic of the GPCET in which the image areas highly described by the GPCET are adjustable by a parameter to mix up the GPCET magnitudes with the different parameters.…”

Section: Related Workmentioning

confidence: 99%

“…In the selection of the reference value, most schemes select one value as the reference value of all the geometric invariants. For example, [19][20][21]23] select the mean of the geometric invariants, [14,18,24,25] select the median of the geometric invariants, and [22] selects the Otsu threshold as the reference value (Table 1). With the invariants and reference value, schemes construct image features.…”

Section: Related Workmentioning

confidence: 99%

“…To verify the performance of the proposed robust feature generation location selection method, this section designs three robust feature generation location selection methods based on the existing reference value selection methods (see Table 1) and compares them with the proposed method. e first method calculates the PCET magnitudes of images and uses the median of the magnitudes as the reference value to generate robust features; the second method also uses the PCET magnitudes as the geometric invariants, but the mean of the magnitude as the reference value; Inspired by Yang et al [24], the third method calculates GPCETmagnitudes with the parameter s of 0. 3 and 4, and the formulas of Hamming distance and BER are seen in the following equations:…”

Section: Performance Comparisonmentioning

confidence: 99%

“…Both the discrimination and robustness can be measured using BER defined in (4). e PCET-based zero-watermarking [19], the QPHFM-based zero-watermarking [20], and the QGPCET-based zero-watermarking [24] are selected for a performance comparison. Because the performance of moment-based zero-watermarking is related to the maximum order of the moment, this section sets the maximum order K to the minimum allowed.…”

Section: Experimental Settingsmentioning

confidence: 99%

See 3 more Smart Citations

A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination

Wang

Liu

2021

Security and Communication Networks

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Zero-watermarking is one of the solutions for image copyright protection without tampering with images, and thus it is suitable for medical images, which commonly do not allow any distortion. Moment-based zero-watermarking is robust against both image processing and geometric attacks, but the discrimination of watermarks is often ignored by researchers, resulting in the high possibility that host images and fake host images cannot be distinguished by verifier. To this end, this paper proposes a PCET- (polar complex exponential transform-) based zero-watermarking scheme based on the stability of the relationships between moment magnitudes of the same order and stability of the relationships between moment magnitudes of the same repetition, which can handle multiple medical images simultaneously. The scheme first calculates the PCET moment magnitudes for each image in an image group. Then, the magnitudes of the same order and the magnitudes of the same repetition are compared to obtain the content-related features. All the image features are added together to obtain the features for the image group. Finally, the scheme extracts a robust feature vector with the chaos system and takes the bitwise XOR of the robust feature and a scrambled watermark to generate a zero-watermark. The scheme produces robust features with both resistance to various attacks and low similarity among different images. In addition, the one-to-many mapping between magnitudes and robust feature bits reduces the number of moments involved, which not only reduces the computation time but also further improves the robustness. The experimental results show that the proposed scheme meets the performance requirements of zero-watermarking on the robustness, discrimination, and capacity, and it outperforms the state-of-the-art methods in terms of robustness, discrimination, and computational time under the same payloads.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Performance Comparisonmentioning

confidence: 99%

Section: Experimental Settingsmentioning

confidence: 99%

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A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination

Wang

Liu

2021

Security and Communication Networks

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“…However, EMs have various errors and numerical instabilities at high orders, which affects the accuracy of EMs [15]. e ubiquitous errors have a very negative impact on the image analysis and reconstruction [16] so that when the order of EMs reaches a critical value, the reconstruction errors are too large to be imaged [17]. e promotion of fractional-order exponential moments (FrEMs) afterward compensated for the numerical instability of EMs effectively [18] and improved the reconstruction and antinoise performance of EMs.…”

Section: Introductionmentioning

confidence: 99%

Accurate Computation of Fractional-Order Exponential Moments

Hao

et al. 2020

Security and Communication Networks

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Exponential moments (EMs) are important radial orthogonal moments, which have good image description ability and have less information redundancy compared with other orthogonal moments. Therefore, it has been used in various fields of image processing in recent years. However, EMs can only take integer order, which limits their reconstruction and antinoising attack performances. The promotion of fractional-order exponential moments (FrEMs) effectively alleviates the numerical instability problem of EMs; however, the numerical integration errors generated by the traditional calculation methods of FrEMs still affect the accuracy of FrEMs. Therefore, the Gaussian numerical integration (GNI) is used in this paper to propose an accurate calculation method of FrEMs, which effectively alleviates the numerical integration error. Extensive experiments are carried out in this paper to prove that the GNI method can significantly improve the performance of FrEMs in many aspects.

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Full Quaternion Matrix and Random Projection for Bimodal Face Template Protection

Shao

Shang

et al. 2021

Biometric Recognition

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Color image zero-watermarking based on fast quaternion generic polar complex exponential transform

Cited by 58 publications

References 36 publications

A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination

A Polar Complex Exponential Transform-Based Zero-Watermarking for Multiple Medical Images with High Discrimination

Accurate Computation of Fractional-Order Exponential Moments

Full Quaternion Matrix and Random Projection for Bimodal Face Template Protection

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