Features Classification Forest: A Novel Development that is Adaptable to Robust Blind Watermarking Techniques

Chang, C. C.; Shen, Jau-Ji

doi:10.1109/tip.2017.2706502

Cited by 45 publications

(7 citation statements)

References 35 publications

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“…To unclear the image outside recognition, it is possible to perform mapping a number of times. The mapping can be done successively many times to completely unclear the image beyond recognition (13) . Alice has the information about the number of times the transform is applied and can successfully recover the original image.…”

Section: Arnold Transformmentioning

confidence: 99%

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Hybrid Watermarking Scheme with Dual Encryption and Channel Coding in YCbCr Color Space

Kaur¹,

Kumar²

2021

IJST

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Objectives: This study aims to Improve and Secure the watermarking scheme with dual encryption (chaotic maps and Arnold transform) and channel coding in YCbCr color space with embedding and extraction procedure. Methods: In this scheme, the cover watermarked image is encoded and the singular value is decomposed by singular value decomposition (SVD). The four levels of Discrete Wavelet Transform (DWT) are applied after that, the singular value matrixes are embedded into the Y, Cb, Cr components of the host image. The embedding factor for each component is calculated with singular vectors of the hl sub-band of DWT with bit selection automatically by Gray level co-occurrence matrix (GLCM). In this paper, the GLCM technique is used to enhance the performance of a watermarked image affected by degradation with the DWT method. An inefficient approach is chosen randomly for image embedding which is bit selection. There is degradation in the quality of the watermark image when randomly selecting the bits. To dynamically choose the embedding bit, this research applies the Grey Level Co-occurrence Matrix method. Findings: Different performance parameters like Mean Squared Error (MSE), Peak Signal to Noise ratio (PSNR), Bit Error Rate (BER), Normalized correlation coefficient (NCC), and Mean Structural Similarity Index Measure (MSSIM) has been used to compare the effectiveness of the proposed scheme. The achieved outcomes show that when applying dual encryption and FFT (Fast Fourier Transform) with the GLCM, around 10 to 15 percent improvement in the results can be obtained. Novelty: We have proposed a hybrid watermarking scheme with Chaotic maps, Arnold transform and Fast Fourier transform in YCbCr color space.

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Section: Arnold Transformmentioning

confidence: 99%

“…The authors observed that the quality of watermarking has been high with performance results. The concept of logo watermarking has been suggested (12,13) . Single level decomposition is to embed a trivial symbol with level decomposition for the host image and the Arnold transform is introduced.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Watermarking Scheme with Dual Encryption and Channel Coding in YCbCr Color Space

Kaur¹,

Kumar²

2021

IJST

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“…Due to the stability of SVD, that is, small numerical changes of singular values or the correlation of feature invariant elements in singular vectors after different attacks, these methods were reported to be robust to most attacks. Chang et al [19] proposed a model that exploits the features of watermarks to establish association rules and embeds the rules into a host image. Their model combined with SVD technology can resist different image processing attacks.…”

Section: Introductionmentioning

confidence: 99%

Robust Dual-Color Watermarking Based on Quaternion Singular Value Decomposition

et al. 2020

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Quaternion singular value decomposition (QSVD) is a robust technique of digital watermarking which can extract high quality watermarks from watermarked images with low distortion. In this paper, QSVD technique is further investigated and an efficient robust watermarking scheme is proposed. The improved algebraic structure-preserving method is proposed to handle the problem of "explosion of complexity" occurred in the conventional QSVD design. Secret information is transmitted blindly by incorporating in QSVD two new strategies, namely, coefficient pair selection and adaptive embedding. Unlike conventional QSVD which embeds watermarks in a single imaginary unit, we propose to adaptively embed the watermark into the optimal hiding position using the Normalized Cross-Correlation (NC) method. This avoids the selection of coefficient pair with less correlation, and thus, it reduces embedding impact by decreasing the maximum modification of coefficient values. In this way, compared with conventional QSVD, the proposed watermarking strategy avoids more modifications to a single color image layer and a better visual quality of the watermarked image is observed. Meanwhile, adaptive QSVD resists some common geometric attacks, and it improves the robustness of conventional QSVD. With these improvements, our method outperforms conventional QSVD. Its superiority over other state-of-the-art methods is also demonstrated experimentally.

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“…For the better selection of features used features optimization process. The process of features optimization used swarm-based optimization algorithms [9,10,21]. The swarm-based algorithms are multi-objective and multi constraint-based fitness function and generate a better optimal solution instead of unguided algorithms for the optimization of features used particle swarm optimization and ant colony optimization [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Optimized Features of SIFT Transform Function for Digital Image Watermarking using Hybrid Swarm Intelligence and Neural Network

Kumar¹,

Sharma²

2019

IJRTE

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Authentication of digital multi-media is a challenging task in the current scenario of internet technology for the authentication of digital multi-media data used digital watermarking techniques. The feature-based watermarking techniques provide the robustness of digital watermarking methods. In this paper proposed optimized features based digital watermarking techniques using a neural network. For the process of features, optimization used hybrid swarm intelligence algorithms. The hybrid algorithms are a combination of PSO and ACO. The PSO used as feature optimizer and ACO used for the selection of features point for the processing of neural network models. Two neural networks models used BP and RBF. The property of both models is different for the processing of data and desired output for the enhancement of the proposed model used cascaded neural network models using BP and RBF. The cascaded models generate dynamic patterns for the embedding of a digital watermark. The dynamic patterns provide the randomness of the pixel value and decrease the value of attack predication. The proposed algorithms have been tested on an extensive database of 300 images. The analysis of the proposed algorithm is satisfactory against different types of attacks and enhance the strength of robustness.

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Features Classification Forest: A Novel Development that is Adaptable to Robust Blind Watermarking Techniques

Cited by 45 publications

References 35 publications

Hybrid Watermarking Scheme with Dual Encryption and Channel Coding in YCbCr Color Space

Hybrid Watermarking Scheme with Dual Encryption and Channel Coding in YCbCr Color Space

Robust Dual-Color Watermarking Based on Quaternion Singular Value Decomposition

Optimized Features of SIFT Transform Function for Digital Image Watermarking using Hybrid Swarm Intelligence and Neural Network

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