Tianyu Ye

Bio: Tianyu Ye is an academic researcher from Zhejiang Gongshang University. The author has contributed to research in topics: Singular value & Discrete cosine transform. The author has an hindex of 1, co-authored 1 publications receiving 5 citations.

A Robust Zero-Watermark Algorithm Based on Singular Value Decomposition and Discreet Cosine Transform

Abstract: A robust zero-watermark algorithm is proposed, which is based on singular value decomposition and discreet cosine transform. The image is firstly spilt into non-overlapping blocks. Afterwards, every block is conducted with singular value decomposition, and its singular value matrix is transformed with discreet cosine transform. The robust zero-watermark sequence is derived from comparing the numerical relationship between two direct coefficients from adjacent blocks. Experimental results of robustness tests show that it has good robustness against various attacks.

A Novel Zero-Watermarking Scheme Based on Variable Parameter Chaotic Mapping in NSPD-DCT Domain

Abstract: In this paper, a novel image zero-watermarking scheme against rotation attacks is proposed based on nonsubsampled pyramid decomposition (NSPD) and discrete cosine transform (DCT) It utilizes the intrinsic characteristics of NSPD and DCT to extract the robust feature of an image as the original zero-watermark To increase the security of the proposed scheme, a variable parameter chaotic mapping (VPCM) is designed for the processes of watermark encryption and robust feature extraction Firstly, the host gray-scale image is decomposed by NSPD, and the low-frequency sub-band image is divided into non-overlapping blocks After the blocks are transformed by DCT, the signs of the first AC coefficients from all the blocks are used to construct a binary feature image Then an exclusive-or operation is performed between the binary feature image and the encrypted watermark image to obtain the verification zero-watermark image Furthermore, a method against arbitrary rotation attacks is employed to improve the robustness of the scheme against geometric attacks The experimental results demonstrate that the proposed scheme is highly robust against various image processing attacks such as filtering, JPEG compression, scaling, translation, rotation and Checkmark attacks

Robust zero-watermark algorithms based on numerical relationship between adjacent blocks

Abstract: In this paper, three robust zero-watermark algorithms named Direct Current coefficient RElationship (DC-RE), CUmulant combined Singular Value Decomposition (CU-SVD), and CUmulant combined Singular Value Decomposition RElationship (CU-SVD-RE) are proposed. The algorithm DC-RE gets the feature vector from the relationship of DC coefficients between adjacent blocks, CU-SVD gets the feature vector from the singular value of third-order cumulants, while CU-SVD-RE combines the essence of the first two algorithms. Specially, CU-SVD-RE gets the feature vector from the relationship between singular values of third-order cumulants. Being a cross-over studying field of watermarking and cryptography, the zero-watermark algorithms are robust without modifying the carrier. Numerical simulation obviously shows that, under geometric attacks, the performance of CU-SVD-RE and DC-RE algorithm are better and all three proposed algorithms are robust to various attacks, such as median filter, salt and pepper noise, and Gaussian low-pass filter attacks.

An improved QR decomposition for color image watermarking

Abstract: Beside many other algorithms such as Singular Value Decomposition (SVD), Discreet Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), QR decomposition is known as an effective method for embedding and extracting watermarked image. In QR decomposition, Gram-Schmidt and Householder [10] are the most popular two algorithms. In this paper, QR factorization is executed by exploiting orthogonality of Q matrix and triangularity of R matrix to find out elements of these matrixes. The algorithm of Sun [12] is used for both embedding and extracting. For embedding watermark, Q and R will be calculated separately where computing R is implemented by solving a set of linear equations and calculating Q is base on Gram-Schmidt algorithm [10] via knowing R. In addition, diagonal elements of R matrix are inspected to ensure their validity except the first diagonal one. For extracting watermark, only the first element $R(1,1)$ of R matrix needs to be computed which is performed by an operation. Experimental results show that the proposed scheme not only has better quality of water- marked image, but also overcomes problems of robustness and computation complexity.

A robust public key watermarking scheme based on improved singular value decomposition

Abstract: Digital watermarking has been considered as an effective solution for multimedia rightful protection and authentication. Singular value decomposition (SVD) is used for many watermarking schemes. This method has encountered some challenges, such as computational complexity and robustness. In this paper, instead of using the conventional SVD, we employed a new algorithm to directly compute the largest eigenvalue and eigenvector of segmented image blocks. Theoretically, by using this approach, our proposed watermarking scheme has computational complexity lower than various SVD based schemes. This improvement is essential for watermarking systems in practice, where ones often have to work with large-scale image datasets. Experimental results also showed that our watermarking scheme outperforms several widely used schemes in terms of robustness. Moreover, using the proposed algorithm, we designed a new robust public key watermarking scheme, where watermarked images can be verified without using the pre-defined watermark and a secret key.