• Title/Summary/Keyword: embedding threshold matrix

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Reversible Watermarking with Adaptive Embedding Threshold Matrix

  • Gao, Guangyong;Shi, Yun-Qing;Sun, Xingming;Zhou, Caixue;Cui, Zongmin;Xu, Liya
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.10 no.9
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    • pp.4603-4624
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    • 2016
  • In this paper, a new reversible watermarking algorithm with adaptive embedding threshold matrix is proposed. Firstly, to avoid the overflow and underflow, two flexible thresholds, TL and TR, are applied to preprocess the image histogram with least histogram shift cost. Secondly, for achieving an optimal or near optimal tradeoff between the embedding capacity and imperceptibility, the embedding threshold matrix, composed of the embedding thresholds of all blocks, is determined adaptively by the combination between the composite chaos and the average energy of Integer Wavelet Transform (IWT) block. As a non-liner system with good randomness, the composite chaos is suitable to search the optimal embedding thresholds. Meanwhile, the average energy of IWT block is calculated to adjust the block embedding capacity, and more data are embedded into those IWT blocks with larger average energy. The experimental results demonstrate that compared with the state-of-the-art reversible watermarking schemes, the proposed scheme has better performance for the tradeoff between the embedding capacity and imperceptibility.

A New Function Embedding Method for the Multiple-Controlled Unitary Gate based on Literal Switch (리터럴 스위치에 의한 다중제어 유니터리 게이트의 새로운 함수 임베딩 방법)

  • Park, Dong-Young
    • The Journal of the Korea institute of electronic communication sciences
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    • v.12 no.1
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    • pp.101-108
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    • 2017
  • As the quantum gate matrix is a $r^{n+1}{\times}r^{n+1}$ dimension when the radix is r, the number of control state vectors is n, and the number of target state vectors is one, the matrix dimension with increasing n is exponentially increasing. If the number of control state vectors is $2^n$, then the number of $2^n-1$ unit matrix operations preserves the output from the input, and only one can be performed the unitary operation to the target state vector. Therefore, this paper proposes a new method of function embedding that can replace $2^n-1$ times of unit matrix operations with deterministic contribution to matrix dimension by arithmetic power switch of the unitary gate. The proposed function embedding method uses a binary literal switch with a multivalued threshold, so that a general purpose hybrid MCU gate can be realized in a $r{\times}r$ unitary matrix.

Secret Image Sharing Scheme using Matrix Decomposition and Adversary Structure (행렬 분해와 공격자 구조를 이용한 비밀이미지 공유 기법)

  • Hyun, Suhng-Ill;Shin, Sang-Ho;Yoo, Kee-Young
    • Journal of Korea Multimedia Society
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    • v.17 no.8
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    • pp.953-960
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    • 2014
  • In Shamir's (t,n)-threshold based secret image sharing schemes, there exists a problem that the secret image can be reconstructed when an arbitrary attacker becomes aware of t secret image pieces, or t participants are malicious collusion. It is because that utilizes linear combination polynomial arithmetic operation. In order to overcome the problem, we propose a secret image sharing scheme using matrix decomposition and adversary structure. In the proposed scheme, there is no reconstruction of the secret image even when an arbitrary attacker become aware of t secret image pieces. Also, we utilize a simple matrix decomposition operation in order to improve the security of the secret image. In experiments, we show that performances of embedding capacity and image distortion ratio of the proposed scheme are superior to previous schemes.

Robust Image Hashing for Tamper Detection Using Non-Negative Matrix Factorization

  • Tang, Zhenjun;Wang, Shuozhong;Zhang, Xinpeng;Wei, Weimin;Su, Shengjun
    • Journal of Ubiquitous Convergence Technology
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    • v.2 no.1
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    • pp.18-26
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    • 2008
  • The invariance relation existing in the non-negative matrix factorization (NMF) is used for constructing robust image hashes in this work. The image is first re-scaled to a fixed size. Low-pass filtering is performed on the luminance component of the re-sized image to produce a normalized matrix. Entries in the normalized matrix are pseudo-randomly re-arranged under the control of a secret key to generate a secondary image. Non-negative matrix factorization is then performed on the secondary image. As the relation between most pairs of adjacent entries in the NMF's coefficient matrix is basically invariant to ordinary image processing, a coarse quantization scheme is devised to compress the extracted features contained in the coefficient matrix. The obtained binary elements are used to form the image hash after being scrambled based on another key. Similarity between hashes is measured by the Hamming distance. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as Gaussian filtering, moderate noise contamination, JPEG compression, re-scaling, and watermark embedding. Hashes of different images have very low collision probability. Tampering to local image areas can be detected by comparing the Hamming distance with a predetermined threshold, indicating the usefulness of the technique in digital forensics.

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Hybrid Watermarking Technique using DWT Subband Structure and Spatial Edge Information (DWT 부대역구조와 공간 윤곽선정보를 이용한 하이브리드 워터마킹 기술)

  • 서영호;김동욱
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.29 no.5C
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    • pp.706-715
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    • 2004
  • In this paper, to decide the watermark embedding positions and embed the watermark we use the subband tee structure which is presented in the wavelet domain and the edge information in the spatial domain. The significant frequency region is estimated by the subband searching from the higher frequency subband to the lower frequency subband. LH1 subband which has the higher frequency in tree structure of the wavelet domain is divided into 4${\times}$4 submatrices, and the threshold which is used in the watermark embedding is obtained by the blockmatrix which is consists by the average of 4${\times}$4 submatrices. Also the watermark embedding position, Keymap is generated by the blockmatrix for the energy distribution in the frequency domain and the edge information in the spatial domain. The watermark is embedded into the wavelet coefficients using the Keymap and the random sequence generated by LFSR(Linear feedback shift register). Finally after the inverse wavelet transform the watermark embedded image is obtained. the proposed watermarking algorithm showed PSNR over 2㏈ and had the higher results from 2% to 8% in the comparison with the previous research for the attack such as the JPEG compression and the general image processing just like blurring, sharpening and gaussian noise.