Local multi-watermarking method based on robust and adaptive feature extraction

Yuan, Xiaochen; Li, Mianjie

doi:10.1016/j.sigpro.2018.03.007

Cited by 36 publications

(15 citation statements)

References 32 publications

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“…(3) The final category is based on classical conventional digital watermarking. In the literature [18][19][20][21][22], an algorithm of multiple watermarks for medical images is implemented. As Aditi Zear et al [18] used SVD, DCT, DWT respectively for different type of watermarks, and back propagation neural networks (BPNN) to eliminate the impact of noise when extracting watermarks; they also enhanced its security by using Arnold transform; Rohit Thanki et al [19] used fast discrete curvelet transform (FDCuT) for medical images to obtain the different frequency coefficients of the curve decomposition, and adopted DCT for high frequency coefficients.…”

Section: Introductionmentioning

confidence: 99%

“…It follows multiple multiplication rules and is based on the minimum risk of bayes. Their DWT coefficients are modeled as generalized gaussian distributions; Xiaochen Yuan et al [21] proposed a local multi-watermark algorithm, which used the the robust and adaptive feature detector based on daisy descriptor (RAF3D) design adaptive detector to embed multiple watermarks into the orthogonal space of a feature extraction region at the same time. When extracting, the image can be extracted independently; Amit Kumar Singh et al [22] proposed a spread spectrum watermarking algorithm, which used a haar wavelet to perform the binary sub-band decomposition, and then embedded different forms of watermark information in the intermediate frequency and second order selected frequency bands of the first order DWT respectively, according to the different contexts in which it was used.…”

Section: Introductionmentioning

confidence: 99%

“…Although the watermark image can be restored to the original image without loss, the watermark is not robust. Therefore, finding a feature vector that can resist geometric attacks in medical images so that the algorithm can show good robustness without changing the original image is a problem that has long puzzled researchers [7,8,12,13,20,21]. For algorithms based on the first and third categories, the embedding of watermarks affects the images themselves, and these watermarking algorithms have poor anti-geometric attack capabilities; they cannot even resist the tiny geometric transformations [9,10,[22][23][24].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map

Liu

et al. 2019

Applied Sciences

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To resolve the contradiction between existing watermarking methods—which are not compatible with the watermark’s ability to resist geometric attacks—and robustness, a robust multi-watermarking algorithm suitable for medical images is proposed. First, the visual feature vector of the medical image was obtained by dual-tree complex wavelet transform and discrete cosine transform (DTCWT-DCT) to perform multi-watermark embedding and extraction. Then, the multi-watermark was preprocessed using the henon map chaotic encryption technology to strengthen the security of watermark information, and combined with the concept of zero watermark to make the watermark able to resist both conventional and geometric attacks. Experimental results show that the proposed algorithm can effectively extract watermark information; it implements zero watermarking and blind extraction. Compared with existing watermark technology, it has good performance in terms of its robustness and resistance to geometric attacks and conventional attacks, especially in geometric attacks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Robust Multi-Watermarking Algorithm for Medical Images Based on DTCWT-DCT and Henon Map

Liu

et al. 2019

Applied Sciences

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“…The multiple watermarking algorithm commonly focuses on images and videos. There are three main methods for addressing the impact of multiple watermarks, as described in References [17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The first is dividing the images into multiple blocks for multiple watermarks [17,18,27,29].…”

mentioning

confidence: 99%

“…The first is dividing the images into multiple blocks for multiple watermarks [17,18,27,29]. The second is embedding multiple watermarks into different frequency domains or channels [19][20][21][22][23]26,30]. The last is merging multiple watermarks into one [24,25,27].…”

mentioning

confidence: 99%

An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data

et al. 2018

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Vector geographic data play an important role in location information services. Digital watermarking has been widely used in protecting vector geographic data from being easily duplicated by digital forensics. Because the production and application of vector geographic data refer to many units and departments, the demand for multiple watermarking technology is increasing. However, multiple watermarking algorithm for vector geographic data draw less attention, and there are many urgent problems to be solved. Therefore, an efficient robust multiple watermark algorithm for vector geographic data is proposed in this paper. The coordinates in vector geographic data are first randomly divided into non-repetitive sets. The multiple watermarks are then embedded into the different sets. In watermark detection correlation, the Lindeberg theory is used to build a detection model and to confirm the detection threshold. Finally, experiments are made in order to demonstrate the detection algorithm, and to test its robustness against common attacks, especially against cropping attacks. The experimental results show that the proposed algorithm is robust against the deletion of vertices, addition of vertices, compression, and cropping attacks. Moreover, the proposed detection algorithm is compatible with single watermarking detection algorithms, and it has good performance in terms of detection efficiency. Therefore, multiple watermarking technology can effectively solve the above information security problems of vector geographic data.Currently, many scholars have been devoted to developing digital watermark technology, and they have proposed many algorithms [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. However, these algorithms focus on embedding only one watermark in the cover data, which then lacks multi-copyright protection. Multiple watermarking draws less attention than single watermarking, and it does not simply involve the embedding of different watermarks by using the single watermarking algorithm. The multiple watermarking algorithm commonly focuses on images and videos. There are three main methods for addressing the impact of multiple watermarks, as described in References [17][18][19][20][21][22][23][24][25][26][27][28][29][30]. The first is dividing the images into multiple blocks for multiple watermarks [17,18,27,29]. The second is embedding multiple watermarks into different frequency domains or channels [19][20][21][22][23]26,30]. The last is merging multiple watermarks into one [24,25,27]. Usually multiple methods are combined in one algorithm. In the above references, only a few previous works have been proposed for vector geographic data; for instance, References [24-29]. Sun et al. combined a child copyright watermark with another child watermark consisting of features selected by fuzzy clustering from the vector map. The new watermark was then embedded into the vector map [24]. Li et al. proposed a multiple watermark embedding solution through the generation of additional information with waterma...

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