A progressive QIM to cope with SVD-based blind image watermarking in DWT domain

Hu, Hwai-Tsu; Chang, Yu-Jung; Chen, Szu-Hong

doi:10.1109/chinasip.2014.6889277

Cited by 8 publications

(16 citation statements)

References 22 publications

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“…This scheme considered only some pixel-value change attacks, by which we can regard that it is specialized to this kind of attacks. In spite of it, ours was better in 3 out of 7 attacks Table 8 shows the performances to compare with [14]. Its robustness assessment was BER as [14].…”

Section: Experimental Results and Comparison With Existingmentioning

confidence: 95%

“…In spite of it, ours was better in 3 out of 7 attacks Table 8 shows the performances to compare with [14]. Its robustness assessment was BER as [14]. This scheme also considered some pixel-value change attacks only.…”

Section: Experimental Results and Comparison With Existingmentioning

confidence: 99%

“…Meanwhile, the values of our scheme were measured by applying our scheme to the same image set and the same set of attacks as the corresponding existing scheme. For the watermark data, we used the same size of data (marked as "-" in Table 4) as the one used in the scheme to be compared: 32×16 for [17], 32×32 for [9,16], and 64×64 for [14,15]. First we compare with [17], which is in Table 5.…”

Section: Experimental Results and Comparison With Existingmentioning

confidence: 99%

“…A quantization index modulation (QIM) was used in [9,[13][14][15][16][17]. It was shown that QIM had high strength to some peculiar attacks but was weak to the geometric attacks.…”

Section: Related Previous Workmentioning

confidence: 99%

“…It was shown that QIM had high strength to some peculiar attacks but was weak to the geometric attacks. A method using SVD (singular value decomposition) was proposed [14]. It divided the LL subband resulting from 1level DWT into 4×4 subblocks and each subblock was decomposed by SVD.…”

Section: Related Previous Workmentioning

confidence: 99%

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Blind Image Watermarking Based on Adaptive Data Spreading in n-Level DWT Subbands

Lee

Seo

Kim

2019

Security and Communication Networks

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This paper proposes a new adaptive watermarking scheme for digital images, which has the properties of blind extraction, invisibility, and robustness against attacks. The typical scheme for invisibility and robustness consisted of two main techniques: finding local positions to be watermarked and mixing or embedding the watermark into the pixels of the locations. In finding the location, however, our scheme uses a global space such that the multiple watermarking data is spread out over all four lowest-frequency subbands, resulting from n-level Mallat-tree 2D (dimensional) DWT, where n depends on the amount of watermarking data and the resolution of the host image, without any further process to find the watermarking locations. To embed the watermark data into the subband coefficients, weighting factors are used according to the type and energy of each subband to adjust the strength of the watermark, so we call this an adaptive scheme. To examine the ability of the proposed scheme, images with various resolutions are tested for various attacks, both pixel-value changing attacks and geometric attacks. With experimental results and comparison to the existing works we show that the proposed scheme has better performance than the previous works, except those which specialize in certain types of attacks.

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Section: Experimental Results and Comparison With Existingmentioning

confidence: 95%

Section: Experimental Results and Comparison With Existingmentioning

confidence: 99%

Section: Experimental Results and Comparison With Existingmentioning

confidence: 99%

“…A quantization index modulation (QIM) was used in [9,[13][14][15][16][17]. It was shown that QIM had high strength to some peculiar attacks but was weak to the geometric attacks.…”

Section: Related Previous Workmentioning

confidence: 99%

Section: Related Previous Workmentioning

confidence: 99%

See 3 more Smart Citations

Blind Image Watermarking Based on Adaptive Data Spreading in n-Level DWT Subbands

Lee

Seo

Kim

2019

Security and Communication Networks

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Performance comparison of progressive QIM and QIM using different wavelet families

Kricha

Sakly

et al. 2015

2015 16th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)

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Detecting Digital Watermarking Image Attacks Using a Convolution Neural Network Approach

Alzahrani

2022

Security and Communication Networks

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In scientific research, one of the most significant problems of recent years has been and continues to be the protection of digital material. The advancement of Internet technology has allowed for the illicit duplication, authentication, and distribution of digital material by unauthorized individuals. For this reason, a variety of watermarking systems have been investigated for a variety of purposes, including broadcast monitoring, intellectual property protection, content authentication, and copy control. There are various types of the watermarking image attacks that impact the quality of the images; therefore, it is critical to ensure that watermarked digital images can withstand these kinds of attacks. Hence, novelty of the proposed research is to develop approaches to detect these attacks which becomes very important to guarantee a sufficient quality of watermarking images. In this paper, a deep learning method based on a convolution neural network (CNN) algorithm was proposed to detect various types of watermarking attacks, namely, median filter, Gaussian filter, salt-and-pepper, average filter, motion blur, and no attack, to improve the watermarking quality. Evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the normalization correlation (NC) were employed to examine the invisibility and robustness of the watermarking images. The empirical results of the CNN model show good performance for detecting watermarking attacks with different sizes (256, 128, and 64). The accuracy percentage of the testing process was 98%. A highly efficient CNN approach was developed. Very high performance of NC was found in the detection of the salt-and-pepper attack (99.02%, 99.97%, and 99.49% with respect to watermarking image sizes of 256 × 256, 128 × 128, and 64 × 64, respectively). The study concludes that the CNN model is able to detect watermarking attacks successfully.

show abstract

A progressive QIM to cope with SVD-based blind image watermarking in DWT domain

Cited by 8 publications

References 22 publications

Blind Image Watermarking Based on Adaptive Data Spreading in n-Level DWT Subbands

Blind Image Watermarking Based on Adaptive Data Spreading in n-Level DWT Subbands

Performance comparison of progressive QIM and QIM using different wavelet families

Detecting Digital Watermarking Image Attacks Using a Convolution Neural Network Approach

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