Artificial bee colony based perceptually tuned blind color image watermarking in hybrid LWT-DCT domain

Sharma, Harish; Sharma, Janki Ballabh

doi:10.1007/s11042-021-10610-y

Cited by 31 publications

(5 citation statements)

References 31 publications

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“…8. PSNR comparison with [15,28,29] in terms of dB Table 3 shows the NC comparison with [14,18,[25][26][27][28] for common image attacks for Lena standard image. The proposed watermarking method performs good against SLP(0.01), SPLN(0.01) and SCL(2.0) attacks, but for GN(0.01) attack it is not performing well, it could be because the GN(0.01) attacks affects the pixels relationship badly.…”

Section: Resultsmentioning

confidence: 99%

“…One of the important steps is to select the region for watermarking it may be done using segmentation [4] or other method. Discrete wavelet transforms (DWT) [5][6], discrete cosine transforms (DCT) [7][8], discrete fourier transform (DFT) [9][10], redundant discrete wavelet transforms (RDWT) [11][12], and integer/Lifting wavelet transform (IWT/LWT) [13][14][15][16][17][18] have previously been used for image watermarking. LWT is second generation wavelet and it has various advantages over the conventional methods and it is most significantly known for absorbing more image distortion due to having more energy compaction properties.…”

Section: Introductionmentioning

confidence: 99%

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Blind Color Image Watermarking using Deep Artificial Neural Network using statistical features

Pandey,

Jaiswal

2023

Preprint

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With increasing digital content over the internet it is very important to secure the digital contents in such a way that the identity and integrity of data is preserved in some way. The healthcare data is also a digital content which is important and this paper presents a methodology for copyright protection and authorship. In order to achieve a balance between imperceptibility and robustness a robust watermarking scheme is proposed using deep artificial neural network (DNN) and lifting wavelet transform. YIQ color model is utilized for image watermarking and statistical features have been obtained for creating training and testing set. Here watermark extraction is done as a binary classification technique and PCA is utilized for reducing the feature set. Ten standard images have been used for image watermarking and for threshold value 0.3 it shows the average imperceptibility of 51.08 dB and shows good robustness under various image attacks.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Blind Color Image Watermarking using Deep Artificial Neural Network using statistical features

Pandey,

Jaiswal

2023

Preprint

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“…Step 2: Perform SVD on the block embedded with the digital signature, and extract six digital signatures ESN i according to Formula (18).…”

Section: Extraction Of the Digital Signaturementioning

confidence: 99%

“…There are many methods to solve the falsepositive problem. The first method is to encrypt the watermark image [18][19][20][21][22][23]. If the attacker does not understand the encryption process, the false-positive problem will not appear.…”

Section: Introductionmentioning

confidence: 99%

Hybrid domain digital watermarking scheme based on improved differential evolution algorithm and singular value block embedding

Liu

Wang

et al. 2023

IET Image Processing

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The internet and related technologies have promoted the development of medical and health fields, especially remote diagnosis. As medical images may be stolen in the transmission process, the patient's personal information could be leaked. Aiming at the problem of privacy disclosure of colour medical images, a hybrid domain watermarking scheme based on improved differential evolution (DE) and the singular value block embedding (SVBE) is proposed in this study. Through this scheme, the image containing the patient's information is hidden in the patient's medical carrier image as a watermark. First, redistributed invariant lifting wavelet transform (RILWT), discrete wavelet transform (DWT), and singular value decomposition (SVD) are used to process the colour medical carrier image, and the high-frequency information in the integer wavelet domain of the watermark is embedded into the processed carrier image using the proposed singular value block embedding method based on the improved differential evolution algorithm. At the same time, to increase the security of the watermark scheme, a digital signature based on the SHA-384 hash function is designed for verification before watermark extraction. Compared with recent related research, the scheme has strong invisibility and robustness.

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“…This scheme has the advantages of spatial as well as transforms domain techniques, resulting in fewer computations and improved robustness; however, it has a lower embedding capacity. Sharma et al [20] demonstrated a watermarking approach incorporating the fusion of lifting wavelet transform (LWT) and discrete cosine transform (DCT) techniques and further applied the arti cial bee colony (ABC) algorithm to improve visual quality and robustness. This scheme is robust against various attacks, but a detailed security analysis needs to be conducted.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Optimization-Based Robust Watermarking Using Denoising Convolutional Neural Network

Mahto

Singh

2021

Preprint

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Colour images have been widely used in many aspects of life; however, copyright violation issues related to these images motivate research efforts. This paper aims to develop an enhanced watermarking algorithm for producing a watermarked image using hybrid optimisation with high imperceptibility and robustness. The algorithm is based on spatial and transform domains and begins by embedding multiple secret marks into cover media using an optimal scaling factor. The multi-type mark contributes an additional level of authenticity to the proposed algorithm. Furthermore, the marked image is encrypted using an improved encryption scheme, and the denoising convolutional neural network (DnCNN) is employed to enhance the robustness of the proposed algorithm. The results reveal that the proposed watermarking algorithm yields low computational overhead, excellent watermark capacity, imperceptibility, and robustness to common filtering attacks. Moreover, the comparison shows that the proposed algorithm outperforms other competing methods.

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Artificial bee colony based perceptually tuned blind color image watermarking in hybrid LWT-DCT domain

Cited by 31 publications

References 31 publications

Blind Color Image Watermarking using Deep Artificial Neural Network using statistical features

Blind Color Image Watermarking using Deep Artificial Neural Network using statistical features

Hybrid domain digital watermarking scheme based on improved differential evolution algorithm and singular value block embedding

Hybrid Optimization-Based Robust Watermarking Using Denoising Convolutional Neural Network

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