Comparative Analysis of Digital Watermarking Techniques

Bansal, Neelam; Deolia, Vinay Kumar; Bansal, Atul; Pathak, Pooja

doi:10.1007/978-981-10-0767-5_13

Cited by 8 publications

(7 citation statements)

References 7 publications

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“…Imperceptibility, robustness, and security are the key features of the watermarking method. Imperceptibility is computed by the visual affinity of the host image in comparison to the watermarked image [18]. The watermarking framework ought to be robust against any deliberate or accidental assault of the watermarked substance that can be a picture, sound, video, or text [13].…”

Section: Digital Watermarking Conceptmentioning

confidence: 99%

An Efficient Watermarking Approach Based on LL and HH Edges of DWT–SVD

Yasmeen

Uddin

2021

SN COMPUT. SCI.

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Digital watermarking is playing a vital role in the improvement of authentication, security, and copyright protection in today’s digital transformation. The performance of this technique is shown to be impressive around the globe. Text, audio, video, and image data are acted as watermarks in the digital platform. In this article, a hybrid watermarking scheme is proposed to furnish the robustness and protection of digital data. This hybrid scheme is a form of discrete wavelet transform (DWT) and singular value decomposition (SVD). The embedding and extracting features are carried out through multi-level operations of DWT and SVD. Various attacks are added to the proposed method to justify the robustness of the watermark. In the end, the suggested approach is contrasted with existing methods to confirm the supremacy.

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Section: Digital Watermarking Conceptmentioning

confidence: 99%

An Efficient Watermarking Approach Based on LL and HH Edges of DWT–SVD

Yasmeen

Uddin

2021

SN COMPUT. SCI.

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“…In spatial domain watermarking, directing changes in the pixel values of the original image. Which robustness is very low compared with a DFT watermarking algorithm [7] [8]. Frequency domain [9] watermarking is modifies the pixel values using, such as the Discrete Cosine Transform (DCT), the Discrete Fourier Transform (DFT), and Discrete Wavelet Transform (DWT).…”

Section: Fig 1 Image Watermarkingmentioning

confidence: 99%

“…The DCT coefficient are modified depends on the DC coefficient and median of the AC coefficients ordered by a zig-zag sequence [5] [6]. In [7], presented that digital image watermarking algorithm based on one level DWT and embeds an invisible watermark into the original image by using alpha blending process. In [8], proposed method an efficient watermarking with the use of rough entropy as embedding strength and rough set based classification for reduction of statistical redundancies between wavelet sub band such as LL, LH, HL and HH.…”

Section: Related Workmentioning

confidence: 99%

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An Efficient Discrete Wavelet Transform Based Hybrid Image Watermarking Algorithm Using Human Visual Model

Sundhararaj

Meenakshipriya

2021

Preprint

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More than ever with growing of multimedia technology, the digital data are exchanged in the internet, which can be duplicated by unauthorized users. To avoid this problem the watermarking technology has brought this paper. Performance improvement with compare to existing algorithm is obtained by proposing a new watermarking algorithm based on Human Visual Model (HVM) and Discrete Wavelet Transform (DWT) for securing the digital data and copyright protection. DWT is applied to the input image and at each level of DWT sub bands to embed a watermark image in selected coefficients of the sub band. HVM integrate the weight factor effect of human visualization by considering into the eye’s vision is less sensitivity area, depending on brightness, frequency band and texture areas of the image sub band. In the proposed approach robustly and imperceptibly, DWT and HVM are used for obtainable weight factor, according to human eye perceptual and to determine the optimal strength at which the threshold to embedding reaches the perceptual invisibility of watermarked image to various attacks. Performance is evaluated such as Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Quality Index (QI) is used to evaluate and achieve the imperceptible watermarked image. Results of PSNR values of hybrid image watermarking are between 49.73db to 36.24db. Experimental results show that our hybrid image watermarking process has enhanced robustness and displays the effectiveness of presenting images watermarking system.

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“…Data hiding (Bansal et al 2016) is referred as embedding additional data in a cover medium such as image, video, audio, and file. Data hiding technology is widely used in secret information transmission (Shi et al 2017), watermarking (Yu 2016), copyright certification (Mun et al 2017), forgery detection (Wolfgang and Delp 1996) applications.…”

Section: Related Work Data Hidingmentioning

confidence: 99%

Attention Based Data Hiding with Generative Adversarial Networks

2020

AAAI

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Recently, the generative adversarial network is the hotspot in research and industrial areas. Its application on data generation is the most common usage. In this paper, we propose the novel end-to-end framework to extend its application to data hiding area. The discriminative model simulates the detection process, which can help us understand the sensitivity of the cover image to semantic changes. The generative model is to generate the target image which is aligned with the original cover image. An attention model is introduced to generate the attention mask. This mask can help to generate a better target image without perturbation of the spotlight. The introduction of cycle discriminative model and inconsistent loss can help to enhance the quality of the generated target image in the iterative training process. The training dataset is mixed with intact images and attacked images. The mix training process can further improve robustness. Through the qualitative, quantitative experiments and analysis, this novel framework shows compelling performance and advantages over the current state-of-the-art methods in data hiding applications.

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Comparative Analysis of Digital Watermarking Techniques

Cited by 8 publications

References 7 publications

An Efficient Watermarking Approach Based on LL and HH Edges of DWT–SVD

An Efficient Watermarking Approach Based on LL and HH Edges of DWT–SVD

An Efficient Discrete Wavelet Transform Based Hybrid Image Watermarking Algorithm Using Human Visual Model

Attention Based Data Hiding with Generative Adversarial Networks

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