Image watermarking using low wavelet subband based on 8×8 sub-block DCT

Winarno, Agus; Setiadi, De Rosal Ignatius Moses; Arrasyid, Adli Azhar; Sari, Christy Atika; Rachmawanto, Eko Hari

doi:10.1109/isemantic.2017.8251835

Cited by 25 publications

(14 citation statements)

References 13 publications

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“…Our experiments are carried out to test the imperceptibility of the hidden watermark as well as the robustness of the proposed scheme against attacks. Additionally, the performance of our method is compared with those of the ones in [18] and [23]. Both experiments are conducted on the host grey images with the size of 512 × 512 pixels, 8 bits/pixel and the binary watermark with the size of 32 × 32 pixels.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…To evaluate the performance of our proposed watermarking scheme, we compare the PSNR and NC performance of our method with the ones in [18] and [23]. It should be noted that the study in [18] is a typical method of watermarking in the transform domain of the DWT-DCT combination while the approach in [23] is to use DWT and BP NNs.…”

Section: B Experiment: Performance Comparison Between the Proposed Scheme With The Previous Methodsmentioning

confidence: 99%

“…To compare the performance of the proposed scheme with one in [18], we use 8 gray scale images shown in Fig. 4 as the inputs and the same watermark in [18] as shown Fig. 11(a).…”

Section: B Experiment: Performance Comparison Between the Proposed Scheme With The Previous Methodsmentioning

confidence: 99%

“…Therefore, spatial domain watermarking techniques are not commonly used in many practical applications. Alternatively, to improve the robustness and imperceptibility, watermarking can be carried out in transform domains such as the fast Fourier transform (FFT) [14], discrete cosine transform (DCT) [15], discrete wavelet transform (DWT) [13], [16], DWT-DCT [17], [18]. The transform domain watermarking can offer better robustness against common attacks since watermark coefficients are spread over the host image.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain

Sy¹,

Kha²,

Hoang³

2020

IJMLC

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Digital watermarking is one of the most widely used techniques for the protection of ownership rights of digital audio, images, and videos. One of the desirable properties of a digital watermarking scheme is its robustness against attacks aiming at removing or destroying the watermark from the host data. Different from the common watermarking techniques based on the spatial domain or transform domain, in this paper, a novel scheme of digital image blind watermarking based on the combination of the discrete wavelet transform (DWT) and the convolutional neural network (CNN) is proposed. Firstly, the host images are decomposed by the DWT with 4 levels and, then, the low frequency sub-bands of the first level and the high frequency sub-bands of the fourth level are used as the input data and the output target data to train the CNN model for embedding and extracting the watermark. Experimental results show that the proposed scheme has superior performance against common attacks of JPEG compression, mean and median filtering, salt and pepper noise, Gaussian noise, speckle noise, brightness modification, scaling, cropping, rotation, and shearing operations.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: B Experiment: Performance Comparison Between the Proposed Scheme With The Previous Methodsmentioning

confidence: 99%

“…To compare the performance of the proposed scheme with one in [18], we use 8 gray scale images shown in Fig. 4 as the inputs and the same watermark in [18] as shown Fig. 11(a).…”

Section: B Experiment: Performance Comparison Between the Proposed Scheme With The Previous Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain

Sy¹,

Kha²,

Hoang³

2020

IJMLC

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“…al.,Presents a review on latest trends in 3D computer vision. In image processing, the research has advanced significantly over the past decade [6]. The author reviews the basic and advanced approaches of 3D image acquisition and procedures for storing, processing and understanding this data.…”

Section: Raj Et Al Analysed the Discrete Fourier Transform Andmentioning

confidence: 99%

An Efficient Watermarking and Key Generation Technique using DWT Algorithm in Three-Dimensional Image

Kumar¹,

Napoleon²

2019

IJRTE

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Discrete Wavelet Transform is the algorithm which can be used to increase the contrast of an image for better visual quality of an image. The histogram value for original image with highest bins is taken for embedding the data into an image to perform the histogram equalization for repeating the process simultaneously. Information can be embedded into the source image with some bit value, for recovering the original image without any loss of the pixels. DWT is the first algorithm which has achieved the image contrast enhancement accurately. This approach maintained the original visual quality of an image even though themessage bits are embedded into the contrast-enhanced images. The proposed work with an original watermarking scheme based on the least significant bit technique. As a substitute of embedding the data into a simple image as watermarking, least significant bitmethod by utilizing the three wavelets transform is applied in the proposed system in order to enhance the embedding technique using spatial domain. For security, the Huffman coding has used to secure the data embedded into a host image, which can convert the secret message sequence into bit sequence for least significant bit operation. DWT can analyze the signal at multiple resolutions and it can divide the image into two types of quadrants as high and low-frequency quadrants. Here dividing an image into low and high it makes the information to hide.

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Blind watermarking algorithm combiningNSCT,DWT,SVD, andHVS

Fanfeng

Bai

Xiao

2022

Security and Privacy

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In order to solve the contradiction between watermark transparency and robustness, this paper combines Non-subsampled Contourlet Transformation (NSCT), Discrete Wavelet Transformation (DWT), Singular Value Decomposition (SVD), and Human Visual System (HVS) to improve a watermarking algorithm. Firstly, this algorithm performs a first-level NSCT decomposition on the carrier image to obtain its low-frequency part, and then performs a second-level DWT on the low-frequency part, extracts its second-level low-frequency sub bands and block these sub bands to sub-blocks according to their size. Secondly, SVD is performed for each sub-block and the best embedding position of the watermark according to the sensitivity of HVS for the average brightness and the modified cosine similarity measurement method is calculated. Finally, the watermark scrambled and encrypted by Arnold is embedded into the selected singular value. This algorithm can realize the blind extraction of watermark. Experimental results show that the algorithm can not only satisfy the transparency but the robustness of the watermark at the same time as well. K E Y W O R D Sblind watermarking, digital watermarking, discrete wavelet transform, NSCT, singular value decomposition INTRODUCTIONNowadays, multimedia documents like images, audio, and video are easy to store and disseminate with the help of digital communication through internet. The document, however, can be modified by intruders during transmission or storage for illegal use. This illegal alteration is becoming a problem in many human-centric applications. Therefore, the effective protection and content certification of digital products have become a real problem and one can incorporate digital watermarking scheme to deal with such issues. Digital watermarking technology uses the principle of information hiding to embed the logo information into the carrier data in a perceptible or imperceptible way, without affecting the use value of the original carrier, protecting copyright, private communication, content authentication, and tracking infringements. 1 Digital watermarking can be applied to images, videos, audio, or texts. 2 The embedded information should be protected and extracted successfully to verify the image. Digital image watermarking can be of two types: visible and invisible. For invisible watermarking, there should not be any perceptual change in the host image. In visible watermarking, the watermark should can be seen so that the watermark can be easily detected. 3 Watermarking can be classified into blind and non-blind watermarking depending

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Image watermarking using low wavelet subband based on 8×8 sub-block DCT

Cited by 25 publications

References 13 publications

An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain

An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain

An Efficient Watermarking and Key Generation Technique using DWT Algorithm in Three-Dimensional Image

Blind watermarking algorithm combiningNSCT,DWT,SVD, andHVS

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