Frequency domain digital watermark recognition using image code sequences with a back-propagation neural network

Yen, Chih-Ta; Huang, Yalin

doi:10.1007/s11042-015-2718-y

Cited by 27 publications

(9 citation statements)

References 12 publications

(9 reference statements)

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“…Experimental results demonstrate that the proposed method offer good visual quality of the watermarked image and robust against different kinds of signal processing attacks. Yen and Huang (2015) proposed a digital watermarking scheme based on DCT and BPNN. In the embedding process, DCT has been applied on the cover image of size 256 × 256 and the watermark of size 32 × 32 is embedded into the mid frequency region.…”

Section: Related Literature On Proposed Work and Research Gapsmentioning

confidence: 99%

“…A typical neural network consists of an input layer, hidden layers and output layer. Different algorithms for training BPNN are steepest descent method, adaptive learning rate, conjugate gradient, quasi-Newton and Levenburg-Marquardt (LM) algorithm (Ali et al, 2014;Yen and Huang, 2015;Hagan and Menhaj, 1994). The iteration of back propagation learning algorithm can be written as (Hagan and Menhaj, 1994): Here X k is current vector of weights and biases, a k is learning rate and g k is current gradient.…”

Section: Back Propagation Neural Networkmentioning

confidence: 99%

“…Recently, BPNN techniques have been used to balance robustness and imperceptibility. Artificial intelligence (AI) techniques such as genetic algorithm (GA), differential evolution (DE), neural networks (NN), clonal selection algorithm (CSA) and particle swarm optimiser (PSO) (Zhicheng et al, 2006;Aslantas, 2009;Aslantas et al, 2008Aslantas et al, , 2009Ali et al, 2014;Yen and Huang, 2015) are used as an optimisation technique to search optimal sub-bands and coefficients in transform domain to embed watermark with different scaling factors. In addition, these techniques can be used as optimisation techniques to remove some round off errors when coefficients in transform domain are transformed to spatial domain.…”

Section: Introductionmentioning

confidence: 99%

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Robust watermarking technique using back propagation neural network: a security protection mechanism for social applications

Zear

Singh

Kumar

2017

IJICS

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Abstract:In this paper, an algorithm for digital watermarking based on discrete wavelet transforms (DWTs) and singular value decomposition (SVD) has been proposed. In the embedding process, the host colour image is decomposed into third-level DWT. Low frequency band (LL3) is transformed by SVD. The watermark image is also transformed by SVD. The S vector of watermark information is embedded in the S component of the host image. Watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DWT. Watermark is extracted using an extraction algorithm. In order to enhance the robustness performance of the image watermark, back propagation neural network (BPNN) is applied to the extracted watermark to reduce the effects of different noise applied on the watermarked image. Results are obtained by varying the gain factor and size of the cover and watermark image, experimental results are provided to illustrate that the proposed method is able to withstand a variety of signal processing attacks and has been found to be giving superior performance for robustness and imperceptibility compared to existing methods suggested by other authors.Keywords: image watermarking; discrete wavelet transform; DWT; singular value decomposition; SVD; back propagation neural network; BPNN; normalised correlation; NC; social applications.Reference to this paper should be made as follows: Zear, A., Singh, A.K. and Kumar, P. (2017) 'Robust watermarking technique using back propagation neural network: a security protection mechanism for social applications', Int.

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Section: Related Literature On Proposed Work and Research Gapsmentioning

confidence: 99%

Section: Back Propagation Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust watermarking technique using back propagation neural network: a security protection mechanism for social applications

Zear

Singh

Kumar

2017

IJICS

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“…The first seven papers belong to the technique category of smart pattern recognition [4,7,10,13,14,18,22]. The first paper entitled BFeature design scheme for Kinect-based DTW human gesture recognition^by Ding and Chang [7] develops a feature design scheme involving hybridizations of joint positions and joint angles for dynamic time warping-based human gesture recognition with the Kinect camera.…”

Section: Summary Of 2accepted Papersmentioning

confidence: 99%

Guest Editorial: Smart Multimedia Communication Technologies for New-Generation Artificial Intelligence Applications

Hsieh

2016

Multimed Tools Appl

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With the development of the Internet of thing (IoT) mechanism nowadays, the required techniques for artificial intelligence (AI)-related applications will be equipped with a crucial property, which is Bsmart.^Smart multimedia communication technologies are composed of two main technique categories: the smart multimedia processing technique class and the smart communication transmission technique class. These will be the necessity for constructing a new-generation AI application. The technical category of multimedia processing involves lots of types of techniques where pattern recognition, video (or image) processing, and human machine interaction (HCI) are three kernel technique types to establish an AI application under the regulation of the IoT mechanism. Video coding and decoding (codecs) techniques, media streaming techniques, and hardware-based techniques for enhancing media communication play important roles for establishing an efficient communication transmission channel between the user end device and the remote media handling server.BSmart multimedia communication technologies^associated with the new-generation AI application will absolutely be an evolution tendency for the development of multimedia and communication technologies. Following this thought line, we deeply believe that the appropriate term Bsmart^will be given to pattern recognition, video processing, human machine interaction, media streaming, and hardware-based media communication enhancement techniques for building up a smart multimedia communication system.The special issue, BSmart Multimedia Communication Technologies for New-Generation Artificial Intelligence Applications,^acts as a path breaker for exploring new-generation AI applications using multimedia communication technologies with intelligence. We totally received around 30 submissions, and the vast majority of all submissions are significant in Multimed Tools Appl (2016) 75:9665-9668

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“…Researchers additionally consider the design properties of the digital watermarking system, as shown in Eq. (1), which is used to embed the watermark in frequency domain images [2].…”

Section: Introductionmentioning

confidence: 99%

Medical image watermarking using bit threshold map based on just noticeable distortion in discrete cosine transform

Seo

Wei

Kwon

et al. 2017

THC

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Abstract. BACKGROUND: Medical images stored in a hospital system are generally confidential and integrated and require strict security. However, medical information stored on digital medical imaging systems, as well as picture archiving and communication systems (PACS), are vulnerable to attack when the data are transferred over wireless or wired communication networks. OBJECTIVE: To solve this problem, a watermarking algorithm for medical images is proposed using a bit threshold map based on just noticeable distortion (JND) in the discrete cosine transform (DCT) method. METHODS: The low-frequency component comprises a considerable amount of the signal energy for most images. As a result, it has a crucial effect on the image quality. Therefore, in this paper, the proposed algorithm embeds watermarks based on the low-frequency components of the image, such as the DC coefficient of the DCT. RESULTS: When watermarks are embedded in a low frequency area, the subjective image quality is often degraded. To compensate for the degradation of the imperceptibility of the watermarking system, which results from embedding watermarks in the low-frequency component, this research considers the human visual system. In addition, the embedding strength of the JND value is used to improve the watermarking imperceptibility. CONCLUSIONS: We applied the proposed watermarking algorithm to a variety of medical images using a computer simulation. The algorithm's performance was verified using a variety of attacks for eliminating watermarks. The simulation results show that the proposed algorithm robustly provides protection against a variety of possible attacks.

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Frequency domain digital watermark recognition using image code sequences with a back-propagation neural network

Cited by 27 publications

References 12 publications

Robust watermarking technique using back propagation neural network: a security protection mechanism for social applications

Robust watermarking technique using back propagation neural network: a security protection mechanism for social applications

Guest Editorial: Smart Multimedia Communication Technologies for New-Generation Artificial Intelligence Applications

Medical image watermarking using bit threshold map based on just noticeable distortion in discrete cosine transform

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