Image hiding algorithm in Discrete Cosine Transform domain based on grey prediction and grey relational analysis

Huang, Haiping; Huang, Shucai; Chen, Jiutian; Wang, Ruchuan

doi:10.1109/cc.2013.6571289

Cited by 8 publications

(4 citation statements)

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“…The GP model or GM(1,1) was first proposed to deal with the data in grey system. It is able to analyze system that includes insufficient information and unapparent relationship [25][26][27]. Hence, the GP model is often used in predicting data in non-linear system based on limited information.…”

Section: Grey Prediction Model For Non-linear Behavior Forecastingmentioning

confidence: 99%

A hybrid model for short term real-time electricity price forecasting in smart grid

2018

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Background: With the prominent growth of power market, real-time electricity price has become a trend in smart grid as it enables moderation of power consumption of customers. Accurate forecast of real-time price (RTP) has much influence on customers' behaviors, such as better scheduling operating time of domestic appliances in order to maximize benefit. In this paper, an innovative hybrid RTP forecasting model considering linear and non-linear behaviors within input data, is proposed to forecast the short-term electricity prices in smart grid. Results: The effectiveness of the proposed hybrid forecasting model is verified by numerical results in terms of forecasting performance evaluations. The results clearly demonstrate that our approach is effective in RTP forecasting with a high accuracy. The mean absolute percentage error (MAPE) is approximate to 3.5% and it also significantly outperforms the existing models. Conclusion: Based on the achieved results, we can conclude that the proposed hybrid model is an accurate and efficient tool in short-term RTP forecasting and it is potentially effective to a variety of forecasting tasks.

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Section: Grey Prediction Model For Non-linear Behavior Forecastingmentioning

confidence: 99%

A hybrid model for short term real-time electricity price forecasting in smart grid

2018

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“…Experimental results show that, the proposed algorithm was robust against Gaussian noise and JPEG compression (Haiping et al, 2013). Zhang et al (2016) introduced a reversible, lossless and combined data hiding schemes for cipher text images.…”

Section: Introductionmentioning

confidence: 95%

“…Haiping et al (2013) proposed a novel method of a blind, colour image information-hiding algorithm based on grey prediction to hide the image. This algorithm compresses the secret image based on the improved grey prediction model and it chooses blocks of rich texture in the cover image as the embedding regions using DGRA (Doubledimension Grey Relational Analysis).…”

Section: Introductionmentioning

confidence: 99%

The clandestine image transmission scheme to prevent from the intruders

Almutairi

Manimurugan

2017

Int. j. adv. appl. sci

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The main aim of this technique is to prevent the capture of secret data during the exchange/transfer between the source and destination, without the knowledge of intruder. A secret message is represented as an image, and the same image is encrypted by the Triple Encryption (TE) technique. This can be divided into three types of processes. These processes are Reverse Matrix (RM) encode, Alpha-Encryption (AE) and Hill Cipher (HC) encryption. In the RM process, the original secret image is hidden within a cover image. The encoded cover image pixels are converted as characters using a lookup table in the AE process. The encrypted cipher characters are once again encrypted in the HC encryption process. The main advantage of this proposed technique is that, to strengthen the proposed technique the pixels positions are interchanged in every stage. After the encryption, the cipher's data are sent to the destination for the reconstruction process. The Triple Decryption (TD) technique is divided into three processes of: Inverse Matrix (IM) decode, Alpha-Decryption (AD) and HC decryption. The received cipher data are decrypted by the HC decryption and AD processes. Finally, the decrypted data is decoded by the IM decode process. In this process, the original secret and cover images are obtained. In addition, to measure the performance of the proposed algorithm, the standard parameters are considered. The results show that the proposed algorithm provided a high Peak Signal to Noise Ratio (PSNR), good Correlation Coefficient (CC), minimum execution time and high CIA (Confidentiality, Integrity and Authentication) compared to existing encryption methods. Therefore, using this proposed method, we can obtain 100% of the original image and the secret data can be prevented from interception by intruders/third parties.

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“…The foundation of grey relational analysis is to clarify the primary and secondary relationships among different factors in the system through calculating relational degree, and find out the influential factors [11]. Grey relational analysis has been extensively used in areas like image hiding algorithm [12], ship's roll motion analysis [13], classification [14] and predictive behavior [15−16]. In addition, grey prediction model on the basis of grey system theory is usually used in aspect of prediction [17−18].…”

Section: Introductionmentioning

confidence: 99%

A strip thickness prediction method of hot rolling based on D_S information reconstruction

Sun

Shao

Zhang

2015

J. Cent. South Univ.

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To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method (DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, ibaAnalyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment (BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

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Image hiding algorithm in Discrete Cosine Transform domain based on grey prediction and grey relational analysis

Cited by 8 publications

References 0 publications

A hybrid model for short term real-time electricity price forecasting in smart grid

A hybrid model for short term real-time electricity price forecasting in smart grid

The clandestine image transmission scheme to prevent from the intruders

A strip thickness prediction method of hot rolling based on D_S information reconstruction

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