Large-Capacity Image Data Hiding based on Table Look-up

Abstract In previous data hiding techniques, binary rules are usually used to guide the fine-tuning of the values of basic objects in the host media to hide bit 0 and bit 1. In this paper, we propose a new data hiding technique for gray images based on querying a 256x256 information table. The information table is constructed by cloning a 3x3 basic block, which we call seed block. Eight unsigned integer values between 0 and 7, i.e., 3 bit binary data, are assigned to different elements of the seed block. Each time, a pair of pixels are chosen from a host image, and their pixel values are used as row and column numbers to look up the information table. If element value obtained by looking up the table is equal to the 3 bit binary data to be hidden, the values of the pixel pair will remain unchanged. Otherwise, take this element as the central point, we call it the focus element, to enclose a 3x3 window in the information table. Then in the window, find the element which is equal to the data to be hidden. Finally, update the pixel values of the pair with the row and column numbers of the found element in the window. Since the row and column numbers are in the range of 0-255, the updated pixel values will not overflow. In the proposed algorithm, a pair of pixels can hide 3 bits of information, so the embedding capacity is very high. Since the adjustment of pixel values is constrained in a 3x3 window, the modification amount of pixel values is small. The proposed technique belongs to fragile digital watermarking, so it can be used for image authentication and tamper localization. By the evaluation of data hiding capacity, security, imperceptibility, computational cost and extensibility, this algorithm is superior to existing information hiding techniques. The proposed technique can also be used in color image and audio data hiding.

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A data hiding method based on information sharing via PNG images for applications of color image authentication and metadata embedding

Signal Processing ◽

10.1016/j.sigpro.2013.01.009 ◽

2013 ◽

Vol 93 (7) ◽

pp. 2010-2025 ◽

Cited By ~ 21

Author(s):

Che-Wei Lee ◽

Wen-Hsiang Tsai

Keyword(s):

Information Sharing ◽

Data Hiding ◽

Color Image ◽

Image Authentication

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Improving Data Hiding Capacity Using Bit-Plane Slicing of Color Image Through (7, 4) Hamming Code

Advances in Intelligent Systems and Computing - Information Systems Design and Intelligent Applications ◽

10.1007/978-981-10-7512-4_36 ◽

2018 ◽

pp. 356-367 ◽

Cited By ~ 1

Author(s):

Ananya Banerjee ◽

Biswapati Jana

Keyword(s):

Data Hiding ◽

Color Image ◽

Hamming Code ◽

Hiding Capacity ◽

Bit Plane ◽

Bit Plane Slicing

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Convolution-Based Encoding of Depth Images for Transfer Learning in RGB-D Scene Classification

Sensors ◽

10.3390/s21237950 ◽

2021 ◽

Vol 21 (23) ◽

pp. 7950

Author(s):

Radhakrishnan Gopalapillai ◽

Deepa Gupta ◽

Mohammed Zakariah ◽

Yousef Ajami Alotaibi

Keyword(s):

Transfer Learning ◽

Single Channel ◽

Color Image ◽

Computational Cost ◽

Fine Tuning ◽

Depth Information ◽

Scene Classification ◽

Class Imbalance Problem ◽

Depth Sensors ◽

Depth Images

Classification of indoor environments is a challenging problem. The availability of low-cost depth sensors has opened up a new research area of using depth information in addition to color image (RGB) data for scene understanding. Transfer learning of deep convolutional networks with pairs of RGB and depth (RGB-D) images has to deal with integrating these two modalities. Single-channel depth images are often converted to three-channel images by extracting horizontal disparity, height above ground, and the angle of the pixel’s local surface normal (HHA) to apply transfer learning using networks trained on the Places365 dataset. The high computational cost of HHA encoding can be a major disadvantage for the real-time prediction of scenes, although this may be less important during the training phase. We propose a new, computationally efficient encoding method that can be integrated with any convolutional neural network. We show that our encoding approach performs equally well or better in a multimodal transfer learning setup for scene classification. Our encoding is implemented in a customized and pretrained VGG16 Net. We address the class imbalance problem seen in the image dataset using a method based on the synthetic minority oversampling technique (SMOTE) at the feature level. With appropriate image augmentation and fine-tuning, our network achieves scene classification accuracy comparable to that of other state-of-the-art architectures.

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Controlled Data Hiding Technique for Color Image Authentication in Frequency Domain (CDHTCIAFD)

2011 Second International Conference on Emerging Applications of Information Technology ◽

10.1109/eait.2011.23 ◽

2011 ◽

Cited By ~ 2

Author(s):

Nabin Ghoshal ◽

J.K. Mandal

Keyword(s):

Frequency Domain ◽

Data Hiding ◽

Color Image ◽

Image Authentication

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Improved data hiding capacity through repeated embedding using modified weighted matrix for color image

International Journal of Computers and Applications ◽

10.1080/1206212x.2017.1422587 ◽

2018 ◽

Vol 41 (3) ◽

pp. 218-232

Author(s):

Partha Chowdhuri ◽

Pabitra Pal ◽

Biswapati Jana

Keyword(s):

Data Hiding ◽

Color Image ◽

Hiding Capacity ◽

Weighted Matrix

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TLBO-FLN: Teaching-Learning Based Optimization of Functional Link Neural Networks for Stock Closing Price Prediction

International Journal of Sensors Wireless Communications and Control ◽

10.2174/2210327909666191202113015 ◽

2020 ◽

Vol 10 (4) ◽

pp. 522-532 ◽

Cited By ~ 1

Author(s):

Sarat Chandra Nayak ◽

Subhranginee Das ◽

Mohammad Dilsad Ansari

Keyword(s):

Neural Networks ◽

Computational Cost ◽

Optimization Techniques ◽

Fine Tuning ◽

Functional Link ◽

Price Prediction ◽

Closing Price ◽

Teaching Learning Based Optimization ◽

Artificial Neural ◽

Teaching Learning

Background and Objective: Stock closing price prediction is enormously complicated. Artificial Neural Networks (ANN) are excellent approximation algorithms applied to this area. Several nature-inspired evolutionary optimization techniques are proposed and used in the literature to search the optimum parameters of ANN based forecasting models. However, most of them need fine-tuning of several control parameters as well as algorithm specific parameters to achieve optimal performance. Improper tuning of such parameters either leads toward additional computational cost or local optima. Methods: Teaching Learning Based Optimization (TLBO) is a newly proposed algorithm which does not necessitate any parameters specific to it. The intrinsic capability of Functional Link Artificial Neural Network (FLANN) to recognize the multifaceted nonlinear relationship present in the historical stock data made it popular and got wide applications in the stock market prediction. This article presents a hybrid model termed as Teaching Learning Based Optimization of Functional Neural Networks (TLBO-FLN) by combining the advantages of both TLBO and FLANN. Results and Conclusion: The model is evaluated by predicting the short, medium, and long-term closing prices of four emerging stock markets. The performance of the TLBO-FLN model is measured through Mean Absolute Percentage of Error (MAPE), Average Relative Variance (ARV), and coefficient of determination (R2); compared with that of few other state-of-the-art models similarly trained and found superior.

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