A digital watermarking scheme based on autoassociative neural networks of the geometric transformations model

Tsymbal, Yurii; Tkachenko, Roman

doi:10.1109/dsmp.2016.7583547

Cited by 11 publications

(7 citation statements)

References 14 publications

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“…One of the promising directions for the construction of high-performance neural network means is the application of the "model of successive geometric transformations" (MSGT) paradigm proposed and developed by R. Tkachenko [16][17].…”

Section: Analysis Of Publicationsmentioning

confidence: 99%

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Neural-Like Means for Data Streams Encryption and Decryption in Real Time

Tsmots

Tsymbal

Khavalko

et al. 2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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The "model of successive geometric transformations" paradigm has been adapted for the implementation of parallel-streaming neural network encryption-decryption of data in real time. A model and structure of a parallel-streaming neural-like element for the mode have been developed. Keywords-intensive data stream; neural networks; geometric transformations model I. INTRODUCTION The latest information technologies are becoming global in the modern world. Their development and development of communications provides ever-widening opportunities for access to information resources and the transfer of large amounts of data for unlimited distances. In the context of the intensive development of the market for information products and services, information has become a full-fledged product that has its own consumer properties and cost characteristics. The widespread introduction of information technology makes a relevant problem for the protection of the transmission of information using cryptographic methods that provide encryption of the ready-to-transmit information. The encrypted information is transmitted by a communication channel to an authorized user, who, after receiving it, performs decryption using a reverse transformation. Cryptographic transformations are carried out using special algorithms. In order to encrypt and decrypt real-time data streams, it is suggested to use neural-like network algorithms, the key in which is the network architecture, weighting factors, and masking codes.

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Section: Analysis Of Publicationsmentioning

confidence: 99%

“…When choosing the structure of a neural-like network for real-time encryption-decryption of data streams, it is proposed to use the architecture of an auto-associative network with one hidden layer [17] (Fig. 1).…”

Section: A the Model Of Successive Geometric Transformationsmentioning

confidence: 99%

Neural-Like Means for Data Streams Encryption and Decryption in Real Time

Tsmots

Tsymbal

Khavalko

et al. 2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

Self Cite

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“…Paper [18] presents methods of multidimensional data visualization based on the autoassociative SGTM. The same SGTM neural-like structure was used in solving the digital image watermarking task [25].…”

Section: Introductionmentioning

confidence: 99%

An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks

Tkachenko

2021

Lecture Notes on Data Engineering and Communications Technologies

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The paper presents a developed software solution that implements a new learning model and application of artificial neural networks, i.e. the Successive Geometric Transformations Model, to solve various applied data mining tasks. This model is applied to construct Feed Forward Neural Networks, which anticipates the rejection of training goal interpretation as a multi-extremal optimization procedure. The modes of use of the system developed (prediction, time series, and cascade modes) are described in detail and illustrated, as well as the structure and functions of separate constituents of the graphical user interface; data selection and download procedures; neuro-like structure parameterization capabilities for each different mode of operation of the program. Simulation of the developed integral software solution was performed while fulfilling prediction, classification, and time series forecasting. Besides, the feasibility of performing nonlinear PCA analysis at high speed is shown. When performing all the above tasks, the program outcomes are presented in both numerical form and using the appropriate one for a particular mode of visualization in 2D or 3D formats.

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“…In papers [5,6], the importance of signal protection for video traffic was shown. The auto associative neural networks approach for the creation of a watermarking scheme is presented in [7]. All authors have denoted that the watermark methods are considerable.…”

Section: Introductionmentioning

confidence: 99%

Constructing of Digital Watermark Based on Generalized Fourier Transform

2020

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We develop in this paper a method for constructing a digital watermark to protect one-dimensional and two-dimensional signals. The creation of a digital watermark is based on the one-dimensional and two-dimensional generalized Fourier and Hartley transformations and the Ateb-functions as a generalization of trigonometric functions. The embedding of the digital watermark is realized in the frequency domain. The simulation of attacks on protected files is carried out to confirm the stability of the proposed method. Experiments proved the high stability of the developed method conformably to the main types of attacks. An additional built-in digital watermark can be used to identify protected files. The proposed method can be used to support the security of a variety of signals—audio, images, electronic files etc.—to protect them from unauthorized access and as well for identification.

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A digital watermarking scheme based on autoassociative neural networks of the geometric transformations model

Cited by 11 publications

References 14 publications

Neural-Like Means for Data Streams Encryption and Decryption in Real Time

Neural-Like Means for Data Streams Encryption and Decryption in Real Time

An Integral Software Solution of the SGTM Neural-Like Structures Implementation for Solving Different Data Mining Tasks

Constructing of Digital Watermark Based on Generalized Fourier Transform

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