Design and implementation of DPFC for multi-bus  power system

Krishna, B. Vamsi; Prashanth, B. U. V.; Sujatha, P.

doi:10.14419/ijet.v7i2.8.10318

Cited by 5 publications

(1 citation statement)

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“…We propose a digital watermarking method using deep learning methods which exploit the expressiveness of deep NN to securely embed invisible, imperceptible, attackresilient binary signatures into the cover images. Coming to the decoder, we adopt adversarial model techniques to cause disorders to decode the desired signature [44]. We perform extended gradient descent under the Expectation over Transformation framework.…”

Section: Algorithm Designmentioning

confidence: 99%

A Computational Modelling and Algorithmic Design Approach of Digital Watermarking in Deep Neural Networks

Kavitha¹,

Eranna²,

Giriprasad³

2020

Adv. sci. technol. eng. syst. j.

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In this paper we propose an algorithmic approach for Convolutional Neural Network (CNN) for digital watermarking which outperforms the existing frequency domain techniques in all aspects including security along with the criteria in the neural networks such as conditions embedded, and types of watermarking attack. This research addresses digital watermarking in deep neural networks and with comprehensive experiments through computational modeling and algorithm design, we examine the performance of the built system to demonstrate the potential of watermarking neural networks. The inability of intruder towards the retrieval of data without the knowledge of architecture and keys is also discussed and results of the proposed method are compared with the state of the art methods at different noises and attacks.

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Section: Algorithm Designmentioning

confidence: 99%