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Erschienen in:
IoT-Based DDoS Attack Detection and Mitigation Using the Edge of SDN Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

A Blind Watermarking Scheme Using Adaptive Neuro-Fuzzy Inference System Optimized by BP Network and LS Learning Model

verfasst von : Jilin Yang, Chunjie Cao, Jun Zhang, Jixin Ma, Xiaoyi Zhou

Erschienen in: Cyberspace Safety and Security

Verlag: Springer International Publishing

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Abstract

To maintain a trade-off between robustness and imperceptibility, as well as secure transmission of digital images over communication channels, a digital image blind watermarking scheme on the basis of adaptive neuro-fuzzy inference system (ANFIS) is proposed in this study. To achieve better results, an optimized ANFIS (OANFIS) combines a back propagation neural network and a least-square (LS) hybrid learning model. Each 3 × 3 non-overlap block of the original host image is selected to form a sample dataset, which is trained to establish a model of nonlinear mapping between the input and the output. As a sequence, the host image is decomposed by wavelet transform to obtain a low frequency subband. Finally, each watermark signal is adaptively embedded into the low frequency subband by OANFIS. Results show that our scheme is robust to various attacks while effectively maintaining satisfying transparency.

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Vorheriges Kapitel Lightweight Encryption Algorithm Containing SPN Structure for IoT
Nächstes Kapitel Towards Secure Computation of Similar Patient Query on Genomic Data Under Multiple Keys
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Metadaten
Titel
A Blind Watermarking Scheme Using Adaptive Neuro-Fuzzy Inference System Optimized by BP Network and LS Learning Model
verfasst von
Jilin Yang
Chunjie Cao
Jun Zhang
Jixin Ma
Xiaoyi Zhou
Copyright-Jahr
2019
Buch
Cyberspace Safety and Security

Print ISBN: 978-3-030-37351-1

Electronic ISBN: 978-3-030-37352-8

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-37352-8_23