Damageless Information Hiding Technique Using Neural Network

Naoe, Kensuke; Takefuji, Yoshiyasu

doi:10.1109/iih-msp.2008.220

Cited by 7 publications

(6 citation statements)

References 10 publications

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“…We would enhance our method by using those other approaches, such as fuzzy for dynamic visual data like video stream data and Bayesian network for continuous data structures. This article is different from the previous work by Ando et al (Ando, R. & Takefuji, Y., 2003) in terms of embedding size where this article does not embed any information to the target content and also implies that the machine learning algorithm is not limited only to the neural network model as proposed in our previous work (Naoe, K. & Takefuji, Y., 2008).…”

Section: Introductioncontrasting

confidence: 76%

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

Naoe

Sasaki

Takefuji

2010

International Journal of Systems and Service-Oriented Engineering

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The Service-Oriented Architecture (SOA) demands supportive technologies and new requirements for mobile collaboration across multiple platforms. One of its representative solutions is intelligent information security of enterprise resources for collaboration systems and services. Digital watermarking became a key technology for protecting copyrights. In this article, the authors propose a method of key generation scheme for static visual digital watermarking by using machine learning technology, neural network as its exemplary approach for machine learning method. The proposed method is to provide intelligent mobile collaboration with secure data transactions using machine learning approaches, herein neural network approach as an exemplary technology. First, the proposed method of key generation is to extract certain type of bit patterns in the forms of visual features out of visual objects or data as training data set for machine learning of digital watermark. Second, the proposed method of watermark extraction is processed by presenting visual features of the target visual image into extraction key or herein is a classifier generated in advance by the training approach of machine learning technology. Third, the training approach is to generate the extraction key, which is conditioned to generate watermark signal patterns, only if proper visual features are presented to the classifier. In the proposed method, this classifier which is generated by the machine learning process is used as watermark extraction key. The proposed method is to contribute to secure visual information hiding without losing any detailed data of visual objects or any additional resources of hiding visual objects as molds to embed hidden visual objects. In the experiments, they have shown that our proposed method is robust to high pass filtering and JPEG compression. The proposed method is limited in its applications on the positions of the feature sub-blocks, especially on geometric attacks like shrinking or rotation of the image.

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Section: Introductioncontrasting

confidence: 76%

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

Naoe

Sasaki

Takefuji

2010

International Journal of Systems and Service-Oriented Engineering

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“…Selanjutnya untuk melindungi Hak akan Kekayaan Intelektual (HKI) (Naoe & Takefuji, 2008) atas penyalahgunaan terhadap karya intelektual dalam hal ini citra digital "motif batik" perlu adanya Copyright perlindungan dalam hak cipta, digital watermarking digunakan sebagai sebuah cara yang efisien untuk mengatasi masalah HKI pada data multimedia, video, digital dan audio (Chang et al, 2009).…”

Section: Pendahuluanunclassified

Pengamanan Hak Cipta Citra Digital dengan Teknik Watermarking Menggunakan Metode Hybrid SVD dengan DWT

Asroni

Serumena

2021

JSA

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Era zaman digital saat ini, internet telah menjadi sebuah kebutuhan sehari-hari, yang memberikan kemudahan terhadap pengguna untuk melakukan aktivitas transmisi file, namun demikian data tersebut membutuhkan proteksi dari tangan yang tidak bertanggung jawab. Dalam penelitian ini di ajukan sebuah metode digital watermarking dalam melakukan perlindungan terhadap motif batik. Adapun algoritma yang diterapkan yaitu Hybrid Singular Value Decomposition dengan Discrete Wavelete Transform, setelah dilakukan experimen penerapan Hybrid Image Watermarking DWT dengan SVD nilai alpha dapat mempengaruhi tingkat imperceptibility terhadap citra watermarked, karena nilai alpha menunjukkan tingkat ketampakan (visible) watermark pada cover image. Semakin rendah nilai alpha maka tingkat ketampakan watermark semakin berkurang dan tingkat imperceptibility semakin tinggi. Dari pengujian yang dilakukan dengan object motif batik ditemukan nilai alpha terbaik yaitu nilai alpha 0.01, karena mendapatkan nilai PSNR tertinggi dari yang lainnya, sedangkan Berdasarkan pengujian tingkat robustness metode Hybrid Image Watermarking DWT-SVD pada subband LL memiliki tingkat ketahanan yang cukup tinggi terhadap upaya menghilangkan watermark yang menjadi identitas kepemilikan sah terhadap suatu citra digital selama citra watermarked tidak direkayasa dengan serangan noise.

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“…Kensuke Naoe and Yoshiyasu Takefuji, "Damageless Information Hiding Technique using Neural Network" Five neural networks are used to hide a classification of 32 secret patterns. and each pattern has five bits using one network to represent one binary digit for corresponding secret codes this method is used for small amount of data, since its very difficult to achieve that hiding [5]. E. Ashish Bansal and S. Singh Bhadauria, "watermarking using neural network and hiding the trained network within the cover image", This paper based on Backpropagation Neural Network to train a given cover image to produce a desired watermark image.…”

Section: Related Workmentioning

confidence: 99%

Text Hiding Using Artificial Neural Networks

Haider¹,

Baji²,

Mohammad³

2012

ETJ

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The growth of information technology and data transfer led to increase the data attacks, so that information security becomes an important issue to keep the data saved during information exchanges in computer networks. Steganography techniques used to protect the information from being detected. The art of steganography will hide secret information into cover data, which will be sending without any change so the attack does not recognize any change into cover image. This paper use the Steganography and artificial neural networks to presents an information hiding procedure for hiding text in cover image, the secret text will be converted to binary code, also the cover image will be converted to the binary data in form of vectors. The supervised learning of neural networks will use binary patterns of hidden text as set of input values, and the corresponding cover image data as target that used as teacher signal to neural network. The generated weights from neural network and the coordinate of data block of cover image have been saved and then used to extract hidden text data.

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Damageless Information Hiding Technique Using Neural Network

Abstract: Abstract

Cited by 7 publications

References 10 publications

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

Secure Key Generation for Static Visual Watermarking by Machine Learning in Intelligent Systems and Services

Pengamanan Hak Cipta Citra Digital dengan Teknik Watermarking Menggunakan Metode Hybrid SVD dengan DWT

Text Hiding Using Artificial Neural Networks

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