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2017 4th NAFOSTED
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Table of Contents
2017 4th NAFOSTED Conference on Information and Computer Science
Computational Intelligence
Communications and Networking
Similarity Measures for Intuitionistic Linguistic Numbers, Intuitionistic Linguistic Vectors and Application
Vu Hue (National University of Civil Engineering, Vietnam), Pham Hong Phong (National
University of Civil Engineering, Vietnam) 1
Anomaly Detection in Wireless Sensor Networks via Support Vector Data Description with Mahalanobis Kernels and Discriminative Adjustment
Van Vuong Trinh (Dong A University, Vietnam), Kim Phuc Tran (Division of Artificial
Intelligence, Dong A University Research Institute & Dong A University, Vietnam), Tuan Mai
Anh (Hanoi University of Science and Technology, Vietnam) 7
APP-RB: an efficient method for biological network querying based on rebuild procedure Ngọc Anh Vũ (The Hanoi College of Industrial Economics, Vietnam), Vương Nguyễn (VNU University of Engineering and Technology, Vietnam), Hải Đặng Thanh (Vietnam National
University-Hanoi, Vietnam), Dong Do Duc (VNU, Hanoi, Vietnam) 13 Domain Adaptation for Neural Network Language Generation in Dialogue
Khanh Van Tran (Japan Advanced Institute of Science and Technology, Japan), Minh Le
Nguyen (Japan Advanced Institute of Science and Technology, Japan) 19
An AF Performance Analysis in the energy harvesting Relaying Network
Tam Nguyen Kieu (Ton Duc Thang University, Ho Chi Minh City, Viet Nam., Vietnam), Miroslav Voznak (VSB-Technical University of Ostrava, Czech Republic), Long Nguyen Ngoc (Ton Duc Thang University, Ho Chi Minh City., Vietnam), Nhu Nguyen Hong (Ho Chi Minh CITY TECHNICAL ECONOMIC COLLEGE, Vietnam), Nguyen Thi Phuong Loan (Posts and Telecommunications Institute of Technology, Vietnam), Duc Le (VSB Technical University of Ostrava & Ho Chi Minh City Technical and Economic College, Vietnam), Jaroslav Zdralek (VSB
– Technical University of Ostrava, Czech Republic) 25
Improving the secrecy of cooperative transmissions using unshared jamming
Ngoc Son Pham (Ho Chi Minh City University of Technology and Education, Vietnam), Van Phu Tuan (University of Ulsan, Korea), Sol Park (University of Ulsan, Korea), Thi Anh Le (Ulsan
University, Korea), Hyung-Yun Kong (University of Ulsan, Korea) 31 High-Accuracy Positioning System based on ToA for Industrial Wireless LAN
Tran Thi Thao Nguyen (Kyushu Institute of Technology, Japan), Khairunisa Ahmad Denney (Kyushu Institute of Technology, Japan), Ohhara Syuhei (Kyushu Institute of Technology, Japan), Yuhei Nagao (Kyushu Institute of Technology, Japan), Masayuki Kurosaki (Kyushu
Institute of Technology, Japan), Hiroshi Ochi (Kyushu Institute of Technology, Japan) 37 Performance of a Phase Estimation Method under Different Nonlinearities Incurred by High
Power Amplifiers in MIMO-STBC Systems
Thanh Nguyen (Le Quy Don Technical University, Vietnam), Tat-Nam Nguyen (Le Quy Don Technical University, Vietnam), Nguyen Quoc Binh (Vietnamese Military Technical Academy,
Vietnam) 42
Foundation of Computer Science
Computational Intelligence
Communications and Networking
Picture fuzzy rough sets: Some new basic propositions
Bui Cong Cuong (Institute of Mathematics, Academy of Science and Technology, Vietnam), Hung Le (Banking Academy of Vietnam, Vietnam), Pham Van Chien (Hanoi University of
Science and Technology, Vietnam) 48
Certificateless Public Key Encryption Made Practical
Cuong Trinh (Hong Duc University, Vietnam) 54
Resource Allocation with Multi-Unit Items: Representation and Computational Results
Trung Thanh Nguyen (Hai Phong University, Vietnam), Khuong Nguyen (Hai Phong University,
Vietnam), Nguyen Le (Hai Phong University, Vietnam) 60
Distributed Redundant Image Storages and Reconstruction Algorithm to Contents Verification
Ta Minh Thanh (Tokyo Institute of Technology, Japan) 66
RBF Models with Shallow and Deep Feature for Skeleton-based Human Gesture Recognition Nguyen Hai (Kyoto University, Japan), Thang Pham (Tay Bac University, Vietnam), Dung Duc Nguyen (Institute of Information Technology & Vietnam Academy of Science and Technology,
Vietnam) 72
Enhancing Threshold-Raising Strategies for Effective Mining Top-k High Utility Patterns Bac Le (University of Science, Vietnam), Cao Truong (Kien Giang Teacher Training College, Vietnam), Minh-Thai Tran (Ho Chi Minh City University of Foreign Languages - Information
Technology, Vietnam) 78
A Robust Random Forest-based Tri-Training Algorithm for Early In-trouble Student Prediction Chau Vo (Ho Chi Minh City University of Technology, Vietnam), Phung Nguyen (CSE/Ho Chi
Minh City University of Technology, Vietnam) 84
A New method for Displayed Mathematical Expression Detection Based on FFT and SVM Bui Hai Phong (MICA, HUST, Vietnam, Vietnam), Thang Manh Hoang (Hanoi University of
Science and Technology, Vietnam), Thi-Lan Le (MICA, HUST, Vietnam) 90 Gender classification by LPQ features from intensity and Monogenic images
Huu-Tuan Nguyen (Faculty of Information Technology, Vietnam Maritime University, 484 Lach tray, Le Chan, Hai Phong, Vietnam, Vietnam), Ngoc Huong Trinh (Faculty of Information
Technology, Vietnam Maritime University, Vietnam) 96
A Simple Method for Anonymous Tag Cardinality Estimation in RFID Systems with False Detection
Chuyen T. Nguyen (Hanoi University of Science and Technology, Vietnam), Tuyen Hoang (Hanoi University of Science and Technology, Vietnam), Xuan Vu Phan (Hanoi University of
Science and Technology, Vietnam) 101
A new multi-proxy multi-signature scheme based on elliptic curve cryptography
Dang Tuan (Vietkey, Vietnam), Viet Nguyen (Institute of Military Science and Technology &
17 Hoang Sam - Cau Giay - Ha Noi, Vietnam) 105
Identification of Paths and Parameters in RESTful URLs for the Detection of Web Attacks
Duc Pham Minh (VNU University of Engineering and Technology, Vietnam), Tuan Ly Van (VNU University of Engineering and Technology, Vietnam), Thanh Le Dinh (VNU University of
Engineering and Technology, Vietnam) 110
Intelligent Environment Management System for Controlled Horticulture
Meonghun Lee (National Institute of Agricultural Sciences, Korea), Haengkon Kim (Catholic
University of Daegu, Korea), Hyun Yoe (Sunchon National University, Korea) 116
v
Foundations of Computer Science
Communications and Networking
HAP-based FSO System using All-Optical Detect-Amplify-and-Forward Relaying and Coherent Detection Receiver
Minh Vu (Posts and Telecommunications Institute of Technology, Vietnam), Nga Nguyen Thi Thu (Posts and Telecommunications Institute of Technology, Vietnam), Binh-Minh Vu (Posts and Telecommunications Institute of Technology, Vietnam), Hang Phan (Hanoi University of Industry, Vietnam), Hien Pham (Posts and Telecommunications Institute of Technology,
Vietnam), Ngoc Dang (Posts and Telecommunications Institute of Technology, Vietnam) 120
A Comparative Survey of 3D GIS Models
Tuan Anh Nguyen Gia (UIT, Vietnam), Minh-Son Dao (Universiti Teknologi Brunei, Brunei Darussalam & University of Information Technology, Vietnam), Cuong Mai-Van (UIT,
Vietnam) 126
Automatic dragon fruit counting on the tree using adaptive thresholds for image segmentation and shape analysis
Chi Cuong Tran (CanTho University, Vietnam), Dinh Tu Nguyen (CanTho University, Vietnam), Hoang Dang LE (CanTho University, Vietnam), Quoc Dinh Truong (CanTho University,
Vietnam), Quoc Bao Truong (CanTho University, Vietnam) 132
Multilinear Mappings Based on Weil Pairing over Elliptic Curves
Binh Nguyen (University of Science, Vietnam), My Huynh Tran Thi (University of Science,
Vietnam) 138
Solving the staff rescheduling problem in Lai Chau hydropower station
Minh Hoàng Hà (UET-VNU, Vietnam), Thuy Do (FPT University, Vietnam), Giang Pham Hoang
(FPT University, Vietnam) 144
A New Windowed Graph Fourier Transform
Le Trung-Thanh (Vietnam National University, Hanoi, Vietnam), Nguyen Linh-Trung (Vietnam National University, Hanoi, Vietnam), Viet-Dung Nguyen (University of Paris Saclay, France),
Karim Abed-Meraim (Polytech'Orléans & University of Sharjah, UAE, France) 150 HA-IDS: A Heterogeneous Anomaly-based Intrusion Detection System
Chau Tran (Ho Chi Minh University of Technology, Vietnam), Tran Nguyen Vo (Faculty of Computer Science and Engineering Ho Chi Minh City University of Technology, Vietnam), Tran
Thinh (Ho Chi Minh City University of Technology, Vietnam) 156
Pilot-based Reference Amplitude Compensation for Ultra-multi-level PAM-SSB-DFTs-OFDM Tomoya Suzuki (Ibaraki University, Japan), Hirokazu Fusayasu (Ibaraki University, Japan), Masahiro Umehira (Ibaraki University, Japan), Shigeki Takeda (Ibaraki University, Japan),
Xiaoyan Wang (Ibaraki University, Japan) 162
A Comparison for Improving The Performance of Two-Stage Optical Phase Conjugation Using The Third-Order Nonlinearity
Binh Tran (Posts and Telecommunications Institute of Technology, Vietnam), Nhan Nguyen (Posts and Telecommunications Institute of Technology, Vietnam), Ngo-Thi-Thu- Trang (Posts
and Telecommunications Institute of Technology, Vietnam) 168
Jamming Signal and Primary Interference in Spectrum Sharing Environment: Performance Analysis
Khuong Ho-Van (HoChiMinh City University of Technology, Vietnam), Thiem Do-Dac (Thu Dau Mot University, Vietnam), Ngoc Pham-Thi-Dan (HoChiMinh City University of Technology, Vietnam), Phong Nguyen - Huu (Broadcast Research & Application Center (BRAC), Vietnam), Tuan Nguyen-Thanh (HoChiMinh City University of Technology, Vietnam), Pham Ngoc Son (Ho Chi Minh City University of Technology and Education, Vietnam), Son Vo (Ho Chi Minh City University of Technology, Vietnam), Lien Pham Hong (University of Technical Education,
Vietnam) 174
LSI design and IoT Applications
Software Engnineering
An Energy-Efficient Smart Monitoring System Using ZigBee Wireless Sensor Network Van-Lan Dao (Le Quy Don Technical University, Vietnam), Xuan-Thiep Nguyen (Viettel Research and Development Institute, Vietnam), Van-Phuc Hoang (Le Quy Don Technical
University, Vietnam) 179
An Efficient Runtime Adaptable Floating-Point Gaussian Filtering Core
Cuong Pham-Quoc (Ho Chi Minh City University of Technology (HCMUT) & Faculty of Computer Science and Engineering, Vietnam), Tran Thinh (Ho Chi Minh City University of
Technology, Vietnam) 183
RTL Design of a Dynamically Reconfigurable Cell Array for Multimedia Processing
Hung Nguyen (VNU University of Engineering and Technology, Vietnam), Minh Phan (VNU
University of Technology and Engineering, Vietnam) 189
Short-term prediction of energy consumption of air conditioners based on weather forecast HoaiSon Nguyen (University of Engineering and Technology, VNU-Hanoi, Vietnam), Yoshiki Makino (Japan Advanced Institute of Science and Technology & National Institute of
Information and Communications Technology, Japan), Yuto Lim (Japan Advanced Institute of Science and Technology (JAIST) & School of Information Science, Japan), Yasuo Tan (JAIST,
Japan) 195
Accurate and Low Complex Cell Histogram Generation By Bypass The Gradient of Pixel Computation
Huy-Hung Ho (VNU University of Engineering and Technology, Vietnam), Ngoc-Sinh Nguyen (VNU University of Engineering and Technology, Vietnam), Duy-Hieu Bui (VNU University of Engineering and Technology (VNU-UET), Vietnam), Xuan-Tu Tran (Vietnam National
University, Hanoi, Vietnam) 201
Automated testing reactive systems from Event-B model
Dieu Huong Vu (Vietnam National University, Hanoi, Vietnam), Hoang Truong (VNU University of Engineering and Technology, Vietnam), Yuki Chiba (JAIST, Japan), Toshiaki Aoki (JAIST,
Japan) 207
An Evaluation on the Effective Processing Time of Raspberry Pi-3B-based Embedded System for Determining Forged Images
Thuong Le-Tien (Hochiminh city University of Technology, Vietnam), Tu Huynh-Kha (Ho Chi Minh City University of Technology, VNUHCM & International University, VNUHCM, Vietnam), Tuan Thanh Nguyen (HCM City Uni. of Technology, National University, Australia), Thinh Pham (HCM City Uni. of Technology, National University, Vietnam), Tin Nguyen (HCM City
Uni. of Technology, National University, Vietnam) 213
A k-Nearest Neighbors Approach for COCOMO Calibration
Phu Le (Global Cybersoft (JSC) Vietnam, Hitachi Consulting, Vietnam), Vu Nguyen (University
of Science & Vietnam National University - Ho Chi Minh city, Vietnam) 219 Enhancing the Performance of Android Applications on Multi-core Processors by Selecting
Parallel Configurations for Source Codes
Huong Pham (Academy of Cryptography Techniques, Vietnam), Phuc Bui Huu (VNU University
of Engineering and Technology, Vietnam) 225
A Formal Contract-based Model for Component-based Real-time Systems
Dong Trinh Nguyen (Faculty of Information Technology & Hai Phong Private University, Vietnam), Hưng Đặng (VNU-UET, Vietnam), Hoang Truong (VNU University of Engineering
and Technology, Vietnam) 230
vii
Computational Intelligence
Communications and Networking
Link Prediction in Weighted Network based on Reliable Routes by Machine Learning Approach Dung Hua (University of Information Technology & Vietnam National University Ho Chi Minh City., Vietnam), Anh-Thu Nguyen-Thi (University of Information Technology, VNU-HCM, Vietnam), Tu-Anh Nguyen-Hoang (University of Information Technology - VNU-HCM,
Vietnam) 236
Random walk with restart: A powerful network propagation algorithm in Bioinformatics field
Duc-Hau Le (Water Resources University, Vietnam) 242
Building minimum recombination ancestral recombination graphs for whole genomes
Thao Nguyen (Vietnam Academy of Science and Technology, Vietnam), Le Sy Vinh (VNU Ha
Noi, Vietnam) 248
Deep Neural Network Accelerator based on FPGA
Thang Viet Huynh (Danang University of Science and Technology, Vietnam) 254 Co-Reference Resolution in Graph Model for Enhancing Vietnamese Paragraph Compression
Trung Tran (University of Information Technology, VNU - HCMC, Vietnam), Dang Tuan
Nguyen (University of Information Technology, VNU-HCM, Vietnam) 258 A Robust Geometric Model of Road Extraction Method for Intelligent Traffic System
Van-Tuyen Dinh (Nguyen Tat Thanh University & Ho Chi Minh City University of Technology,
Vietnam), Hoang-Hon Trinh (HCMUT, Vietnam) 264
VNU-SMM: A Social Media Monitoring Framework on Vietnamese Online News
Duc Nguyen (University of Engineering and Technology, Vietnam), Duc M. Nguyen (University of Engineering and Technology, Vietnam), Vu Tran (University of Engineering and
Technology, Vietnam), Phan Xuan Hieu (VNU Ha Noi, Vietnam), Pham Son (Vietnam National
University, Thailand) 269
Impact of Timing Mismatch in Multicarrier Spectral-Slicing Transmission System Using Single Coherent Receiver
Van Dien Nguyen (The University of Danang, Vietnam), Son Thai Le (Nokia-Bell-Labs, Germany), Cao Dung Truong (Hanoi University of Science and Technology, Vietnam), Tuan Nguyen Van (Da Nang University, Vietnam), Hung Nguyen (The University of Danang,
Vietnam) 275
A Duplicated Network Structure to Ensure the Reliability of an LTE-based Train Control System
Nguyen Dinh Han (Hung Yen University of Technology and Education, Vietnam) 281 A Compact Triplexer Based on Cascaded Three Tilted MMI Couplers Using Silicon Waveguides
Cao Dung Truong (Hanoi University of Science and Technology, Vietnam), Hung Nguyen (The
University of Danang, Vietnam) 287
Toward Service Placement on Fog Computing Landscape
Quang Tran Minh (Hochiminh City University of Technology, Vietnam), Duy Tai Nguyen (Ho Chi Minh University of Technology, Vietnam), An Van Le (Hochiminh City University of
Technology, Vietnam), Hai Duc Nguyen (Ho Chi Minh City University of Technology, Vietnam),
Anh Truong (Ho Chi Minh City University of Technology, Vietnam) 291 Channel Reallocation for Reducing Power Consumption in Femtocell Mobile Networks
Nam-Hoang Nguyen (VNU-Uiversity of Engineering and Technology, Vietnam), Bac Nguyen (University of Engineering and Technology, Vietnam), Takahiko Saba (Chiba Institute of
Technology, Japan) 297
An Adaptive Streaming Method of 360 Videos over HTTP/2 Protocol
Minh Nguyen (Hanoi University of Science and Technology, Vietnam), Dang Nguyen (Hanoi University of Science and Technology, Vietnam), Cuong Pham (Hanoi University of Science and Technology, Vietnam), Pham Nam (Hanoi University of Science and Technology & School of Electronics and Telecoms, Vietnam), Duc V. Nguyen (The University of Aizu, Japan),
Truong Cong Thang (The University of Aizu, Japan) 302
Multimedia
Joint Exploration Model based Light Field Image Coding: A Comparative Study
Huy Phi (Posts and Telecommunications Institute of Technology, Vietnam), Xiem HoangVan
(Vietnam National University & University of Engineering and Technology, Vietnam) 308 HEVC based distributed scalable video coding for surveillance visual system
Xiem HoangVan (Vietnam National University & University of Engineering and Technology, Vietnam), Le Dao Thi Hue (University of Engineering and Technology, Vietnam), Dinh Trieu
Duong (VNU, Vietnam) 314
An optimized hardware design of Integer Motion Estimation HEVC for encoding 8K Video Nguyen Thang (HUST, Vietnam), Tung Vu (School of Electronics & Telecommunications,
Vietnam), Hoan Nguyen (School of Electronics & Telecommunications, Vietnam) 319 Mobile Devices Pixel Density and Video Quality
Is-Haka Mkwawa (University of Plymouth, United Kingdom (Great Britain)), Abubakr Almnsi
(Sudan University of Science and Technology, Sudan) 325
A Robust Hybrid Watermarking Scheme Based on DCT and SVD for Copyright Protection of Stereo Images
Hung Vo (Tra Vinh University & Tra Vinh Province, Vietnam) 331 Adaptive steganography technique to secure patient confidential information using ECG signal
Liem Dao (Saigon Technology University, Vietnam), Thy Nguyen (Saigon Technology
University, Vietnam), Tu Huynh (Saigon Technology University, Vietnam) 336
ix
Distributed Redundant Image Storages and Reconstruction Algorithm to Contents Verification
Ta Minh Thanh
Dept. of Network Security, Le Quy Don Technical University, 236 Hoang Quoc Viet, Cau Giay, Ha Noi, Viet Nam. Email: [email protected].
Abstract—In this paper, we propose a novel contents verifi- cation method using the distributed redundant image storages and user-based reconstruction algorithm. There are two pro- cesses composing in our proposal such as the image dispersion method and the image reconstruction method. In the image dispersion method, the images are dispersed into many infor- mation patches and are stored on the distributed storages,e.g Dropbox, Google Drive, ... Since the size of information patches is smaller than the full-size of image, the information patches can be distributed on cloud storages via network. Therefore, the resource of cloud storages is used effectively. On the other hand, in the image reconstruction method, the information patches of the image can be collected in order to reconstruct the image for user. The key point of idea in the image reconstruction method is that the reconstructed images should be distinguished each others and the producer can detect the user that it belongs to. The algorithm of reconstruction method employs the watermarking method to generate the watermark pattern based on the user’s information such as license number. The demonstration results of our proposed method show that our system can be used as the contents verification in the digital contents distribution system.
Keywords-Image Watermarking, Contents Verification, Cloud Storages, Redundant Image Storages, Image Reconstruction, Information Patches, Digital Rights Management (DRM).
I. INTRODUCTION
A. Background
With the development of network, the data transmission and data storage via Internet has become the most popular way for various forms of digital media such as picture, audio, movie, and so on. The end-users also can buy and redistribute the digital contents anytime and anywhere in order to make more benefit. However, everyone can easily copy and alter or even stole the digital contents. It has outstretched the security concerns of digital multimedia content such as copyright protection, owner’s right problem, legal user verification and so on. Therefore, the need for an effective dispersion cloud storages and contents rights man- agement system, where only the legitimate users can access to the digital content, is required recently. Additionally, our technique proposes a new idea for employing the digital contents on the cloud system to digital rights management (DRM).
There are several solutions that are proposed for the purpose of contents verification and users verification. En- cryption is an efficient solution for copyright protection since only the legal users who bought the decrypted key from the producers can access the encrypted contents. However, if the legal users redistribute illegally the decrypted contents to get more benefit, the producers cannot detect the redistributed source because there is no copyright information embedded into the contents. That means the encryption cannot protect the copyright of the evaluated contents so far.
In order to improve the disadvantage of encryption method, digital watermarking techniques are used for em- bedding directly the copyright information into the digital contents [1]. With the existence of the watermark informa- tion, watermarking technique can limit the users to distribute again the digital contents via network. However, the ignored users still distribute the contents widely in Internet, there- fore, the watermarking techniques also cannot stop the illegal distribution and illegal use efficiently.
Especially, the user-based watermarking is also researched for detect the traitor when the legal users redistribute the digital contents [2], [7], [3]. However, once the user-based watermark is embedded, only a user can be detected and be proved. Therefore, the watermarking techniques should be improved for suiting to the distributed storages via network and detecting the multiple users.
There is an efficient solution for deterring the illegal distribution and protecting the copyright of contents. That solution is using the DRM (Digital Rights Management) [4], [5], [8], [9] in online purchasing system. The DRM system consists of encryption and watermarking technique to manage the digital contents. The legal users only can use the digital content if only if they use special devices or special software to embed the copyright information into the digital contents before using it. However, the encryption and the watermarking technique inside DRM device/software are implemented separately. It raises a risk problem so that the original contents can be achieved between the encryption and watermarking process. That means the original content can be leakage inside the special devices/softwares. There- fore, the content verification and users verification cannot be detectable.
2017 4th NAFOSTED Conference on Information and Computer Science
(b) “9-patches” dispersion method
(c) “4-patches” dispersion method (a) Girl image
Figure 1. “9-patches” and “4-patches” of spatial dispersion methods.
B. Our contributions
In this paper, we propose a novel digital rights man- agement system based on the distributed redundant image information on cloud storages and suggest a systematic in- formation reconstruction algorithm for contents verification and users verification. The proposed method can control the number of information patches of digital images that will be dispersed to distributed storages. The digital contents are firstly dispersed to obtain the information patches and those will be saved in the distributed storage systems. We prepare numerous watermark logos to embed directly in to each information patch before dispersion on the cloud storages.
Note that the information patches can be overlapped each other.
When the original contents are needed, the information patches are collected from many distributed storage systems and are reconstructed to create the digital contents based on the user’s license provided by the producer. According to the license of the legal user, the appropriate information patches are collected. Therefore the reconstructed content is gen- erated with the watermark pattern to distinguish user each other. That means that there are multiple watermark pat- terns are embedded into multiple information patches before saving those on the distributed storages. After the content reconstruction, the high quality of the digital contents can be obtained and the copyright information (watermark pattern) is determined into the reconstructed contents based on the license number of users. Our solution ensures that the owner of the reconstructed contents can be detected correctly. In particular, we make the following contributions in this paper:
1) Dividing the digital content into many information patches to disperse it on online storages such as cloud system. Our solution gives a new idea for implement- ing of DRM system. It also can employ distributed
(a) Girl image (b) R-component
(c) G-component (d) B-component
Figure 2. RGB component dispersion methods
storages in order to managing the digital contents. That increases the efficiency of online data storage systems.
2) Our system embeds a logo into an information patch before distributing it on the online storages. The com- bination of information patches provides one pattern of combination watermark while reconstructing the contents to distinguish the users. Therefore, once the producer embeds the watermark into the information patches, he/she can sell the digital contents to multiple users by license and can detect the traitor exactly.
3) Proof the copyright of the individual users by using the license. The legal users can show the license number to producer when property dispute happens.
The producer can extract the watermark pattern from the reconstructed content and compare it with the watermark pattern based on the license number. If the watermark pattern matches, the user is legal.
Conversely, the user is illegal.
4) Since our proposed method can detect the owner of the reconstructed contents, it can limit the users to illegally redistribute the copyrighted contents via network.
This paper is organized as follows. The digital content dispersion methods are described in Section II. We also explain our proposed method by using the “n-patches”
dispersion method combining the watermarking technique in Section III. Our simulation results are shown in Section IV. Section V concludes our paper.
II. DIGITAL CONTENT DISPERSION METHODS
In order to distribute the information patches on the cloud system, we should survey various digital content dispersion methods. According to the number of distributed databases, 2017 4th NAFOSTED Conference on Information and Computer Science
978-1-5386-3210-9/17/$31.00 ©2017 IEEE 67
Figure 3. Object-based dispersion methods
the producer can separate the digital image into many information patches. Also, the size of information patches are needed to consider for using efficiently the resource of distributed storages.
1) Spatial dispersion methods: The most simple that can be considered first is spatial dispersion methods. The image Ican be separated into “n-patches” information. Each patch information I(i) then is distributed on the cloud storages, where i = 1· · ·n. In general, the producer decides to disperse the image based on the number of online storages that they have.
Fig. 1 shows an example of “n-patches” information dis- persion method. “9-patches” information method separates the Girl image into 9 patches of image (Fig. 1 (b)). “4- patches” information method separates the Girl image into 4 patches of image (Fig. 1 (c)). The size of each patch information should be controlled to fit the space of online storages.
2) Component dispersion methods: The digital image is normally constructed by some components color spaces such as RGB1, YUV2, YCbCr3, and so on. Based on the color space, the data structure of the digital image is changed.
However, according to the color space, the component of image can be separated to reduce the size of image and saved them in the online storages.
Fig. 2 describes how to separate the R-, G-, B-component of the normal image [6]. 8-bit image is separated from 24- bit image, therefore, the size of the component of image is reduced before redistributing on the online storages.
1https://en.wikipedia.org/wiki/RGB color model
2http://en.wikipedia.org/wiki/YUV
3https://en.wikipedia.org/wiki/YCbCr
3) Object-based dispersion methods: In the nature image, there are many objects inside the image. If we can fragment all objects separately, we can manage each object to dis- tribute on several storages.
For instance, Fig. 3 can be fragmented into several objects such as a tiger, grasses, ground, background, and the shadow of the tiger. Each object can be saved in the separate storages in the internet. When the digital image is needed, all objects are collected from those, then the image is reconstructed back.
Object-based dispersion method is quite complex method.
It requires time consuming and hard to implement.
III. PROPOSEDDISTRIBUTEDREDUNDANTIMAGE
STORAGES ANDRECONSTRUCTIONSYSTEM
In this section, we explain about the idea of distributed redundant image combination of watermark embedding and license based image reconstruction method. We employ the spatial dispersion method in order to fragment the digital images. “N-patches” spatial dispersion method is implemented. Before distributing,M patterns of watermark are embedded into each patch to create M patterns of information patches. By doing so, the producer can prepare N⇥M patches for managing the value digital images. The detailed steps of those processes are given as follows.
A. Redundant image storages combining watermark embed- ding
Suppose that the digital image I is used to sell to users. It is fragmented intoN patches information. Before embedding, we prepare M patterns of logomark W(i) as watermarks information, where i = 1,· · · , M. Fig. 4 2017 4th NAFOSTED Conference on Information and Computer Science
(a) Girl image I
(b) I(1)
(c) W(1) W(2) W(3) W(4)
(d) I’(1,1) I’(1,2) I’(1,3) I’(1,4)
(e) S(1) S(2) S(3) S(4)
Figure 4. Redundant image storages combining watermark embedding system.
describes the detailed steps in our proposed method. The fragmentation process is explained as follows:
–Step 1. Fragment the imageI into N patches ofI(j), wherej= 1,· · ·, N (See Fig. 4(b)).
–Step 2.All watermark patternsW(i)are embedded into each patchesI(j)by the embedding functionE(·)to create M patterns of each patch.
I0(j, i) =E(I(j), W(i)), i= 1,· · ·, M, j= 1,· · ·, N (1) I0(j, i) denotes that the watermark pattern W(i) is em- bedded into the output image I0(j) (See Fig. 4(d)). The watermark embedding method ofE(·)can be referred from paper [10], [11].
–Step 3. Each patch image I0(j, i) is distributed via network on the online storages S(i) (See Fig. 4(e)).
According to above process, we can fragment the digital image into some patches information then distribute it on the online storages in order to (i) efficiently use the free space of the online storages system, (ii) enhance the security of saving digital contents by distributed storages instead of central saving digital contents.
B. Reconstruction algorithm to contents verification The reconstruction algorithm is performed based on the license number of the legal users. According to the license
number, the appropriate patches information are collected from the distributed storages. Basic steps involved in the reconstruction algorithm, shown in Fig. 5, are given as follows:
–Step 1. In order to reconstruct the digital image, the producer generates the license L(k) for each user R(k) based on his/her information.
–Step 2.According to the licenseL, the patches informa- tionI0(j, i)are collected from S(i). Note that, the parame- ter (j, i) of patches information determines the logomark embedded into the patches information. For instance, in Fig. 5(b), the patches informationI0(1,2), I0(2,2), I0(3,1), andI0(4,3) are collected fromS(1), S(4), S(3),andS(2), respectively. That means the logomarks of those patches information areW(2), W(2), W(1),andW(3).
–Step 3.When the producer need to verify the users are whether legal or not, he uses the licenseL to confirm the watermark pattern PM
n=1W(i). Afterward, he can extract the watermark information from the patches information I0(j, i) to construct the watermark pattern PM
n=1W0(i).
ComparePM
n=1W(i)andPM
n=1W0(i), producer can judge the user.
C. Users verification
Suppose that, our system employs “N-patches” method to fragment the digital image I. We also prepare M of logomarks for embedding in each patch information. There- fore, the number of patches information that is generated to distribute on the online storages, isM ⇥N patches.
In order to sell the digital contents to users, the producer can reconstruct the digital contents by combination of all patterns of patches information. The number of users that can be distinguished by using our proposed method, isMN.
IV. EXPERIMENTAL RESULTS AND DISCUSSION
A. Experimental environment
For assessing the performance of the proposed algorithm, we conducted seven color and grayscale images of SIDBA (Standard Image Data-BAse) database4. All these test images are with size512⇥512pixels, and 4 logomarks used in our experiments are the binary image with size64⇥64which is shown in Fig. 4(c).
In order to evaluate the quality of watermarked images, we employ PSNR (Peak Signal to Noise Ratio) criterion [2].
The PSNR ofH⇥W pixels of imageI(l, m)andI0(l, m) is calculated with,
P SN R= 20 log 255
M SE [dB] (2)
M SE= vu ut 1
H⇥W
HX1
l=0 WX1
m=0
{I(l, m) I0(l, m)}2
(MSE : Mean Square Error).
4www.vision.kuee.kyoto-u.ac.jp/IUE/IMAGE DATABASE/STD IMAGES/
2017 4th NAFOSTED Conference on Information and Computer Science
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(b) I’(1,2)
(a) S(1) S(2) S(3) S(4)
I’(2,2) I’(3,1) I’(4,3)
(c) I’(j)
Watermark pattern extraction
(d)!'()*W’(i)
Figure 5. Reconstruction algorithm to contents verification system.
B. Experimental results
We implemented the “N-patches” dispersion method with N = 4. That means the experimental images are sepa- rated into four patches of image informations. Then four logomarks are embedded into each patch image in order to generate the embedded patches information. Afterward, the embedded patches information are distributed to four Dropbox servers.
To reconstruct the digital contents, we generate the license number L and collect four patches information from the Dropbox server. By doing so, we can reconstruct the digital contents with the watermark pattern inside based on the license numberL. We try with various of license number to confirm the efficiency of our proposed method. We confirm the quality of the reconstructed images and also try to extract the watermark patterns from those.
First of all, we try to confirm the dispersion method and reconstruction method by using the random license number L. Based on the license numberL, we reconstruct the digital images and compute the PSNRs values. We generate random ten license numberLand reconstruct the digital image based
Table I
PSNR[DB]AND WATERMARK PATTERNS OF RANDOM TEN LICENSE NUMBERL(GIRL IMAGE).
Image PSNR LicenseL Watermark pattern Girl 55.89 LICENSE142 3-3-2-2 Girl 55.84 LICENSE157 3-1-1-2 Girl 55.85 LICENSE100 1-2-3-4 Girl 55.79 LICENSE67 1-4-4-2 Girl 55.84 LICENSE17 3-4-4-2 Girl 55.82 LICENSE182 2-1-4-2 Girl 55.78 LICENSE2 4-4-1-1 Girl 55.87 LICENSE211 3-3-1-4 Girl 55.79 LICENSE141 3-1-4-1 Girl 55.78 LICENSE221 1-1-2-4
Table II
PSNR[DB]AND WATERMARK PATTERNS OF ALL EXPERIMENTAL IMAGES.
Image PSNR LicenseL Watermark pattern Girl 55.87 LICENSE136 3-3-2-4 Lena 55.90 LICENSE187 2-2-3-3 Baboon 55.93 LICENSE217 4-1-2-1 Barbara 55.91 LICENSE16 2-2-2-2 Goldhill 55.92 LICENSE156 2-4-1-2 Couple 55.84 LICENSE6 4-4-2-2 Peppers 55.89 LICENSE164 1-2-4-2
onL. The experimental results are shown in Table I.
According to Table I, we recognize that the quality of the reconstructed images is over than 55dB. That means the reconstructed images are good for sell. Additionally, the watermark patterns are successfully extracted from the reconstructed images to distinguish the legal users. For example, the watermark pattern “3-3-2-2” means the combi- nation of the logomarks (W(3), W(3), W(2),andW(2)(see Fig. 4)(c)) including the reconstructed image.
We also try to test with seven images of the test database.
The results are shown in Table II. The results in Table II shows that our proposed method is suitable for image distribution system. We can easily manage to separate the digital image and save the patches information on the online storage (e.g Dropbox servers). According to the extracted watermark patterns, our system also can distinguish the users. Therefore, the proposed method can limit the illegally redistribution of the reconstructed contents beforehand.
Fig. 6 shows our experimental results of Table II. The experimental results show that the reconstructed images are excellent images with high quality. In additional, the ex- tracted watermark patterns are visible to detect the copyright information. It also shows the efficiency of our proposed sys- tem for new idea of DRM system employing the distributed online storages.
C. Discussions
Our proposed method shows a new idea to implement the digital right management system. The combination of the dispersion technique and the watermarking method gives 2017 4th NAFOSTED Conference on Information and Computer Science
Girl, 55.87 dB
Lena, 55.90 dB
Baboon, 55.93 dB
Barbara, 55.91 dB
Goldhill, 55.92 dB
Couple, 55.84 dB
Peppers, 55.89 dB 3-3-2-4
2-2-3-3
4-1-2-1
2-2-2-2
2-4-1-2
4-4-2-2
1-2-4-2
Figure 6. Some samples of our proposed results.
us an efficient way to embed the watermark including users distinction. Our proposed watermarking method is difference from the normal watermarking methods. Once, the logomarks are embedded into the patches information, the logomarks are used to combine to make various wa- termark patterns for many legal users. On the other hand, in the conventional methods [11], [2], after embedding the watermark into the digital images, only a legal user is specified.
In addition, in this paper, we do not discuss about the watermark embedding technique. In fact, the producer can employs various watermarking techniques for implementing our proposed system. That makes our system more flexible for DRM system.
V. CONCLUSION
We have proposed a novel distributed redundant image storages and reconstruction algorithm to contents verifi- cation and users authentication system, which is not yet proposed for watermarking field before. The digital images are separated and distributed on the online storages after embedding the logomarks inside the patches information.
The watermark patterns are generated based on the license number sold to the legal users. Those can be used for specify the legal users. Therefore, our proposed method
also can limit the illegal distribution. According to our experimental results, the embedded watermark patterns can be successfully extracted and distinguished each others.
In the future works, we try to apply the proposed method on another digital format such as digital audio, video, and so on.
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