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Watermarking and Steganography

Introduction to Digital Watermarking - Lecture Slides


Lecture notes for undergraduate and first-year graduate students on digital watermarking and data embedding in multimedia data.

Based on lectures developed at University of Maryland, College Park, USA.

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23 February 2016 - 1:43pm
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[1] , "Introduction to Digital Watermarking - Lecture Slides", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/133. Accessed: May. 21, 2019.
@article{133-15,
url = {http://sigport.org/133},
author = { },
publisher = {IEEE SigPort},
title = {Introduction to Digital Watermarking - Lecture Slides},
year = {2015} }
TY - EJOUR
T1 - Introduction to Digital Watermarking - Lecture Slides
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/133
ER -
. (2015). Introduction to Digital Watermarking - Lecture Slides. IEEE SigPort. http://sigport.org/133
, 2015. Introduction to Digital Watermarking - Lecture Slides. Available at: http://sigport.org/133.
. (2015). "Introduction to Digital Watermarking - Lecture Slides." Web.
1. . Introduction to Digital Watermarking - Lecture Slides [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/133

A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model


Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients.

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Authors:
Xinghong Qin, Bin Li, Jiwu Huang
Submitted On:
10 May 2019 - 2:04pm
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[1] Xinghong Qin, Bin Li, Jiwu Huang, "A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4383. Accessed: May. 21, 2019.
@article{4383-19,
url = {http://sigport.org/4383},
author = {Xinghong Qin; Bin Li; Jiwu Huang },
publisher = {IEEE SigPort},
title = {A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model},
year = {2019} }
TY - EJOUR
T1 - A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model
AU - Xinghong Qin; Bin Li; Jiwu Huang
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4383
ER -
Xinghong Qin, Bin Li, Jiwu Huang. (2019). A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model. IEEE SigPort. http://sigport.org/4383
Xinghong Qin, Bin Li, Jiwu Huang, 2019. A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model. Available at: http://sigport.org/4383.
Xinghong Qin, Bin Li, Jiwu Huang. (2019). "A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model." Web.
1. Xinghong Qin, Bin Li, Jiwu Huang. A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4383

Graph Spectral Domain Blind Watermarking


This paper proposes the first ever graph spectral domain blind watermarking algorithm. We explore the recently developed graph signal processing for spread-spectrum watermarking to authenticate the data recorded on non-Cartesian grids, such as sensor data, 3D point clouds, Lidar scans and mesh data. The choice of coefficients for embedding the watermark is driven by the model for minimisation embedding distortion and the robustness model.

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Authors:
Hiba Al-Khafaji and Charith Abhayaratne
Submitted On:
10 May 2019 - 11:04am
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Poster at ICASSP 2019

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[1] Hiba Al-Khafaji and Charith Abhayaratne, "Graph Spectral Domain Blind Watermarking", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4358. Accessed: May. 21, 2019.
@article{4358-19,
url = {http://sigport.org/4358},
author = {Hiba Al-Khafaji and Charith Abhayaratne },
publisher = {IEEE SigPort},
title = {Graph Spectral Domain Blind Watermarking},
year = {2019} }
TY - EJOUR
T1 - Graph Spectral Domain Blind Watermarking
AU - Hiba Al-Khafaji and Charith Abhayaratne
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4358
ER -
Hiba Al-Khafaji and Charith Abhayaratne. (2019). Graph Spectral Domain Blind Watermarking. IEEE SigPort. http://sigport.org/4358
Hiba Al-Khafaji and Charith Abhayaratne, 2019. Graph Spectral Domain Blind Watermarking. Available at: http://sigport.org/4358.
Hiba Al-Khafaji and Charith Abhayaratne. (2019). "Graph Spectral Domain Blind Watermarking." Web.
1. Hiba Al-Khafaji and Charith Abhayaratne. Graph Spectral Domain Blind Watermarking [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4358

MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING


Recently pixel pairing and pixel sorting/selection have been used in prediction-error expansion based reversible data hiding schemes to generate low entropy prediction-error histograms (PEH) necessary for achieving high fidelity. Such schemes generally use the four-neighbor average rhombus predictor as it allows pixel sorting and flexible pixel pairing. In this paper, we propose the maximally separated averages (MSA) predictor that uses the four-neighborhood context.

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Authors:
Dibakar Hazarika, Sobhan Kanti Dhara, Debashis Sen
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10 May 2019 - 5:23am
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[1] Dibakar Hazarika, Sobhan Kanti Dhara, Debashis Sen, "MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4290. Accessed: May. 21, 2019.
@article{4290-19,
url = {http://sigport.org/4290},
author = {Dibakar Hazarika; Sobhan Kanti Dhara; Debashis Sen },
publisher = {IEEE SigPort},
title = {MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING},
year = {2019} }
TY - EJOUR
T1 - MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING
AU - Dibakar Hazarika; Sobhan Kanti Dhara; Debashis Sen
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4290
ER -
Dibakar Hazarika, Sobhan Kanti Dhara, Debashis Sen. (2019). MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING. IEEE SigPort. http://sigport.org/4290
Dibakar Hazarika, Sobhan Kanti Dhara, Debashis Sen, 2019. MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING. Available at: http://sigport.org/4290.
Dibakar Hazarika, Sobhan Kanti Dhara, Debashis Sen. (2019). "MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING." Web.
1. Dibakar Hazarika, Sobhan Kanti Dhara, Debashis Sen. MAXIMALLY SEPARATED AVERAGES PREDICTION FOR HIGH FIDELITY REVERSIBLE DATA HIDING [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4290

Attacks On Digital Watermarks For Deep Neural Networks


Training deep neural networks is a computationally expensive task. Furthermore, models are often derived from proprietary datasets that have been carefully prepared and labelled. Hence, creators of deep learning models want to protect their models against intellectual property theft. However, this is not always possible, since the model may, e.g., be embedded in a mobile app for fast response times. As a countermeasure watermarks for deep neural networks have been developed that embed secret information into the model.

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Authors:
Tianhao Wang, Florian Kerschbaum
Submitted On:
9 May 2019 - 9:11pm
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Poster Presentation

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[1] Tianhao Wang, Florian Kerschbaum, "Attacks On Digital Watermarks For Deep Neural Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4249. Accessed: May. 21, 2019.
@article{4249-19,
url = {http://sigport.org/4249},
author = {Tianhao Wang; Florian Kerschbaum },
publisher = {IEEE SigPort},
title = {Attacks On Digital Watermarks For Deep Neural Networks},
year = {2019} }
TY - EJOUR
T1 - Attacks On Digital Watermarks For Deep Neural Networks
AU - Tianhao Wang; Florian Kerschbaum
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4249
ER -
Tianhao Wang, Florian Kerschbaum. (2019). Attacks On Digital Watermarks For Deep Neural Networks. IEEE SigPort. http://sigport.org/4249
Tianhao Wang, Florian Kerschbaum, 2019. Attacks On Digital Watermarks For Deep Neural Networks. Available at: http://sigport.org/4249.
Tianhao Wang, Florian Kerschbaum. (2019). "Attacks On Digital Watermarks For Deep Neural Networks." Web.
1. Tianhao Wang, Florian Kerschbaum. Attacks On Digital Watermarks For Deep Neural Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4249

ICASSP 2019 Poster


The method reported here realizes an inaudible echo-hiding based speech watermarking by using sparse subspace clustering (SSC). Speech signal is first analyzed with SSC to obtain its sparse and low-rank components. Watermarks are embedded as the echoes of the sparse component for robust extraction. Self-compensated echoes consisting of two independent echo kernels are designed to have similar delay offsets but opposite amplitudes. A one-bit watermark is embedded by separately performing the echo kernels on the sparse and low-rank components.

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Authors:
Shengbei Wang,Weitao Yuan, Jianming Wang, Masashi Unoki
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7 May 2019 - 9:56pm
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[1] Shengbei Wang,Weitao Yuan, Jianming Wang, Masashi Unoki, "ICASSP 2019 Poster", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3986. Accessed: May. 21, 2019.
@article{3986-19,
url = {http://sigport.org/3986},
author = {Shengbei Wang,Weitao Yuan; Jianming Wang; Masashi Unoki },
publisher = {IEEE SigPort},
title = {ICASSP 2019 Poster},
year = {2019} }
TY - EJOUR
T1 - ICASSP 2019 Poster
AU - Shengbei Wang,Weitao Yuan; Jianming Wang; Masashi Unoki
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3986
ER -
Shengbei Wang,Weitao Yuan, Jianming Wang, Masashi Unoki. (2019). ICASSP 2019 Poster. IEEE SigPort. http://sigport.org/3986
Shengbei Wang,Weitao Yuan, Jianming Wang, Masashi Unoki, 2019. ICASSP 2019 Poster. Available at: http://sigport.org/3986.
Shengbei Wang,Weitao Yuan, Jianming Wang, Masashi Unoki. (2019). "ICASSP 2019 Poster." Web.
1. Shengbei Wang,Weitao Yuan, Jianming Wang, Masashi Unoki. ICASSP 2019 Poster [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3986

RHFCN: Fully CNN-based Steganalysis of MP3 with Rich High-Pass Filtering


Recent studies have shown that convolutional neural networks (CNNs) can boost the performance of audio steganalysis. In this paper, we propose a well-designed fully CNN architecture for MP3 steganalysis based on rich high-pass filtering (HPF). On the one hand, multi-type HPFs are employed for "residual" extraction to enlarge the traces of the signal in view of the truth that signal introduced by secret messages can be seen as high-pass frequency noise.

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Authors:
Yuntao Wang, Xiaowei Yi, Xianfeng Zhao, Ante Su
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7 May 2019 - 9:07pm
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[1] Yuntao Wang, Xiaowei Yi, Xianfeng Zhao, Ante Su, "RHFCN: Fully CNN-based Steganalysis of MP3 with Rich High-Pass Filtering", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3976. Accessed: May. 21, 2019.
@article{3976-19,
url = {http://sigport.org/3976},
author = {Yuntao Wang; Xiaowei Yi; Xianfeng Zhao; Ante Su },
publisher = {IEEE SigPort},
title = {RHFCN: Fully CNN-based Steganalysis of MP3 with Rich High-Pass Filtering},
year = {2019} }
TY - EJOUR
T1 - RHFCN: Fully CNN-based Steganalysis of MP3 with Rich High-Pass Filtering
AU - Yuntao Wang; Xiaowei Yi; Xianfeng Zhao; Ante Su
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3976
ER -
Yuntao Wang, Xiaowei Yi, Xianfeng Zhao, Ante Su. (2019). RHFCN: Fully CNN-based Steganalysis of MP3 with Rich High-Pass Filtering. IEEE SigPort. http://sigport.org/3976
Yuntao Wang, Xiaowei Yi, Xianfeng Zhao, Ante Su, 2019. RHFCN: Fully CNN-based Steganalysis of MP3 with Rich High-Pass Filtering. Available at: http://sigport.org/3976.
Yuntao Wang, Xiaowei Yi, Xianfeng Zhao, Ante Su. (2019). "RHFCN: Fully CNN-based Steganalysis of MP3 with Rich High-Pass Filtering." Web.
1. Yuntao Wang, Xiaowei Yi, Xianfeng Zhao, Ante Su. RHFCN: Fully CNN-based Steganalysis of MP3 with Rich High-Pass Filtering [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3976

UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO

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Authors:
Baolin Zhu,Jiangqun Ni
Submitted On:
7 October 2018 - 4:32pm
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[1] Baolin Zhu,Jiangqun Ni, "UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3601. Accessed: May. 21, 2019.
@article{3601-18,
url = {http://sigport.org/3601},
author = {Baolin Zhu;Jiangqun Ni },
publisher = {IEEE SigPort},
title = {UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO},
year = {2018} }
TY - EJOUR
T1 - UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO
AU - Baolin Zhu;Jiangqun Ni
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3601
ER -
Baolin Zhu,Jiangqun Ni. (2018). UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO. IEEE SigPort. http://sigport.org/3601
Baolin Zhu,Jiangqun Ni, 2018. UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO. Available at: http://sigport.org/3601.
Baolin Zhu,Jiangqun Ni. (2018). "UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO." Web.
1. Baolin Zhu,Jiangqun Ni. UNIFORM EMBEDDING FOR EFFICIENT STEGANOGRAPHY OF H.264 VIDEO [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3601

IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING


Pixel-value-ordering (PVO) appears as an efficient technique for high-fidelity reversible data hiding. This paper proposes a reversible data hiding scheme based on the pairwise PVO framework with improved difference equations. Both the pixel pair selection and the embedding algorithms are also streamlined. The proposed scheme uses a block classification approach based on a local complexity metric. Uniform blocks are processed using the proposed improved pairwise PVO algorithm. Slightly noisy blocks are embedded using a classic PVO scheme and noisy blocks are kept unchanged.

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Authors:
Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc
Submitted On:
6 October 2018 - 8:51am
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[1] Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc, "IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3576. Accessed: May. 21, 2019.
@article{3576-18,
url = {http://sigport.org/3576},
author = {Ioan-Catalin Dragoi; Ion Caciula; Dinu Coltuc },
publisher = {IEEE SigPort},
title = {IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING},
year = {2018} }
TY - EJOUR
T1 - IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING
AU - Ioan-Catalin Dragoi; Ion Caciula; Dinu Coltuc
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3576
ER -
Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc. (2018). IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING. IEEE SigPort. http://sigport.org/3576
Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc, 2018. IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING. Available at: http://sigport.org/3576.
Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc. (2018). "IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING." Web.
1. Ioan-Catalin Dragoi, Ion Caciula, Dinu Coltuc. IMPROVED PAIRWISE PIXEL-VALUE-ORDERING FOR HIGH-FIDELITY REVERSIBLE DATA HIDING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3576

REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION


This paper proposes a new vacating room after encryption reversible data hiding scheme developed for color images. The proposed scheme uses standard exclusive-or encryption and inherits the main features of vacating room after encryption schemes, namely joint and separate methods for data embedding. The proposed scheme exploits both the correlation between neighboring pixels and the correlation between color channels by predicting the original pixel values on color channel differences.

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Authors:
Ioan-Catalin Dragoi, Dinu Coltuc
Submitted On:
6 October 2018 - 8:44am
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ICIP2018_CriptColor.pdf

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[1] Ioan-Catalin Dragoi, Dinu Coltuc, "REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3574. Accessed: May. 21, 2019.
@article{3574-18,
url = {http://sigport.org/3574},
author = {Ioan-Catalin Dragoi; Dinu Coltuc },
publisher = {IEEE SigPort},
title = {REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION},
year = {2018} }
TY - EJOUR
T1 - REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION
AU - Ioan-Catalin Dragoi; Dinu Coltuc
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3574
ER -
Ioan-Catalin Dragoi, Dinu Coltuc. (2018). REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION. IEEE SigPort. http://sigport.org/3574
Ioan-Catalin Dragoi, Dinu Coltuc, 2018. REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION. Available at: http://sigport.org/3574.
Ioan-Catalin Dragoi, Dinu Coltuc. (2018). "REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION." Web.
1. Ioan-Catalin Dragoi, Dinu Coltuc. REVERSIBLE DATA HIDING IN ENCRYPTED COLOR IMAGES BASED ON VACATING ROOM AFTER ENCRYPTION AND PIXEL PREDICTION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3574

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