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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: Jul. 04, 2020.
@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

Approaching Optimal Embedding in Audio Steganography with GAN


Audio steganography is a technology that embeds messages into audio without raising any suspicion from hearing it. Current steganography methods are based on heuristic cost designs. In this work, we proposed a framework based on Generative Adversarial Network (GAN) to approach optimal embedding for audio steganography in the temporal domain. This is the first attempt to approach optimal embedding with GAN and automatically learn the embedding probability/cost for audio steganography.

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Authors:
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi
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28 May 2020 - 10:39pm
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[1] Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi, "Approaching Optimal Embedding in Audio Steganography with GAN", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5445. Accessed: Jul. 04, 2020.
@article{5445-20,
url = {http://sigport.org/5445},
author = {Jianhua Yang; Huilin Zheng; Xiangui Kang; Yun-Qing Shi },
publisher = {IEEE SigPort},
title = {Approaching Optimal Embedding in Audio Steganography with GAN},
year = {2020} }
TY - EJOUR
T1 - Approaching Optimal Embedding in Audio Steganography with GAN
AU - Jianhua Yang; Huilin Zheng; Xiangui Kang; Yun-Qing Shi
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5445
ER -
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. (2020). Approaching Optimal Embedding in Audio Steganography with GAN. IEEE SigPort. http://sigport.org/5445
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi, 2020. Approaching Optimal Embedding in Audio Steganography with GAN. Available at: http://sigport.org/5445.
Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. (2020). "Approaching Optimal Embedding in Audio Steganography with GAN." Web.
1. Jianhua Yang, Huilin Zheng, Xiangui Kang, Yun-Qing Shi. Approaching Optimal Embedding in Audio Steganography with GAN [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5445

Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer


Many portable imaging devices use the operation of “trunc” (rounding towards zero) instead of rounding as the final quantizer for computing DCT coefficients during JPEG compression. We show that this has rather profound consequences for steganography and its detection. In particular, side-informed steganography needs to be redesigned due to the different nature of the rounding error. The steganographic algorithm J-UNIWARD becomes vulnerable to steganalysis with the JPEG rich model and needs to be adjusted for this source.

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Jan Butora, Jessica Fridrich
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13 May 2020 - 5:02pm
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[1] Jan Butora, Jessica Fridrich, "Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5143. Accessed: Jul. 04, 2020.
@article{5143-20,
url = {http://sigport.org/5143},
author = {Jan Butora; Jessica Fridrich },
publisher = {IEEE SigPort},
title = {Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer},
year = {2020} }
TY - EJOUR
T1 - Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer
AU - Jan Butora; Jessica Fridrich
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5143
ER -
Jan Butora, Jessica Fridrich. (2020). Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer. IEEE SigPort. http://sigport.org/5143
Jan Butora, Jessica Fridrich, 2020. Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer. Available at: http://sigport.org/5143.
Jan Butora, Jessica Fridrich. (2020). "Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer." Web.
1. Jan Butora, Jessica Fridrich. Steganography and its Detection in JPEG Images Obtained with the “Trunc” Quantizer [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5143

MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY


The widespread application of audio communication technologies has speeded up audio data flowing across the Internet, which made it an popular carrier for covert communication. In this paper, we present a cross-modal steganography method for hiding image content into audio carriers while preserving the perceptual fidelity of the cover audio.

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Authors:
Wenxue Cui, Shaohui Liu, Feng Jiang, Yongliang Liu, Debin Zhao
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13 February 2020 - 7:23am
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Poster-MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY

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[1] Wenxue Cui, Shaohui Liu, Feng Jiang, Yongliang Liu, Debin Zhao, "MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/4986. Accessed: Jul. 04, 2020.
@article{4986-20,
url = {http://sigport.org/4986},
author = {Wenxue Cui; Shaohui Liu; Feng Jiang; Yongliang Liu; Debin Zhao },
publisher = {IEEE SigPort},
title = {MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY},
year = {2020} }
TY - EJOUR
T1 - MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY
AU - Wenxue Cui; Shaohui Liu; Feng Jiang; Yongliang Liu; Debin Zhao
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/4986
ER -
Wenxue Cui, Shaohui Liu, Feng Jiang, Yongliang Liu, Debin Zhao. (2020). MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY. IEEE SigPort. http://sigport.org/4986
Wenxue Cui, Shaohui Liu, Feng Jiang, Yongliang Liu, Debin Zhao, 2020. MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY. Available at: http://sigport.org/4986.
Wenxue Cui, Shaohui Liu, Feng Jiang, Yongliang Liu, Debin Zhao. (2020). "MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY." Web.
1. Wenxue Cui, Shaohui Liu, Feng Jiang, Yongliang Liu, Debin Zhao. MULTI-STAGE RESIDUAL HIDING FOR IMAGE-INTO-AUDIO STEGANOGRAPHY [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/4986

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: Jul. 04, 2020.
@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: Jul. 04, 2020.
@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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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: Jul. 04, 2020.
@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
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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: Jul. 04, 2020.
@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: Jul. 04, 2020.
@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: Jul. 04, 2020.
@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

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