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Information Forensics and Security

Protect Your Deep Neural Networks from Piracy


Building an effective DNN model requires massive human-labeled training data, powerful computing hardware and researchers' skills and efforts. Successful DNN models are becoming important intellectual properties for the model owners and should be protected from unauthorized access and piracy. This paper proposes a novel framework to provide access control to the trained deep neural networks so that only authorized users can utilize them properly.

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Authors:
Mingliang Chen, Min Wu
Submitted On:
5 February 2019 - 11:23am
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wifs18_dnn_piracy.pdf

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[1] Mingliang Chen, Min Wu, "Protect Your Deep Neural Networks from Piracy", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3848. Accessed: Mar. 19, 2019.
@article{3848-19,
url = {http://sigport.org/3848},
author = {Mingliang Chen; Min Wu },
publisher = {IEEE SigPort},
title = {Protect Your Deep Neural Networks from Piracy},
year = {2019} }
TY - EJOUR
T1 - Protect Your Deep Neural Networks from Piracy
AU - Mingliang Chen; Min Wu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3848
ER -
Mingliang Chen, Min Wu. (2019). Protect Your Deep Neural Networks from Piracy. IEEE SigPort. http://sigport.org/3848
Mingliang Chen, Min Wu, 2019. Protect Your Deep Neural Networks from Piracy. Available at: http://sigport.org/3848.
Mingliang Chen, Min Wu. (2019). "Protect Your Deep Neural Networks from Piracy." Web.
1. Mingliang Chen, Min Wu. Protect Your Deep Neural Networks from Piracy [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3848

Factors Affecting ENF Capture in Audio


The electric network frequency (ENF) signal is an environmental signature that can be captured in audiovisual recordings made in locations where there is electrical activity. This signal is influenced by the power grid in which the recording is made, and recent work has shown that it can be useful toward a number of forensics and security applications. An under-studied area of ENF research is the factors that can affect the capture of ENF traces in media recordings.

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Authors:
Steven Gambino, Miao Yu
Submitted On:
7 January 2019 - 5:06pm
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Poster for TIFS journal paper

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[1] Steven Gambino, Miao Yu, "Factors Affecting ENF Capture in Audio", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3844. Accessed: Mar. 19, 2019.
@article{3844-19,
url = {http://sigport.org/3844},
author = {Steven Gambino; Miao Yu },
publisher = {IEEE SigPort},
title = {Factors Affecting ENF Capture in Audio},
year = {2019} }
TY - EJOUR
T1 - Factors Affecting ENF Capture in Audio
AU - Steven Gambino; Miao Yu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3844
ER -
Steven Gambino, Miao Yu. (2019). Factors Affecting ENF Capture in Audio. IEEE SigPort. http://sigport.org/3844
Steven Gambino, Miao Yu, 2019. Factors Affecting ENF Capture in Audio. Available at: http://sigport.org/3844.
Steven Gambino, Miao Yu. (2019). "Factors Affecting ENF Capture in Audio." Web.
1. Steven Gambino, Miao Yu. Factors Affecting ENF Capture in Audio [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3844

Enhanced Geometric Reflection Models for Paper Surface Based Authentication


Paper under the microscopic view has a rough surface formed by intertwisted wood fibers. Such roughness is unique on a specific location of the paper and is almost impossible to duplicate. Previous work has shown that commodity scanners and cameras are capable of capturing such intrinsic roughness in term of surface normal vectors for security and forensics applications. In this paper, we examine several candidate mathematical models for camera captured images of paper surfaces and compare the modeling accuracies with reference to the measurement by the confocal microscopy.

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Authors:
Runze Liu
Submitted On:
3 February 2019 - 5:14pm
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wifs2018_final.pdf

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[1] Runze Liu, "Enhanced Geometric Reflection Models for Paper Surface Based Authentication", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3843. Accessed: Mar. 19, 2019.
@article{3843-19,
url = {http://sigport.org/3843},
author = {Runze Liu },
publisher = {IEEE SigPort},
title = {Enhanced Geometric Reflection Models for Paper Surface Based Authentication},
year = {2019} }
TY - EJOUR
T1 - Enhanced Geometric Reflection Models for Paper Surface Based Authentication
AU - Runze Liu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3843
ER -
Runze Liu. (2019). Enhanced Geometric Reflection Models for Paper Surface Based Authentication. IEEE SigPort. http://sigport.org/3843
Runze Liu, 2019. Enhanced Geometric Reflection Models for Paper Surface Based Authentication. Available at: http://sigport.org/3843.
Runze Liu. (2019). "Enhanced Geometric Reflection Models for Paper Surface Based Authentication." Web.
1. Runze Liu. Enhanced Geometric Reflection Models for Paper Surface Based Authentication [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3843

Security in the Internet of Things: Information Theoretic Insights


The emerging Internet of Things (IoT) has several salient characteristics that differentiate it from existing wireless networking architectures. These include the deployment of very large numbers of (possibly) low-complexity terminals; the need for low-latency, short-packet communications (e.g., to support automation); light or no infrastructure; and primary applications of data gathering, inference and control.

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Submitted On:
30 November 2018 - 6:01pm
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globalsip18.pdf

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[1] , "Security in the Internet of Things: Information Theoretic Insights", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3836. Accessed: Mar. 19, 2019.
@article{3836-18,
url = {http://sigport.org/3836},
author = { },
publisher = {IEEE SigPort},
title = {Security in the Internet of Things: Information Theoretic Insights},
year = {2018} }
TY - EJOUR
T1 - Security in the Internet of Things: Information Theoretic Insights
AU -
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3836
ER -
. (2018). Security in the Internet of Things: Information Theoretic Insights. IEEE SigPort. http://sigport.org/3836
, 2018. Security in the Internet of Things: Information Theoretic Insights. Available at: http://sigport.org/3836.
. (2018). "Security in the Internet of Things: Information Theoretic Insights." Web.
1. . Security in the Internet of Things: Information Theoretic Insights [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3836

REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BASED ON RESERVING ROOM AFTER ENCRYPTION AND MULTIPLE PREDICTORS


This paper proposes a new vacating room after encryption reversible data hiding scheme. Hidden data is embedded into the encrypted host image by bit-flipping a preselected bitplane of a randomly formed pixel group. The major novelty of the paper is the use of multiple predictors in an adaptive procedure for detecting between original and modified pixels. Four predictors are used on a context of four neighbors, namely the average of the four pixels, a weighted average based on local gradients, the median and the midpoint. Experimental results are provided.

Paper Details

Authors:
Ioan-Catalin Dragoi, Dinu Coltuc
Submitted On:
13 April 2018 - 11:34am
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Dragoi_Coltuc_ICASSP2018.pdf

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[1] Ioan-Catalin Dragoi, Dinu Coltuc, "REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BASED ON RESERVING ROOM AFTER ENCRYPTION AND MULTIPLE PREDICTORS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2719. Accessed: Mar. 19, 2019.
@article{2719-18,
url = {http://sigport.org/2719},
author = {Ioan-Catalin Dragoi; Dinu Coltuc },
publisher = {IEEE SigPort},
title = {REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BASED ON RESERVING ROOM AFTER ENCRYPTION AND MULTIPLE PREDICTORS},
year = {2018} }
TY - EJOUR
T1 - REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BASED ON RESERVING ROOM AFTER ENCRYPTION AND MULTIPLE PREDICTORS
AU - Ioan-Catalin Dragoi; Dinu Coltuc
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2719
ER -
Ioan-Catalin Dragoi, Dinu Coltuc. (2018). REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BASED ON RESERVING ROOM AFTER ENCRYPTION AND MULTIPLE PREDICTORS. IEEE SigPort. http://sigport.org/2719
Ioan-Catalin Dragoi, Dinu Coltuc, 2018. REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BASED ON RESERVING ROOM AFTER ENCRYPTION AND MULTIPLE PREDICTORS. Available at: http://sigport.org/2719.
Ioan-Catalin Dragoi, Dinu Coltuc. (2018). "REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BASED ON RESERVING ROOM AFTER ENCRYPTION AND MULTIPLE PREDICTORS." Web.
1. Ioan-Catalin Dragoi, Dinu Coltuc. REVERSIBLE DATA HIDING IN ENCRYPTED IMAGES BASED ON RESERVING ROOM AFTER ENCRYPTION AND MULTIPLE PREDICTORS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2719

2D vector map reversible data hiding with topological relation preservation

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Authors:
Nana Wang, Xiangjun Zhao
Submitted On:
12 April 2018 - 9:51pm
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2D Vector Map Reversible Data Hiding with Topological .pptx

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[1] Nana Wang, Xiangjun Zhao, "2D vector map reversible data hiding with topological relation preservation", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2545. Accessed: Mar. 19, 2019.
@article{2545-18,
url = {http://sigport.org/2545},
author = {Nana Wang; Xiangjun Zhao },
publisher = {IEEE SigPort},
title = {2D vector map reversible data hiding with topological relation preservation},
year = {2018} }
TY - EJOUR
T1 - 2D vector map reversible data hiding with topological relation preservation
AU - Nana Wang; Xiangjun Zhao
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2545
ER -
Nana Wang, Xiangjun Zhao. (2018). 2D vector map reversible data hiding with topological relation preservation. IEEE SigPort. http://sigport.org/2545
Nana Wang, Xiangjun Zhao, 2018. 2D vector map reversible data hiding with topological relation preservation. Available at: http://sigport.org/2545.
Nana Wang, Xiangjun Zhao. (2018). "2D vector map reversible data hiding with topological relation preservation." Web.
1. Nana Wang, Xiangjun Zhao. 2D vector map reversible data hiding with topological relation preservation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2545

Making Likelihood Ratios Digestible for Cross-Application Performance Assessment


Performance estimation is crucial to the assessment of novel algorithms and systems. In detection error trade-off (DET) diagrams, discrimination performance is solely assessed targeting one application, where cross-application performance considers risks resulting from decisions, depending on application constraints. For the purpose of interchangeability of research results across different application constraints, we propose to augment DET curves by depicting systems regarding their support of security and convenience levels.

Paper Details

Authors:
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch
Submitted On:
12 April 2018 - 1:11pm
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poster.pdf

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[1] Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch, "Making Likelihood Ratios Digestible for Cross-Application Performance Assessment", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2447. Accessed: Mar. 19, 2019.
@article{2447-18,
url = {http://sigport.org/2447},
author = {Andreas Nautsch; Didier Meuwly; Daniel Ramos; Jonas Lindh; Christoph Busch },
publisher = {IEEE SigPort},
title = {Making Likelihood Ratios Digestible for Cross-Application Performance Assessment},
year = {2018} }
TY - EJOUR
T1 - Making Likelihood Ratios Digestible for Cross-Application Performance Assessment
AU - Andreas Nautsch; Didier Meuwly; Daniel Ramos; Jonas Lindh; Christoph Busch
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2447
ER -
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. (2018). Making Likelihood Ratios Digestible for Cross-Application Performance Assessment. IEEE SigPort. http://sigport.org/2447
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch, 2018. Making Likelihood Ratios Digestible for Cross-Application Performance Assessment. Available at: http://sigport.org/2447.
Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. (2018). "Making Likelihood Ratios Digestible for Cross-Application Performance Assessment." Web.
1. Andreas Nautsch, Didier Meuwly, Daniel Ramos, Jonas Lindh, Christoph Busch. Making Likelihood Ratios Digestible for Cross-Application Performance Assessment [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2447

Keynote: Physical Security is from Mars, Cybersecurity is from Venus


Corporations are dealing with the traditions of managing security in silos, leading to an increased number of attacks on things like critical infrastructure. Corporate (Physical) Security deals with facilities and building access, whereas IT Security is dealing with cybersecurity as well as system and network access. It’s as if they are living on different planets!

Paper Details

Authors:
Brian Harrell
Submitted On:
15 November 2017 - 8:38pm
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KeyNote Talk.pdf

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[1] Brian Harrell, "Keynote: Physical Security is from Mars, Cybersecurity is from Venus ", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2359. Accessed: Mar. 19, 2019.
@article{2359-17,
url = {http://sigport.org/2359},
author = {Brian Harrell },
publisher = {IEEE SigPort},
title = {Keynote: Physical Security is from Mars, Cybersecurity is from Venus },
year = {2017} }
TY - EJOUR
T1 - Keynote: Physical Security is from Mars, Cybersecurity is from Venus
AU - Brian Harrell
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2359
ER -
Brian Harrell. (2017). Keynote: Physical Security is from Mars, Cybersecurity is from Venus . IEEE SigPort. http://sigport.org/2359
Brian Harrell, 2017. Keynote: Physical Security is from Mars, Cybersecurity is from Venus . Available at: http://sigport.org/2359.
Brian Harrell. (2017). "Keynote: Physical Security is from Mars, Cybersecurity is from Venus ." Web.
1. Brian Harrell. Keynote: Physical Security is from Mars, Cybersecurity is from Venus [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2359

Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics


Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics

In this paper, we present Discriminant Correlation Analysis (DCA), a feature level fusion technique that incorporates the class associations in correlation analysis of the feature sets. DCA performs an effective feature fusion by maximizing the pair-wise correlations across the two feature sets, and at the same time, eliminating the between-class correlations and restricting the correlations to be within classes.

[1] Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/828. Accessed: Mar. 19, 2019.
@article{828-16,
url = {http://sigport.org/828},
author = {Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi },
publisher = {IEEE SigPort},
title = {Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics},
year = {2016} }
TY - EJOUR
T1 - Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics
AU - Mohammad Haghighat; Mohamed Abdel-Mottaleb; Wadee Alhalabi
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/828
ER -
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. IEEE SigPort. http://sigport.org/828
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi, 2016. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics. Available at: http://sigport.org/828.
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. (2016). "Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics." Web.
1. Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi. Discriminant Correlation Analysis for Feature Level Fusion with Application to Multimodal Biometrics [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/828

Cyber-Physical Intrusion Detection on a Robotic Vehicle

Paper Details

Authors:
Tuan Vuong
Submitted On:
23 February 2016 - 1:44pm
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WIFS2015_Vuong.pdf

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[1] Tuan Vuong, "Cyber-Physical Intrusion Detection on a Robotic Vehicle", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/598. Accessed: Mar. 19, 2019.
@article{598-16,
url = {http://sigport.org/598},
author = {Tuan Vuong },
publisher = {IEEE SigPort},
title = {Cyber-Physical Intrusion Detection on a Robotic Vehicle},
year = {2016} }
TY - EJOUR
T1 - Cyber-Physical Intrusion Detection on a Robotic Vehicle
AU - Tuan Vuong
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/598
ER -
Tuan Vuong. (2016). Cyber-Physical Intrusion Detection on a Robotic Vehicle. IEEE SigPort. http://sigport.org/598
Tuan Vuong, 2016. Cyber-Physical Intrusion Detection on a Robotic Vehicle. Available at: http://sigport.org/598.
Tuan Vuong. (2016). "Cyber-Physical Intrusion Detection on a Robotic Vehicle." Web.
1. Tuan Vuong. Cyber-Physical Intrusion Detection on a Robotic Vehicle [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/598

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