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Information Forensics and Security

FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals


With the growth of location based services, indoor localization is attracting great interests as it facilitates further ubiquitous environments. In this paper, we propose FuseLoc, the first information fusion based indoor localization using multiple features extracted from Channel State Information (CSI). In FuseLoc, the localization problem is modelled as a pattern matching problem, where the location of a subject is predicted based on the similarity measure of the CSI features of the unknown location with those of the training locations.

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Authors:
Tahsina Farah Sanam, Hana Godrich
Submitted On:
10 May 2019 - 2:04pm
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ICASSP_Poster.pdf

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[1] Tahsina Farah Sanam, Hana Godrich, "FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4385. Accessed: May. 26, 2019.
@article{4385-19,
url = {http://sigport.org/4385},
author = {Tahsina Farah Sanam; Hana Godrich },
publisher = {IEEE SigPort},
title = {FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals},
year = {2019} }
TY - EJOUR
T1 - FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals
AU - Tahsina Farah Sanam; Hana Godrich
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4385
ER -
Tahsina Farah Sanam, Hana Godrich. (2019). FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals. IEEE SigPort. http://sigport.org/4385
Tahsina Farah Sanam, Hana Godrich, 2019. FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals. Available at: http://sigport.org/4385.
Tahsina Farah Sanam, Hana Godrich. (2019). "FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals." Web.
1. Tahsina Farah Sanam, Hana Godrich. FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4385

Clonability of anti-counterfeiting printable graphical codes: a machine learning approach

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Authors:
Olga Taran, Slavi Bonev, Slava Voloshynovskiy
Submitted On:
10 May 2019 - 11:13am
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Slides

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[1] Olga Taran, Slavi Bonev, Slava Voloshynovskiy, "Clonability of anti-counterfeiting printable graphical codes: a machine learning approach", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4359. Accessed: May. 26, 2019.
@article{4359-19,
url = {http://sigport.org/4359},
author = {Olga Taran; Slavi Bonev; Slava Voloshynovskiy },
publisher = {IEEE SigPort},
title = {Clonability of anti-counterfeiting printable graphical codes: a machine learning approach},
year = {2019} }
TY - EJOUR
T1 - Clonability of anti-counterfeiting printable graphical codes: a machine learning approach
AU - Olga Taran; Slavi Bonev; Slava Voloshynovskiy
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4359
ER -
Olga Taran, Slavi Bonev, Slava Voloshynovskiy. (2019). Clonability of anti-counterfeiting printable graphical codes: a machine learning approach. IEEE SigPort. http://sigport.org/4359
Olga Taran, Slavi Bonev, Slava Voloshynovskiy, 2019. Clonability of anti-counterfeiting printable graphical codes: a machine learning approach. Available at: http://sigport.org/4359.
Olga Taran, Slavi Bonev, Slava Voloshynovskiy. (2019). "Clonability of anti-counterfeiting printable graphical codes: a machine learning approach." Web.
1. Olga Taran, Slavi Bonev, Slava Voloshynovskiy. Clonability of anti-counterfeiting printable graphical codes: a machine learning approach [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4359

Phylogenetic Analysis of Software Using Cache Miss Statistics


While the phylogenetic analysis of multimedia documents keeps being investigated, some recent studies have shown the possibility of re-using the same strategies to analyze the evolution of computer programs (Software Phylogeny), considering its several applications spanning from copyright enforcement to malware detection.

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Authors:
Sebastiano Verde, Simone Milani, Giancarlo Calvagno
Submitted On:
8 May 2019 - 10:28am
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verde2019phylosoft

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[1] Sebastiano Verde, Simone Milani, Giancarlo Calvagno, "Phylogenetic Analysis of Software Using Cache Miss Statistics", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4035. Accessed: May. 26, 2019.
@article{4035-19,
url = {http://sigport.org/4035},
author = {Sebastiano Verde; Simone Milani; Giancarlo Calvagno },
publisher = {IEEE SigPort},
title = {Phylogenetic Analysis of Software Using Cache Miss Statistics},
year = {2019} }
TY - EJOUR
T1 - Phylogenetic Analysis of Software Using Cache Miss Statistics
AU - Sebastiano Verde; Simone Milani; Giancarlo Calvagno
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4035
ER -
Sebastiano Verde, Simone Milani, Giancarlo Calvagno. (2019). Phylogenetic Analysis of Software Using Cache Miss Statistics. IEEE SigPort. http://sigport.org/4035
Sebastiano Verde, Simone Milani, Giancarlo Calvagno, 2019. Phylogenetic Analysis of Software Using Cache Miss Statistics. Available at: http://sigport.org/4035.
Sebastiano Verde, Simone Milani, Giancarlo Calvagno. (2019). "Phylogenetic Analysis of Software Using Cache Miss Statistics." Web.
1. Sebastiano Verde, Simone Milani, Giancarlo Calvagno. Phylogenetic Analysis of Software Using Cache Miss Statistics [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4035

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: May. 26, 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

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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Submitted On:
27 March 2019 - 9:03am
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wifs18_dnn_piracy.pdf

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[1] , "Protect Your Deep Neural Networks from Piracy", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3846. Accessed: May. 26, 2019.
@article{3846-19,
url = {http://sigport.org/3846},
author = { },
publisher = {IEEE SigPort},
title = {Protect Your Deep Neural Networks from Piracy},
year = {2019} }
TY - EJOUR
T1 - Protect Your Deep Neural Networks from Piracy
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3846
ER -
. (2019). Protect Your Deep Neural Networks from Piracy. IEEE SigPort. http://sigport.org/3846
, 2019. Protect Your Deep Neural Networks from Piracy. Available at: http://sigport.org/3846.
. (2019). "Protect Your Deep Neural Networks from Piracy." Web.
1. . Protect Your Deep Neural Networks from Piracy [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3846

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: May. 26, 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: May. 26, 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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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: May. 26, 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.

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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: May. 26, 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

Paper Details

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: May. 26, 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

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