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ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES

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Authors:
Kenta Iida, Hitoshi Kiya
Submitted On:
15 September 2017 - 9:56am
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PID2499.pdf

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[1] Kenta Iida, Hitoshi Kiya, "ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2105. Accessed: Dec. 16, 2017.
@article{2105-17,
url = {http://sigport.org/2105},
author = {Kenta Iida; Hitoshi Kiya },
publisher = {IEEE SigPort},
title = {ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES},
year = {2017} }
TY - EJOUR
T1 - ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES
AU - Kenta Iida; Hitoshi Kiya
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2105
ER -
Kenta Iida, Hitoshi Kiya. (2017). ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES. IEEE SigPort. http://sigport.org/2105
Kenta Iida, Hitoshi Kiya, 2017. ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES. Available at: http://sigport.org/2105.
Kenta Iida, Hitoshi Kiya. (2017). "ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES." Web.
1. Kenta Iida, Hitoshi Kiya. ROBUST IMAGE IDENTIFICATION WITH SECURE FEATURES FOR JPEG IMAGES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2105

ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION


Face and object recognition in uncontrolled scenarios due to pose and illumination variations, low resolution, etc. is a challenging research area. Here we propose a novel descriptor, Aligned Discriminative Pose Robust ( ADPR) descriptor, for matching faces and objects across pose which is also robust to resolution and illumination variations. We generate virtual intermediate pose subspaces from training examples at a few poses and compute the alignment matrices of those subspaces with the frontal subspace.

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Authors:
Soubhik Sanyal, Devraj Mandal, Soma Biswas
Submitted On:
14 September 2017 - 8:38am
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This is the presentation slides.

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[1] Soubhik Sanyal, Devraj Mandal, Soma Biswas, "ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2024. Accessed: Dec. 16, 2017.
@article{2024-17,
url = {http://sigport.org/2024},
author = {Soubhik Sanyal; Devraj Mandal; Soma Biswas },
publisher = {IEEE SigPort},
title = {ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION},
year = {2017} }
TY - EJOUR
T1 - ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION
AU - Soubhik Sanyal; Devraj Mandal; Soma Biswas
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2024
ER -
Soubhik Sanyal, Devraj Mandal, Soma Biswas. (2017). ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION. IEEE SigPort. http://sigport.org/2024
Soubhik Sanyal, Devraj Mandal, Soma Biswas, 2017. ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION. Available at: http://sigport.org/2024.
Soubhik Sanyal, Devraj Mandal, Soma Biswas. (2017). "ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION." Web.
1. Soubhik Sanyal, Devraj Mandal, Soma Biswas. ALIGNED DISCRIMINATIVE POSE ROBUST DESCRIPTORS FOR FACE AND OBJECT RECOGNITION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2024

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.

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Authors:
Mohammad Haghighat, Mohamed Abdel-Mottaleb, Wadee Alhalabi
Submitted On:
16 July 2016 - 11:13pm
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DCA_ICASSP16_Poster.pdf

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[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: Dec. 16, 2017.
@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

Detection of False Data Injection Attacks in Smart Grid Communication Systems


The transformation of traditional energy networks to smart grids can assist in revolutionizing the energy industry in terms of reliability, performance and manageability. However, increased connectivity of power grid assets for bidirectional communications presents severe security vulnerabilities. In this paper, we investigate Chi-square detector and cosine similarity matching approaches for attack detection in smart grids where Kalman filter estimation is used to measure any deviation from actual measurements.

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Authors:
Chandra Bajracharya
Submitted On:
23 February 2016 - 1:44pm
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Attack Detection in Smart Grid @ GlobalSIP 2015.pdf

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[1] Chandra Bajracharya, "Detection of False Data Injection Attacks in Smart Grid Communication Systems", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/529. Accessed: Dec. 16, 2017.
@article{529-15,
url = {http://sigport.org/529},
author = {Chandra Bajracharya },
publisher = {IEEE SigPort},
title = {Detection of False Data Injection Attacks in Smart Grid Communication Systems},
year = {2015} }
TY - EJOUR
T1 - Detection of False Data Injection Attacks in Smart Grid Communication Systems
AU - Chandra Bajracharya
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/529
ER -
Chandra Bajracharya. (2015). Detection of False Data Injection Attacks in Smart Grid Communication Systems. IEEE SigPort. http://sigport.org/529
Chandra Bajracharya, 2015. Detection of False Data Injection Attacks in Smart Grid Communication Systems. Available at: http://sigport.org/529.
Chandra Bajracharya. (2015). "Detection of False Data Injection Attacks in Smart Grid Communication Systems." Web.
1. Chandra Bajracharya. Detection of False Data Injection Attacks in Smart Grid Communication Systems [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/529

STEALTHY CONTROL SIGNAL ATTACKS IN SCALAR LQG SYSTEMS

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23 February 2016 - 1:44pm
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RZhangGlobalsip15.pdf

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[1] , "STEALTHY CONTROL SIGNAL ATTACKS IN SCALAR LQG SYSTEMS", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/505. Accessed: Dec. 16, 2017.
@article{505-15,
url = {http://sigport.org/505},
author = { },
publisher = {IEEE SigPort},
title = {STEALTHY CONTROL SIGNAL ATTACKS IN SCALAR LQG SYSTEMS},
year = {2015} }
TY - EJOUR
T1 - STEALTHY CONTROL SIGNAL ATTACKS IN SCALAR LQG SYSTEMS
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/505
ER -
. (2015). STEALTHY CONTROL SIGNAL ATTACKS IN SCALAR LQG SYSTEMS. IEEE SigPort. http://sigport.org/505
, 2015. STEALTHY CONTROL SIGNAL ATTACKS IN SCALAR LQG SYSTEMS. Available at: http://sigport.org/505.
. (2015). "STEALTHY CONTROL SIGNAL ATTACKS IN SCALAR LQG SYSTEMS." Web.
1. . STEALTHY CONTROL SIGNAL ATTACKS IN SCALAR LQG SYSTEMS [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/505

Malware Images: Visualization and Automatic Classification


We propose a simple yet effective method for visualizing and classifying malware using image processing techniques. Malware binaries are visualized as gray-scale images, with the observation that for many malware families, the images belonging to the same family appear very similar in layout and texture. Motivated by this visual similarity, a classification method using standard image features is proposed. Neither disassembly nor code execution is required for classification.

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Authors:
S. Karthikeyan, Gregoire Jacob, B.S. Manjunath
Submitted On:
23 February 2016 - 1:43pm
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2011-VizSec-Malware-Images.pdf

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[1] S. Karthikeyan, Gregoire Jacob, B.S. Manjunath, "Malware Images: Visualization and Automatic Classification", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/218. Accessed: Dec. 16, 2017.
@article{218-15,
url = {http://sigport.org/218},
author = {S. Karthikeyan; Gregoire Jacob; B.S. Manjunath },
publisher = {IEEE SigPort},
title = {Malware Images: Visualization and Automatic Classification},
year = {2015} }
TY - EJOUR
T1 - Malware Images: Visualization and Automatic Classification
AU - S. Karthikeyan; Gregoire Jacob; B.S. Manjunath
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/218
ER -
S. Karthikeyan, Gregoire Jacob, B.S. Manjunath. (2015). Malware Images: Visualization and Automatic Classification. IEEE SigPort. http://sigport.org/218
S. Karthikeyan, Gregoire Jacob, B.S. Manjunath, 2015. Malware Images: Visualization and Automatic Classification. Available at: http://sigport.org/218.
S. Karthikeyan, Gregoire Jacob, B.S. Manjunath. (2015). "Malware Images: Visualization and Automatic Classification." Web.
1. S. Karthikeyan, Gregoire Jacob, B.S. Manjunath. Malware Images: Visualization and Automatic Classification [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/218

SATTVA: SpArsiTy inspired classificaTion of malware VAriants


There is an alarming increase in the amount of malware that is generated today. However, several studies have shown that most of these new malware are just variants of existing ones. Fast detection of these variants plays an effective role in thwarting new attacks. In this paper, we propose a novel approach to detect malware variants using a sparse representation framework. Exploiting the fact that most malware variants have small differences in their structure, we model a new/unknown malware sample as a sparse linear combination of other malware in the training set.

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Authors:
S. Karthikeyan, B.S. Manjunath
Submitted On:
23 February 2016 - 1:43pm
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SATTVA-ACM-MMSEC-2015-Presentation.pdf

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[1] S. Karthikeyan, B.S. Manjunath, "SATTVA: SpArsiTy inspired classificaTion of malware VAriants", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/214. Accessed: Dec. 16, 2017.
@article{214-15,
url = {http://sigport.org/214},
author = {S. Karthikeyan; B.S. Manjunath },
publisher = {IEEE SigPort},
title = {SATTVA: SpArsiTy inspired classificaTion of malware VAriants},
year = {2015} }
TY - EJOUR
T1 - SATTVA: SpArsiTy inspired classificaTion of malware VAriants
AU - S. Karthikeyan; B.S. Manjunath
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/214
ER -
S. Karthikeyan, B.S. Manjunath. (2015). SATTVA: SpArsiTy inspired classificaTion of malware VAriants. IEEE SigPort. http://sigport.org/214
S. Karthikeyan, B.S. Manjunath, 2015. SATTVA: SpArsiTy inspired classificaTion of malware VAriants. Available at: http://sigport.org/214.
S. Karthikeyan, B.S. Manjunath. (2015). "SATTVA: SpArsiTy inspired classificaTion of malware VAriants." Web.
1. S. Karthikeyan, B.S. Manjunath. SATTVA: SpArsiTy inspired classificaTion of malware VAriants [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/214

Online Social Networks Privacy Study Through TAPE Framework


Abstract—While personal information privacy is threatened by online social networks, researchers are seeking for privacy pro- tection tools and methods to assist online social network users. In this paper, we propose a Trust-Aware Privacy Evaluation frame- work, called TAPE, aiming to address this problem. Under the TAPE framework we investigate how to quantitatively evaluate the privacy risk, as a function of people’s awarenesses of privacy risks as well as whether people can be trusted by their friends to protect others’ personal information.

document.pdf

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Authors:
Liudong Xing, Vinod Vokkarane
Submitted On:
23 February 2016 - 1:38pm
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[1] Liudong Xing, Vinod Vokkarane, "Online Social Networks Privacy Study Through TAPE Framework", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/195. Accessed: Dec. 16, 2017.
@article{195-15,
url = {http://sigport.org/195},
author = {Liudong Xing; Vinod Vokkarane },
publisher = {IEEE SigPort},
title = {Online Social Networks Privacy Study Through TAPE Framework},
year = {2015} }
TY - EJOUR
T1 - Online Social Networks Privacy Study Through TAPE Framework
AU - Liudong Xing; Vinod Vokkarane
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/195
ER -
Liudong Xing, Vinod Vokkarane. (2015). Online Social Networks Privacy Study Through TAPE Framework. IEEE SigPort. http://sigport.org/195
Liudong Xing, Vinod Vokkarane, 2015. Online Social Networks Privacy Study Through TAPE Framework. Available at: http://sigport.org/195.
Liudong Xing, Vinod Vokkarane. (2015). "Online Social Networks Privacy Study Through TAPE Framework." Web.
1. Liudong Xing, Vinod Vokkarane. Online Social Networks Privacy Study Through TAPE Framework [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/195

EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION (for testing)


Abstract—Online review plays an important role when people are making decisions to purchase a product or service. It is shown that sellers can benefit from boosting their product review or downgrading their competitors’ product review. Dishonest behavior on reviews can seriously affect both buyers and sellers. In this paper, we introduce a novel angle to detect dishonest reviews, called Equal Rating Opportunity (ERO) evaluation. The proposed ERO evaluation can detect embedded manipulation signals based on limited amount of data. Experiments based on real data are conducted.

document.pdf

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23 February 2016 - 1:43pm
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[1] , "EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION (for testing)", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/192. Accessed: Dec. 16, 2017.
@article{192-15,
url = {http://sigport.org/192},
author = { },
publisher = {IEEE SigPort},
title = {EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION (for testing)},
year = {2015} }
TY - EJOUR
T1 - EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION (for testing)
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/192
ER -
. (2015). EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION (for testing). IEEE SigPort. http://sigport.org/192
, 2015. EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION (for testing). Available at: http://sigport.org/192.
. (2015). "EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION (for testing)." Web.
1. . EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION (for testing) [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/192

EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION


Abstract—Online review plays an important role when people are making decisions to purchase a product or service. It is shown that sellers can benefit from boosting their product review or downgrading their competitors’ product review. Dishonest behavior on reviews can seriously affect both buyers and sellers. In this paper, we introduce a novel angle to detect dishonest reviews, called Equal Rating Opportunity (ERO) evaluation. The proposed ERO evaluation can detect embedded manipulation signals based on limited amount of data. Experiments based on real data are conducted.

final1.pdf

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Submitted On:
23 February 2016 - 1:43pm
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final1.pdf

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[1] , "EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/177. Accessed: Dec. 16, 2017.
@article{177-15,
url = {http://sigport.org/177},
author = { },
publisher = {IEEE SigPort},
title = {EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION},
year = {2015} }
TY - EJOUR
T1 - EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION
AU -
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/177
ER -
. (2015). EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION. IEEE SigPort. http://sigport.org/177
, 2015. EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION. Available at: http://sigport.org/177.
. (2015). "EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION." Web.
1. . EQUAL RATING OPPORTUNITY ANALYSIS FOR DETECTING REVIEW MANIPULATION [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/177

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