Sorry, you need to enable JavaScript to visit this website.

Applications

END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET


In video surveillance applications, person search is a chal-
lenging task consisting in detecting people and extracting
features from their silhouette for re-identification (re-ID) pur-
pose. We propose a new end-to-end model that jointly com-
putes detection and feature extraction steps through a single
deep Convolutional Neural Network architecture. Sharing
feature maps between the two tasks for jointly describing
people commonalities and specificities allows faster runtime,
which is valuable in real-world applications. In addition

Paper Details

Authors:
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier
Submitted On:
19 September 2019 - 12:16pm
Short Link:
Type:
Event:
Document Year:
Cite

Document Files

2019_ICIP_aloesch

(7)

Keywords

Subscribe

[1] Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier, "END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4743. Accessed: Nov. 21, 2019.
@article{4743-19,
url = {http://sigport.org/4743},
author = {Angelique Loesch; Jaonary Rabarisoa; Romaric Audigier },
publisher = {IEEE SigPort},
title = {END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET},
year = {2019} }
TY - EJOUR
T1 - END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET
AU - Angelique Loesch; Jaonary Rabarisoa; Romaric Audigier
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4743
ER -
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier. (2019). END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET. IEEE SigPort. http://sigport.org/4743
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier, 2019. END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET. Available at: http://sigport.org/4743.
Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier. (2019). "END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET." Web.
1. Angelique Loesch, Jaonary Rabarisoa, Romaric Audigier. END-TO-END PERSON SEARCH SEQUENTIALLY TRAINED ON AGGREGATED DATASET [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4743

A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS


We describe in this paper a Two-Stream Siamese Neural Network for vehicle re-identification. The proposed network is fed simultaneously with small coarse patches of the vehicle shape’s, with 96 × 96 pixels, in one stream, and fine features extracted from license plate patches, easily readable by humans, with 96 × 48 pixels, in the other one.

Paper Details

Authors:
Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto
Submitted On:
19 September 2019 - 6:09am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICIP 2019.pdf

(6)

Keywords

Subscribe

[1] Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto, "A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4719. Accessed: Nov. 21, 2019.
@article{4719-19,
url = {http://sigport.org/4719},
author = {Icaro Oliveira; Keiko Fonseca; Rodrigo Minetto },
publisher = {IEEE SigPort},
title = {A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS},
year = {2019} }
TY - EJOUR
T1 - A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS
AU - Icaro Oliveira; Keiko Fonseca; Rodrigo Minetto
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4719
ER -
Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto. (2019). A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS. IEEE SigPort. http://sigport.org/4719
Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto, 2019. A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS. Available at: http://sigport.org/4719.
Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto. (2019). "A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS." Web.
1. Icaro Oliveira, Keiko Fonseca, Rodrigo Minetto. A TWO-STREAM SIAMESE NEURAL NETWORK FOR VEHICLE RE-IDENTIFICATION BY USING NON-OVERLAPPING CAMERAS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4719

Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)


Given a sequence of observations for each person in each camera, identifying or re-identifying the same person across different cameras is one of the objectives of video surveillance systems. In the case of video based person re-id, the challenge is to handle the high correlation between temporally adjacent frames. The presence of non-informative frames results in high redundancy which needs to be removed for an efficient re-id.

Paper Details

Authors:
Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty
Submitted On:
19 September 2019 - 6:40am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICIP_2019_POSTER_(PAPER_ID_3410).pdf

(23)

Keywords

Subscribe

[1] Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty, "Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4691. Accessed: Nov. 21, 2019.
@article{4691-19,
url = {http://sigport.org/4691},
author = {Gaurav Kumar Nayak; Utkarsh Shreemali; R Venkatesh Babu; Anirban Chakraborty },
publisher = {IEEE SigPort},
title = {Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)},
year = {2019} }
TY - EJOUR
T1 - Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)
AU - Gaurav Kumar Nayak; Utkarsh Shreemali; R Venkatesh Babu; Anirban Chakraborty
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4691
ER -
Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty. (2019). Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP). IEEE SigPort. http://sigport.org/4691
Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty, 2019. Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP). Available at: http://sigport.org/4691.
Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty. (2019). "Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP)." Web.
1. Gaurav Kumar Nayak, Utkarsh Shreemali, R Venkatesh Babu, Anirban Chakraborty. Efficient Person Re-Identification in Videos Using Sequence Lazy Greedy Determinantal Point Process (SLGDPP) [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4691

Differentially Private Sparse Inverse Covariance Estimation

Paper Details

Authors:
Mengdi Huai, Jinhui Xu
Submitted On:
20 November 2018 - 4:05pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

private_sparse_inverse.pdf

(78)

Subscribe

[1] Mengdi Huai, Jinhui Xu, "Differentially Private Sparse Inverse Covariance Estimation ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3694. Accessed: Nov. 21, 2019.
@article{3694-18,
url = {http://sigport.org/3694},
author = {Mengdi Huai; Jinhui Xu },
publisher = {IEEE SigPort},
title = {Differentially Private Sparse Inverse Covariance Estimation },
year = {2018} }
TY - EJOUR
T1 - Differentially Private Sparse Inverse Covariance Estimation
AU - Mengdi Huai; Jinhui Xu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3694
ER -
Mengdi Huai, Jinhui Xu. (2018). Differentially Private Sparse Inverse Covariance Estimation . IEEE SigPort. http://sigport.org/3694
Mengdi Huai, Jinhui Xu, 2018. Differentially Private Sparse Inverse Covariance Estimation . Available at: http://sigport.org/3694.
Mengdi Huai, Jinhui Xu. (2018). "Differentially Private Sparse Inverse Covariance Estimation ." Web.
1. Mengdi Huai, Jinhui Xu. Differentially Private Sparse Inverse Covariance Estimation [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3694

USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY

Paper Details

Authors:
R. Soundar Raja James, K. Naik and A. Nayak
Submitted On:
23 April 2018 - 12:51pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018-Poster-Albasir.v2.pdf

(225)

Keywords

Subscribe

[1] R. Soundar Raja James, K. Naik and A. Nayak, "USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3151. Accessed: Nov. 21, 2019.
@article{3151-18,
url = {http://sigport.org/3151},
author = {R. Soundar Raja James; K. Naik and A. Nayak },
publisher = {IEEE SigPort},
title = {USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY},
year = {2018} }
TY - EJOUR
T1 - USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY
AU - R. Soundar Raja James; K. Naik and A. Nayak
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3151
ER -
R. Soundar Raja James, K. Naik and A. Nayak. (2018). USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY. IEEE SigPort. http://sigport.org/3151
R. Soundar Raja James, K. Naik and A. Nayak, 2018. USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY. Available at: http://sigport.org/3151.
R. Soundar Raja James, K. Naik and A. Nayak. (2018). "USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY." Web.
1. R. Soundar Raja James, K. Naik and A. Nayak. USING DEEP LEARNING TO CLASSIFY POWER CONSUMPTION SIGNALS OF WIRELESS DEVICES: AN APPLICATION TO CYBERSECURITY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3151

Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss


In this paper, we propose deep feature embedding learning for person re-identification (re-id) using lifted structured loss. Although triplet loss has been commonly used in deep neural networks for person re-id, the triplet loss-based framework is not effective in fully using the batch information. Thus, it needs to choose hard negative samples manually that is very time-consuming. To address this problem, we adopt lifted structured loss for deep neural networks that makes the network learn better feature embedding by minimizing intra-class variation and maximizing inter-class variation.

Paper Details

Authors:
Zhangping He, Zhendong Zhang, Cheolkon Jung
Submitted On:
20 April 2018 - 5:23am
Short Link:
Type:
Event:
Presenter's Name:
Document Year:
Cite

Document Files

ICASSP2018_PersonReID_final.pdf

(196)

Keywords

Additional Categories

Subscribe

[1] Zhangping He, Zhendong Zhang, Cheolkon Jung, "Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3098. Accessed: Nov. 21, 2019.
@article{3098-18,
url = {http://sigport.org/3098},
author = {Zhangping He; Zhendong Zhang; Cheolkon Jung },
publisher = {IEEE SigPort},
title = {Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss},
year = {2018} }
TY - EJOUR
T1 - Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss
AU - Zhangping He; Zhendong Zhang; Cheolkon Jung
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3098
ER -
Zhangping He, Zhendong Zhang, Cheolkon Jung. (2018). Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss. IEEE SigPort. http://sigport.org/3098
Zhangping He, Zhendong Zhang, Cheolkon Jung, 2018. Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss. Available at: http://sigport.org/3098.
Zhangping He, Zhendong Zhang, Cheolkon Jung. (2018). "Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss." Web.
1. Zhangping He, Zhendong Zhang, Cheolkon Jung. Deep Feature Embedding Learning for Person Re-Identification Using Lifted Structured Loss [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3098

Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid


We address the problem of constructing false data injection (FDI) attacks that can bypass the bad data detector (BDD) of a power grid. The attacker is assumed to have access to only power flow measurement data traces (collected over a limited period of time) and no other prior knowledge about the grid. Existing related algorithms are formulated under the assumption that the attacker has access to measurements collected over a long (asymptotically infinite) time period, which may not be realistic.

Paper Details

Authors:
Fuxi Wen, David Yau
Submitted On:
19 April 2018 - 2:51pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

Poster presentation

(163)

Subscribe

[1] Fuxi Wen, David Yau, " Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3002. Accessed: Nov. 21, 2019.
@article{3002-18,
url = {http://sigport.org/3002},
author = {Fuxi Wen; David Yau },
publisher = {IEEE SigPort},
title = { Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid},
year = {2018} }
TY - EJOUR
T1 - Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid
AU - Fuxi Wen; David Yau
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3002
ER -
Fuxi Wen, David Yau. (2018). Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid. IEEE SigPort. http://sigport.org/3002
Fuxi Wen, David Yau, 2018. Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid. Available at: http://sigport.org/3002.
Fuxi Wen, David Yau. (2018). " Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid." Web.
1. Fuxi Wen, David Yau. Trade-offs in Data-Driven False Data Injection Attacks Against the Power Grid [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3002

TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS


A common problem in audio forensics is the difficulty to
authenticate an audio recording. In this paper we provide a novel
and reliable solution to this problem by making use of a control
signal, visible and audible on the actual recording, yet ignored by
the listener, the TIC-TAC signal. We describe our live watermark
solution, we incorporate it in an integrity check algorithm and we
provide meaningful preliminary tests. Their results, computed in
terms of precision show an outstanding performance: 100%

Paper Details

Authors:
A. Ciobanu
Submitted On:
14 April 2018 - 3:31pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP 2018 TIC TAC.pptx

(45)

ICASSP 2018 TIC TAC.pptx

(39)

Keywords

Subscribe

[1] A. Ciobanu, "TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2848. Accessed: Nov. 21, 2019.
@article{2848-18,
url = {http://sigport.org/2848},
author = {A. Ciobanu },
publisher = {IEEE SigPort},
title = {TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS},
year = {2018} }
TY - EJOUR
T1 - TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS
AU - A. Ciobanu
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2848
ER -
A. Ciobanu. (2018). TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS. IEEE SigPort. http://sigport.org/2848
A. Ciobanu, 2018. TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS. Available at: http://sigport.org/2848.
A. Ciobanu. (2018). "TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS." Web.
1. A. Ciobanu. TIC-TAC, FORGERY TIME HAS RUN-UP! LIVE ACOUSTIC WATERMARKING FOR INTEGRITY CHECK IN FORENSIC APPLICATIONS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2848

Continuous Security In IoT Using Blockchain


The two major roadblocks for state of the art Internet of Things (IoT) infrastructure like smart buildings, smart cities, etc. are lack of trust between various entities of system and single point of failure which is a vulnerability causing extreme damage to the whole system. This paper proposes a blockchain based IoT security solution where, trust is established through the immutable and decentralized nature of blockchain. The distributed nature of blockchain makes the system more robust and immune to single point of failure.

Paper Details

Authors:
Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar
Submitted On:
13 April 2018 - 2:02am
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

ICASSP2018Poster.pdf

(187)

Keywords

Additional Categories

Subscribe

[1] Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar, "Continuous Security In IoT Using Blockchain", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2612. Accessed: Nov. 21, 2019.
@article{2612-18,
url = {http://sigport.org/2612},
author = {Pratik Verma; Dr. Aloknath De; Sai Anirudh Kondaveeti; Suman Shekhar },
publisher = {IEEE SigPort},
title = {Continuous Security In IoT Using Blockchain},
year = {2018} }
TY - EJOUR
T1 - Continuous Security In IoT Using Blockchain
AU - Pratik Verma; Dr. Aloknath De; Sai Anirudh Kondaveeti; Suman Shekhar
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2612
ER -
Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar. (2018). Continuous Security In IoT Using Blockchain. IEEE SigPort. http://sigport.org/2612
Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar, 2018. Continuous Security In IoT Using Blockchain. Available at: http://sigport.org/2612.
Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar. (2018). "Continuous Security In IoT Using Blockchain." Web.
1. Pratik Verma, Dr. Aloknath De, Sai Anirudh Kondaveeti, Suman Shekhar. Continuous Security In IoT Using Blockchain [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2612

DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions


We introduce DigitalSeal, a transaction authentication tool that works in both online and offline use scenarios. DigitalSeal is a digital scanner that reads transaction information sent by an issuing entity of the DigitalSeal reader for authentication, and the information is encoded using a specially crafted bar-code. DigitalSeal views various pieces of transaction information for users to verify and proceed with transaction authentication. DigitalSeal is generic, and is capable of reading information viewed on paper, computer monitors (similarly, kiosk monitors), and mobile phones.

Paper Details

Authors:
Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang
Submitted On:
19 April 2018 - 1:10pm
Short Link:
Type:
Event:
Presenter's Name:
Paper Code:
Document Year:
Cite

Document Files

[Presentation] DigitalSeal Presentation.pdf

(43)

Keywords

Subscribe

[1] Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang, "DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2534. Accessed: Nov. 21, 2019.
@article{2534-18,
url = {http://sigport.org/2534},
author = {Changhun Jung; Jeonil Kang; Aziz Mohaisen; DaeHun Nyang },
publisher = {IEEE SigPort},
title = {DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions},
year = {2018} }
TY - EJOUR
T1 - DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions
AU - Changhun Jung; Jeonil Kang; Aziz Mohaisen; DaeHun Nyang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2534
ER -
Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang. (2018). DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions. IEEE SigPort. http://sigport.org/2534
Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang, 2018. DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions. Available at: http://sigport.org/2534.
Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang. (2018). "DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions." Web.
1. Changhun Jung, Jeonil Kang, Aziz Mohaisen, DaeHun Nyang. DIGITALSEAL: A Transaction Authentication Tool for Online and Offline Transactions [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2534

Pages