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Multimedia Forensics

Information Theoretical Limit of Operation Forensics


Abstract—While more and more forensic techniques have been proposed to detect the processing history of multimedia content, one starts to wonder if there exists a fundamental limit on the capability of forensics. In other words, besides keeping on searching what investigators can do, it is also important to find out the limit of their capability and what they cannot do. In this work, we explore the fundamental limit of operation forensics by proposing an information theoretical framework.

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Authors:
Matthew C. Stamm
Submitted On:
23 February 2016 - 1:44pm
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[1] Matthew C. Stamm, "Information Theoretical Limit of Operation Forensics", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/147. Accessed: Jul. 23, 2019.
@article{147-15,
url = {http://sigport.org/147},
author = {Matthew C. Stamm },
publisher = {IEEE SigPort},
title = {Information Theoretical Limit of Operation Forensics},
year = {2015} }
TY - EJOUR
T1 - Information Theoretical Limit of Operation Forensics
AU - Matthew C. Stamm
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/147
ER -
Matthew C. Stamm. (2015). Information Theoretical Limit of Operation Forensics. IEEE SigPort. http://sigport.org/147
Matthew C. Stamm, 2015. Information Theoretical Limit of Operation Forensics. Available at: http://sigport.org/147.
Matthew C. Stamm. (2015). "Information Theoretical Limit of Operation Forensics." Web.
1. Matthew C. Stamm. Information Theoretical Limit of Operation Forensics [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/147

Compressive Sensing Forensics


Abstract—Identifying a signal’s origin and how it was acquired is an important forensic problem. While forensic techniques currently exist to determine a signal’s acquisition history, these techniques do not account for the possibility that a signal could be compressively sensed. This is an important problem since compressive sensing techniques have seen increased popularity in recent years. In this paper, we propose a set of forensic techniques to identify signals acquired by compressive sensing. We do this by first identifying the fingerprints left in a signal by compressive sensing.

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Authors:
Matthew C. Stamm
Submitted On:
23 February 2016 - 1:43pm
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[1] Matthew C. Stamm, "Compressive Sensing Forensics", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/146. Accessed: Jul. 23, 2019.
@article{146-15,
url = {http://sigport.org/146},
author = {Matthew C. Stamm },
publisher = {IEEE SigPort},
title = {Compressive Sensing Forensics},
year = {2015} }
TY - EJOUR
T1 - Compressive Sensing Forensics
AU - Matthew C. Stamm
PY - 2015
PB - IEEE SigPort
UR - http://sigport.org/146
ER -
Matthew C. Stamm. (2015). Compressive Sensing Forensics. IEEE SigPort. http://sigport.org/146
Matthew C. Stamm, 2015. Compressive Sensing Forensics. Available at: http://sigport.org/146.
Matthew C. Stamm. (2015). "Compressive Sensing Forensics." Web.
1. Matthew C. Stamm. Compressive Sensing Forensics [Internet]. IEEE SigPort; 2015. Available from : http://sigport.org/146

Sentiment Aware Fake News Detection on Online Social Networks

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Authors:
Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari
Submitted On:
19 May 2019 - 7:04pm
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[1] Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari, "Sentiment Aware Fake News Detection on Online Social Networks", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4552. Accessed: Jul. 23, 2019.
@article{4552-19,
url = {http://sigport.org/4552},
author = {Oluwaseun Ajao; Deepayan Bhowmik; Shahrzad Zargari },
publisher = {IEEE SigPort},
title = {Sentiment Aware Fake News Detection on Online Social Networks},
year = {2019} }
TY - EJOUR
T1 - Sentiment Aware Fake News Detection on Online Social Networks
AU - Oluwaseun Ajao; Deepayan Bhowmik; Shahrzad Zargari
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4552
ER -
Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari. (2019). Sentiment Aware Fake News Detection on Online Social Networks. IEEE SigPort. http://sigport.org/4552
Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari, 2019. Sentiment Aware Fake News Detection on Online Social Networks. Available at: http://sigport.org/4552.
Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari. (2019). "Sentiment Aware Fake News Detection on Online Social Networks." Web.
1. Oluwaseun Ajao, Deepayan Bhowmik, Shahrzad Zargari. Sentiment Aware Fake News Detection on Online Social Networks [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4552

ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding


Electric Network Frequency (ENF) analysis is a promising forensic technique for authenticating digital recordings and detecting tampering within the recordings. The validity of ENF analysis heavily relies on high-quality ENF signals extracted from multimedia recordings. In this paper, we propose an ENF signal extraction method for rolling shutter acquired videos using periodic zero-padding. Our analysis shows that the extracted ENF signals using the proposed method are not distorted and the component with the highest signal-to-noise ratio is located at the intrinsic frequency.

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Submitted On:
21 May 2019 - 8:49pm
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Poster for ICASSP 2019 paper

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[1] , "ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4550. Accessed: Jul. 23, 2019.
@article{4550-19,
url = {http://sigport.org/4550},
author = { },
publisher = {IEEE SigPort},
title = {ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding},
year = {2019} }
TY - EJOUR
T1 - ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding
AU -
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4550
ER -
. (2019). ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding. IEEE SigPort. http://sigport.org/4550
, 2019. ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding. Available at: http://sigport.org/4550.
. (2019). "ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding." Web.
1. . ENF Signal Extraction for Rolling-shutter Videos Using Periodic Zero-padding [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4550

The Effect of Light Source on ENF Based Video Forensics


ENF (Electric Network Frequency) oscillates around a nominal value (50/60 Hz) due to imbalance between consumed and generated power. The intensity of a light source powered by mains electricity varies depending on the ENF fluctuations. These fluctuations can be extracted from videos recorded in the presence of mains-powered source illumination. This work investigates how the quality of the ENF signal estimated from video is affected by different light source illumination, compression ratios, and by social media encoding.

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Authors:
Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon
Submitted On:
15 May 2019 - 8:17am
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[1] Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon, "The Effect of Light Source on ENF Based Video Forensics", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4524. Accessed: Jul. 23, 2019.
@article{4524-19,
url = {http://sigport.org/4524},
author = {Saffet Vatansever; Ahmet Emir Dirik; Nasir Memon },
publisher = {IEEE SigPort},
title = {The Effect of Light Source on ENF Based Video Forensics},
year = {2019} }
TY - EJOUR
T1 - The Effect of Light Source on ENF Based Video Forensics
AU - Saffet Vatansever; Ahmet Emir Dirik; Nasir Memon
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4524
ER -
Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon. (2019). The Effect of Light Source on ENF Based Video Forensics. IEEE SigPort. http://sigport.org/4524
Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon, 2019. The Effect of Light Source on ENF Based Video Forensics. Available at: http://sigport.org/4524.
Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon. (2019). "The Effect of Light Source on ENF Based Video Forensics." Web.
1. Saffet Vatansever, Ahmet Emir Dirik, Nasir Memon. The Effect of Light Source on ENF Based Video Forensics [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4524

Towards learned color representations for image splicing detection

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Authors:
Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess
Submitted On:
13 May 2019 - 2:05pm
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[1] Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess, "Towards learned color representations for image splicing detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4366. Accessed: Jul. 23, 2019.
@article{4366-19,
url = {http://sigport.org/4366},
author = {Benjamin Hadwiger; Daniele Baracchi; Alessandro Piva; Christian Riess },
publisher = {IEEE SigPort},
title = {Towards learned color representations for image splicing detection},
year = {2019} }
TY - EJOUR
T1 - Towards learned color representations for image splicing detection
AU - Benjamin Hadwiger; Daniele Baracchi; Alessandro Piva; Christian Riess
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4366
ER -
Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess. (2019). Towards learned color representations for image splicing detection. IEEE SigPort. http://sigport.org/4366
Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess, 2019. Towards learned color representations for image splicing detection. Available at: http://sigport.org/4366.
Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess. (2019). "Towards learned color representations for image splicing detection." Web.
1. Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess. Towards learned color representations for image splicing detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4366

Towards learned color representations for image splicing detection

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Authors:
Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess
Submitted On:
10 May 2019 - 11:48am
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[1] Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess, "Towards learned color representations for image splicing detection", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4365. Accessed: Jul. 23, 2019.
@article{4365-19,
url = {http://sigport.org/4365},
author = {Benjamin Hadwiger; Daniele Baracchi; Alessandro Piva; Christian Riess },
publisher = {IEEE SigPort},
title = {Towards learned color representations for image splicing detection},
year = {2019} }
TY - EJOUR
T1 - Towards learned color representations for image splicing detection
AU - Benjamin Hadwiger; Daniele Baracchi; Alessandro Piva; Christian Riess
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4365
ER -
Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess. (2019). Towards learned color representations for image splicing detection. IEEE SigPort. http://sigport.org/4365
Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess, 2019. Towards learned color representations for image splicing detection. Available at: http://sigport.org/4365.
Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess. (2019). "Towards learned color representations for image splicing detection." Web.
1. Benjamin Hadwiger, Daniele Baracchi, Alessandro Piva, Christian Riess. Towards learned color representations for image splicing detection [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4365

REDUCED COMPLEXITY IMAGE CLUSTERING BASED ON CAMERA FINGERPRINTS


This work presents a reduced complexity image clustering (RCIC) algorithm that blindly groups images based on their camera fingerprint. The algorithm does not need any prior information and can be implemented without and with attraction, to refine clusters. After a camera fingerprint is estimated for each image in the data set, a fingerprint is randomly selected as reference fingerprint and a cluster is constructed using this fingerprint as centroid. The clustered fingerprints are removed from the data set and the remaining fingerprints are clustered repeating the same process.

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Authors:
Sahib Khan
Submitted On:
8 May 2019 - 3:46am
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[1] Sahib Khan, "REDUCED COMPLEXITY IMAGE CLUSTERING BASED ON CAMERA FINGERPRINTS", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4037. Accessed: Jul. 23, 2019.
@article{4037-19,
url = {http://sigport.org/4037},
author = {Sahib Khan },
publisher = {IEEE SigPort},
title = {REDUCED COMPLEXITY IMAGE CLUSTERING BASED ON CAMERA FINGERPRINTS},
year = {2019} }
TY - EJOUR
T1 - REDUCED COMPLEXITY IMAGE CLUSTERING BASED ON CAMERA FINGERPRINTS
AU - Sahib Khan
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4037
ER -
Sahib Khan. (2019). REDUCED COMPLEXITY IMAGE CLUSTERING BASED ON CAMERA FINGERPRINTS. IEEE SigPort. http://sigport.org/4037
Sahib Khan, 2019. REDUCED COMPLEXITY IMAGE CLUSTERING BASED ON CAMERA FINGERPRINTS. Available at: http://sigport.org/4037.
Sahib Khan. (2019). "REDUCED COMPLEXITY IMAGE CLUSTERING BASED ON CAMERA FINGERPRINTS." Web.
1. Sahib Khan. REDUCED COMPLEXITY IMAGE CLUSTERING BASED ON CAMERA FINGERPRINTS [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4037

Detection of Real-world Fights in Surveillance Videos


In this work, we tackle this problem by firstly proposing CCTV-Fights, a novel and challenging dataset containing 1,000 videos of real fights, with more than 8 hours of annotated CCTV footage. Then we propose a pipeline, on which we assess the impact of different feature extractors, as well as different classifiers.

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Authors:
Mauricio Perez, Alex C. Kot, Anderson Rocha
Submitted On:
7 May 2019 - 10:47pm
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[1] Mauricio Perez, Alex C. Kot, Anderson Rocha, "Detection of Real-world Fights in Surveillance Videos", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3994. Accessed: Jul. 23, 2019.
@article{3994-19,
url = {http://sigport.org/3994},
author = {Mauricio Perez; Alex C. Kot; Anderson Rocha },
publisher = {IEEE SigPort},
title = {Detection of Real-world Fights in Surveillance Videos},
year = {2019} }
TY - EJOUR
T1 - Detection of Real-world Fights in Surveillance Videos
AU - Mauricio Perez; Alex C. Kot; Anderson Rocha
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3994
ER -
Mauricio Perez, Alex C. Kot, Anderson Rocha. (2019). Detection of Real-world Fights in Surveillance Videos. IEEE SigPort. http://sigport.org/3994
Mauricio Perez, Alex C. Kot, Anderson Rocha, 2019. Detection of Real-world Fights in Surveillance Videos. Available at: http://sigport.org/3994.
Mauricio Perez, Alex C. Kot, Anderson Rocha. (2019). "Detection of Real-world Fights in Surveillance Videos." Web.
1. Mauricio Perez, Alex C. Kot, Anderson Rocha. Detection of Real-world Fights in Surveillance Videos [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3994

A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION


While significant work has been conducted to perform source cam- era model identification for images, little work has been done specif- ically for video camera model identification. This is problematic because different forensic traces may be left in digital images and videos captured by the same camera. As our experiments in this paper will show, a system trained to perform camera model identifi- cation for images yields unacceptably low performance when given video frames from the same cameras. To overcome this problem, new systems for identifying a videos source must be developed.

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Authors:
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm
Submitted On:
27 March 2019 - 9:03am
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[1] B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm, "A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/3856. Accessed: Jul. 23, 2019.
@article{3856-19,
url = {http://sigport.org/3856},
author = {B. Hosler; O. Mayer; B. Bayar; X. Zhao; C. Chen; J. A. Shackleford; M. C. Stamm },
publisher = {IEEE SigPort},
title = {A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION},
year = {2019} }
TY - EJOUR
T1 - A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION
AU - B. Hosler; O. Mayer; B. Bayar; X. Zhao; C. Chen; J. A. Shackleford; M. C. Stamm
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/3856
ER -
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm. (2019). A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION. IEEE SigPort. http://sigport.org/3856
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm, 2019. A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION. Available at: http://sigport.org/3856.
B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm. (2019). "A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION." Web.
1. B. Hosler, O. Mayer, B. Bayar, X. Zhao, C. Chen, J. A. Shackleford, M. C. Stamm. A VIDEO CAMERA MODEL IDENTIFICATION SYSTEM USING DEEP LEARNING AND FUSION [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/3856

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