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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, 2018.
@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.

double_AQ.pdf

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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, 2018.
@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

LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS


Information about an image's source camera model is important knowledge in many forensic investigations. In this paper we propose a system that compares two image patches to determine if they were captured by the same camera model. To do this, we first train a CNN based feature extractor to output generic, high level features which encode information about the source camera model of an image patch. Then, we learn a similarity measure that maps pairs of these features to a score indicating whether the two image patches were captured by the same or different camera models.

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Authors:
Owen Mayer, Mathew C. Stamm
Submitted On:
27 April 2018 - 12:45pm
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[1] Owen Mayer, Mathew C. Stamm, "LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3185. Accessed: Jul. 23, 2018.
@article{3185-18,
url = {http://sigport.org/3185},
author = {Owen Mayer; Mathew C. Stamm },
publisher = {IEEE SigPort},
title = {LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS},
year = {2018} }
TY - EJOUR
T1 - LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS
AU - Owen Mayer; Mathew C. Stamm
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3185
ER -
Owen Mayer, Mathew C. Stamm. (2018). LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS. IEEE SigPort. http://sigport.org/3185
Owen Mayer, Mathew C. Stamm, 2018. LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS. Available at: http://sigport.org/3185.
Owen Mayer, Mathew C. Stamm. (2018). "LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS." Web.
1. Owen Mayer, Mathew C. Stamm. LEARNED FORENSIC SOURCE SIMILARITY FOR UNKNOWN CAMERA MODELS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3185

AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION

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Authors:
Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna
Submitted On:
21 April 2018 - 8:37am
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[1] Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna, "AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3122. Accessed: Jul. 23, 2018.
@article{3122-18,
url = {http://sigport.org/3122},
author = {Sharad Joshi; Mohit Lamba; Vivek Goyal; Nitin Khanna },
publisher = {IEEE SigPort},
title = {AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION},
year = {2018} }
TY - EJOUR
T1 - AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION
AU - Sharad Joshi; Mohit Lamba; Vivek Goyal; Nitin Khanna
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3122
ER -
Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna. (2018). AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION. IEEE SigPort. http://sigport.org/3122
Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna, 2018. AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION. Available at: http://sigport.org/3122.
Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna. (2018). "AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION." Web.
1. Sharad Joshi, Mohit Lamba, Vivek Goyal, Nitin Khanna. AUGMENTED DATA AND IMPROVED NOISE RESIDUAL-BASED CNN FOR PRINTER SOURCE IDENTIFICATION [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3122

A ROTATION-INVARIANT CONVOLUTIONAL NEURAL NETWORK FOR IMAGE ENHANCEMENT FORENSICS

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Authors:
Yifang Chen, Zixian Lyu, Xiangui Kang, Z. Jane Wang
Submitted On:
13 April 2018 - 9:57pm
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[1] Yifang Chen, Zixian Lyu, Xiangui Kang, Z. Jane Wang, "A ROTATION-INVARIANT CONVOLUTIONAL NEURAL NETWORK FOR IMAGE ENHANCEMENT FORENSICS", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2780. Accessed: Jul. 23, 2018.
@article{2780-18,
url = {http://sigport.org/2780},
author = {Yifang Chen; Zixian Lyu; Xiangui Kang; Z. Jane Wang },
publisher = {IEEE SigPort},
title = {A ROTATION-INVARIANT CONVOLUTIONAL NEURAL NETWORK FOR IMAGE ENHANCEMENT FORENSICS},
year = {2018} }
TY - EJOUR
T1 - A ROTATION-INVARIANT CONVOLUTIONAL NEURAL NETWORK FOR IMAGE ENHANCEMENT FORENSICS
AU - Yifang Chen; Zixian Lyu; Xiangui Kang; Z. Jane Wang
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2780
ER -
Yifang Chen, Zixian Lyu, Xiangui Kang, Z. Jane Wang. (2018). A ROTATION-INVARIANT CONVOLUTIONAL NEURAL NETWORK FOR IMAGE ENHANCEMENT FORENSICS. IEEE SigPort. http://sigport.org/2780
Yifang Chen, Zixian Lyu, Xiangui Kang, Z. Jane Wang, 2018. A ROTATION-INVARIANT CONVOLUTIONAL NEURAL NETWORK FOR IMAGE ENHANCEMENT FORENSICS. Available at: http://sigport.org/2780.
Yifang Chen, Zixian Lyu, Xiangui Kang, Z. Jane Wang. (2018). "A ROTATION-INVARIANT CONVOLUTIONAL NEURAL NETWORK FOR IMAGE ENHANCEMENT FORENSICS." Web.
1. Yifang Chen, Zixian Lyu, Xiangui Kang, Z. Jane Wang. A ROTATION-INVARIANT CONVOLUTIONAL NEURAL NETWORK FOR IMAGE ENHANCEMENT FORENSICS [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2780

Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding

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14 November 2017 - 10:08pm
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[1] , "Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2325. Accessed: Jul. 23, 2018.
@article{2325-17,
url = {http://sigport.org/2325},
author = { },
publisher = {IEEE SigPort},
title = {Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding},
year = {2017} }
TY - EJOUR
T1 - Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding
AU -
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2325
ER -
. (2017). Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding. IEEE SigPort. http://sigport.org/2325
, 2017. Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding. Available at: http://sigport.org/2325.
. (2017). "Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding." Web.
1. . Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2325

INPAINTING-BASED CAMERA ANONYMIZATION


Over the years, the forensic community has developed a series of very accurate camera attribution algorithms enabling to detect which device has been used to acquire an image with outstanding results. Many of these methods are based on photo response non uniformity (PRNU) that allows tracing back a picture to the camera used to shoot it. However, when privacy is required, it would be desirable to anonymize photos, unlinking them from their specific device. This paper investigates a new and alternative approach to image anonymization task.

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Authors:
Sara Mandelli, Luca Bondi, Silvia Lameri, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro
Submitted On:
19 September 2017 - 5:48am
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[1] Sara Mandelli, Luca Bondi, Silvia Lameri, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro, "INPAINTING-BASED CAMERA ANONYMIZATION", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2226. Accessed: Jul. 23, 2018.
@article{2226-17,
url = {http://sigport.org/2226},
author = {Sara Mandelli; Luca Bondi; Silvia Lameri; Vincenzo Lipari; Paolo Bestagini; Stefano Tubaro },
publisher = {IEEE SigPort},
title = {INPAINTING-BASED CAMERA ANONYMIZATION},
year = {2017} }
TY - EJOUR
T1 - INPAINTING-BASED CAMERA ANONYMIZATION
AU - Sara Mandelli; Luca Bondi; Silvia Lameri; Vincenzo Lipari; Paolo Bestagini; Stefano Tubaro
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2226
ER -
Sara Mandelli, Luca Bondi, Silvia Lameri, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro. (2017). INPAINTING-BASED CAMERA ANONYMIZATION. IEEE SigPort. http://sigport.org/2226
Sara Mandelli, Luca Bondi, Silvia Lameri, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro, 2017. INPAINTING-BASED CAMERA ANONYMIZATION. Available at: http://sigport.org/2226.
Sara Mandelli, Luca Bondi, Silvia Lameri, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro. (2017). "INPAINTING-BASED CAMERA ANONYMIZATION." Web.
1. Sara Mandelli, Luca Bondi, Silvia Lameri, Vincenzo Lipari, Paolo Bestagini, Stefano Tubaro. INPAINTING-BASED CAMERA ANONYMIZATION [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2226

FAST CAMERA FINGERPRINT MATCHING IN VERY LARGE DATABASES


Given a query image or video, or a known camera fingerprint, there is a lack of capabilities for fast identification of media, from a large repository of images and videos, that match the query fingerprint.
This work introduces a new approach that improves the computation efficiency of pairwise camera fingerprint matching and incorporates group testing to make the search more effective.

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Authors:
Taha Sencar, Sevinc Bayram, Nasir Memon
Submitted On:
17 September 2017 - 2:35am
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[1] Taha Sencar, Sevinc Bayram, Nasir Memon, "FAST CAMERA FINGERPRINT MATCHING IN VERY LARGE DATABASES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2210. Accessed: Jul. 23, 2018.
@article{2210-17,
url = {http://sigport.org/2210},
author = {Taha Sencar; Sevinc Bayram; Nasir Memon },
publisher = {IEEE SigPort},
title = {FAST CAMERA FINGERPRINT MATCHING IN VERY LARGE DATABASES},
year = {2017} }
TY - EJOUR
T1 - FAST CAMERA FINGERPRINT MATCHING IN VERY LARGE DATABASES
AU - Taha Sencar; Sevinc Bayram; Nasir Memon
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2210
ER -
Taha Sencar, Sevinc Bayram, Nasir Memon. (2017). FAST CAMERA FINGERPRINT MATCHING IN VERY LARGE DATABASES. IEEE SigPort. http://sigport.org/2210
Taha Sencar, Sevinc Bayram, Nasir Memon, 2017. FAST CAMERA FINGERPRINT MATCHING IN VERY LARGE DATABASES. Available at: http://sigport.org/2210.
Taha Sencar, Sevinc Bayram, Nasir Memon. (2017). "FAST CAMERA FINGERPRINT MATCHING IN VERY LARGE DATABASES." Web.
1. Taha Sencar, Sevinc Bayram, Nasir Memon. FAST CAMERA FINGERPRINT MATCHING IN VERY LARGE DATABASES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2210

RESIDUAL-BASED FORENSIC COMPARISON OF VIDEO SEQUENCES

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Authors:
Davide Cozzolino, Luisa Verdoliva, Christian Riess
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14 September 2017 - 2:29pm
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Detecting or localizating splices in videos with help of noise residuals

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[1] Davide Cozzolino, Luisa Verdoliva, Christian Riess, "RESIDUAL-BASED FORENSIC COMPARISON OF VIDEO SEQUENCES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2043. Accessed: Jul. 23, 2018.
@article{2043-17,
url = {http://sigport.org/2043},
author = {Davide Cozzolino; Luisa Verdoliva; Christian Riess },
publisher = {IEEE SigPort},
title = {RESIDUAL-BASED FORENSIC COMPARISON OF VIDEO SEQUENCES},
year = {2017} }
TY - EJOUR
T1 - RESIDUAL-BASED FORENSIC COMPARISON OF VIDEO SEQUENCES
AU - Davide Cozzolino; Luisa Verdoliva; Christian Riess
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2043
ER -
Davide Cozzolino, Luisa Verdoliva, Christian Riess. (2017). RESIDUAL-BASED FORENSIC COMPARISON OF VIDEO SEQUENCES. IEEE SigPort. http://sigport.org/2043
Davide Cozzolino, Luisa Verdoliva, Christian Riess, 2017. RESIDUAL-BASED FORENSIC COMPARISON OF VIDEO SEQUENCES. Available at: http://sigport.org/2043.
Davide Cozzolino, Luisa Verdoliva, Christian Riess. (2017). "RESIDUAL-BASED FORENSIC COMPARISON OF VIDEO SEQUENCES." Web.
1. Davide Cozzolino, Luisa Verdoliva, Christian Riess. RESIDUAL-BASED FORENSIC COMPARISON OF VIDEO SEQUENCES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2043

A Consistent Two-Level Metric for Evaluation of Automated Abandoned Object Detection Methods


Scientific interest in automated abandoned object detection algorithms using visual information is high and many related systems have been published in recent years. However, most evaluation techniques rely only on statistical evaluation on the object level.

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Authors:
Patrick Krusch, Erik Bochinski, Volker Eiselein, Thomas Sikora
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14 September 2017 - 8:08am
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[1] Patrick Krusch, Erik Bochinski, Volker Eiselein, Thomas Sikora, "A Consistent Two-Level Metric for Evaluation of Automated Abandoned Object Detection Methods", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/2021. Accessed: Jul. 23, 2018.
@article{2021-17,
url = {http://sigport.org/2021},
author = {Patrick Krusch; Erik Bochinski; Volker Eiselein; Thomas Sikora },
publisher = {IEEE SigPort},
title = {A Consistent Two-Level Metric for Evaluation of Automated Abandoned Object Detection Methods},
year = {2017} }
TY - EJOUR
T1 - A Consistent Two-Level Metric for Evaluation of Automated Abandoned Object Detection Methods
AU - Patrick Krusch; Erik Bochinski; Volker Eiselein; Thomas Sikora
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/2021
ER -
Patrick Krusch, Erik Bochinski, Volker Eiselein, Thomas Sikora. (2017). A Consistent Two-Level Metric for Evaluation of Automated Abandoned Object Detection Methods. IEEE SigPort. http://sigport.org/2021
Patrick Krusch, Erik Bochinski, Volker Eiselein, Thomas Sikora, 2017. A Consistent Two-Level Metric for Evaluation of Automated Abandoned Object Detection Methods. Available at: http://sigport.org/2021.
Patrick Krusch, Erik Bochinski, Volker Eiselein, Thomas Sikora. (2017). "A Consistent Two-Level Metric for Evaluation of Automated Abandoned Object Detection Methods." Web.
1. Patrick Krusch, Erik Bochinski, Volker Eiselein, Thomas Sikora. A Consistent Two-Level Metric for Evaluation of Automated Abandoned Object Detection Methods [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/2021

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