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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.

double_RQ.pdf

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Authors:
Matthew C. Stamm
Submitted On:
23 February 2016 - 1:44pm
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double_RQ.pdf

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[1] Matthew C. Stamm, "Information Theoretical Limit of Operation Forensics", IEEE SigPort, 2015. [Online]. Available: http://sigport.org/147. Accessed: Dec. 17, 2017.
@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: Dec. 17, 2017.
@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

Median Filtering Forensics Based on Discriminative Multi-Scale Sparse Coding

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14 November 2017 - 10:08pm
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Median Filtering Forensics1.pdf

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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: Dec. 17, 2017.
@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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polimi_prnu-removal_icip17.pdf

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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: Dec. 17, 2017.
@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.

Hybrid.pdf

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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: Dec. 17, 2017.
@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: Dec. 17, 2017.
@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: Dec. 17, 2017.
@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

Copy move forgery detection with similar but genuine objects


Copy-Move Forgery Detection (CMFD) is a well-studied
image forensics problem. However, CMFD with Similar but
Genuine Objects (SGO) has received relatively less attention.
Recently, it has been found that current state-of-the-art
CFMD techniques are mostly inadequate in satisfactorily
solving this important problem variant. In this paper, we have
addressed this issue by using Rotated Local Binary Pattern
(RLBP) based rotation-invariant texture features, followed
by Generalized Two Nearest Neighbourhood (g2NN) based

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Authors:
Aniket Roy, Akhil Konda, Rajat Subhra Chakraborty
Submitted On:
13 September 2017 - 6:14pm
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ICIP_Poster.pdf

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[1] Aniket Roy, Akhil Konda, Rajat Subhra Chakraborty, "Copy move forgery detection with similar but genuine objects", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1982. Accessed: Dec. 17, 2017.
@article{1982-17,
url = {http://sigport.org/1982},
author = {Aniket Roy; Akhil Konda; Rajat Subhra Chakraborty },
publisher = {IEEE SigPort},
title = {Copy move forgery detection with similar but genuine objects},
year = {2017} }
TY - EJOUR
T1 - Copy move forgery detection with similar but genuine objects
AU - Aniket Roy; Akhil Konda; Rajat Subhra Chakraborty
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1982
ER -
Aniket Roy, Akhil Konda, Rajat Subhra Chakraborty. (2017). Copy move forgery detection with similar but genuine objects. IEEE SigPort. http://sigport.org/1982
Aniket Roy, Akhil Konda, Rajat Subhra Chakraborty, 2017. Copy move forgery detection with similar but genuine objects. Available at: http://sigport.org/1982.
Aniket Roy, Akhil Konda, Rajat Subhra Chakraborty. (2017). "Copy move forgery detection with similar but genuine objects." Web.
1. Aniket Roy, Akhil Konda, Rajat Subhra Chakraborty. Copy move forgery detection with similar but genuine objects [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1982

NEAR-DUPLICATE VIDEO DETECTION EXPLOITING NOISE RESIDUAL TRACES


Video phylogeny research about joint analysis of correlated video sequences has shown the possibility of developing interesting forensic applications. As an example, it is possible to study the provenance of near-duplicate (ND) video sequences, i.e., videos generated from the same original one through content preserving transformations. To perform this kind of analysis, accurate detection of ND videos is paramount. In this paper, we propose an algorithm for ND video detection and clustering in a challenging setup.

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Authors:
Silvia Lameri, Luca Bondi, Paolo Bestagini, Stefano Tubaro
Submitted On:
19 September 2017 - 5:25am
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[1] Silvia Lameri, Luca Bondi, Paolo Bestagini, Stefano Tubaro, "NEAR-DUPLICATE VIDEO DETECTION EXPLOITING NOISE RESIDUAL TRACES", IEEE SigPort, 2017. [Online]. Available: http://sigport.org/1843. Accessed: Dec. 17, 2017.
@article{1843-17,
url = {http://sigport.org/1843},
author = {Silvia Lameri; Luca Bondi; Paolo Bestagini; Stefano Tubaro },
publisher = {IEEE SigPort},
title = {NEAR-DUPLICATE VIDEO DETECTION EXPLOITING NOISE RESIDUAL TRACES},
year = {2017} }
TY - EJOUR
T1 - NEAR-DUPLICATE VIDEO DETECTION EXPLOITING NOISE RESIDUAL TRACES
AU - Silvia Lameri; Luca Bondi; Paolo Bestagini; Stefano Tubaro
PY - 2017
PB - IEEE SigPort
UR - http://sigport.org/1843
ER -
Silvia Lameri, Luca Bondi, Paolo Bestagini, Stefano Tubaro. (2017). NEAR-DUPLICATE VIDEO DETECTION EXPLOITING NOISE RESIDUAL TRACES. IEEE SigPort. http://sigport.org/1843
Silvia Lameri, Luca Bondi, Paolo Bestagini, Stefano Tubaro, 2017. NEAR-DUPLICATE VIDEO DETECTION EXPLOITING NOISE RESIDUAL TRACES. Available at: http://sigport.org/1843.
Silvia Lameri, Luca Bondi, Paolo Bestagini, Stefano Tubaro. (2017). "NEAR-DUPLICATE VIDEO DETECTION EXPLOITING NOISE RESIDUAL TRACES." Web.
1. Silvia Lameri, Luca Bondi, Paolo Bestagini, Stefano Tubaro. NEAR-DUPLICATE VIDEO DETECTION EXPLOITING NOISE RESIDUAL TRACES [Internet]. IEEE SigPort; 2017. Available from : http://sigport.org/1843

SP Cup 2016 Purdue Report


We report a multi-harmonic histogram method for extracting and analyzing electric network frequency (ENF) signals to identify power grids. Given a voltage-time measurement of a power grid with a base frequency f0, we compute the ENF signals at multiple harmonic locations f_0 and extract (i) a histogram of the magnitudes of the ENF; (ii) a histogram of the signal power and noise power surrounding the ENF; (iii) a histogram of the signal-to noise-ratio (SNR) of the ENF.

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29 September 2016 - 11:29am
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SPCup2016_Purdue.pdf

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[1] , "SP Cup 2016 Purdue Report", IEEE SigPort, 2016. [Online]. Available: http://sigport.org/1147. Accessed: Dec. 17, 2017.
@article{1147-16,
url = {http://sigport.org/1147},
author = { },
publisher = {IEEE SigPort},
title = {SP Cup 2016 Purdue Report},
year = {2016} }
TY - EJOUR
T1 - SP Cup 2016 Purdue Report
AU -
PY - 2016
PB - IEEE SigPort
UR - http://sigport.org/1147
ER -
. (2016). SP Cup 2016 Purdue Report. IEEE SigPort. http://sigport.org/1147
, 2016. SP Cup 2016 Purdue Report. Available at: http://sigport.org/1147.
. (2016). "SP Cup 2016 Purdue Report." Web.
1. . SP Cup 2016 Purdue Report [Internet]. IEEE SigPort; 2016. Available from : http://sigport.org/1147

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