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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: Nov. 13, 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.

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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: Nov. 13, 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

The Impact of Exposure Settings in Digital Image Forensics


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Authors:
Wenhao Chen, Yangxiao Wang, Stephanie Reinder, Min Wu, Yong Guan, and Jennifer Newman
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23 October 2018 - 5:54am
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[1] Wenhao Chen, Yangxiao Wang, Stephanie Reinder, Min Wu, Yong Guan, and Jennifer Newman, "The Impact of Exposure Settings in Digital Image Forensics", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3668. Accessed: Nov. 13, 2018.
@article{3668-18,
url = {http://sigport.org/3668},
author = {Wenhao Chen; Yangxiao Wang; Stephanie Reinder; Min Wu; Yong Guan; and Jennifer Newman },
publisher = {IEEE SigPort},
title = {The Impact of Exposure Settings in Digital Image Forensics},
year = {2018} }
TY - EJOUR
T1 - The Impact of Exposure Settings in Digital Image Forensics
AU - Wenhao Chen; Yangxiao Wang; Stephanie Reinder; Min Wu; Yong Guan; and Jennifer Newman
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3668
ER -
Wenhao Chen, Yangxiao Wang, Stephanie Reinder, Min Wu, Yong Guan, and Jennifer Newman. (2018). The Impact of Exposure Settings in Digital Image Forensics. IEEE SigPort. http://sigport.org/3668
Wenhao Chen, Yangxiao Wang, Stephanie Reinder, Min Wu, Yong Guan, and Jennifer Newman, 2018. The Impact of Exposure Settings in Digital Image Forensics. Available at: http://sigport.org/3668.
Wenhao Chen, Yangxiao Wang, Stephanie Reinder, Min Wu, Yong Guan, and Jennifer Newman. (2018). "The Impact of Exposure Settings in Digital Image Forensics." Web.
1. Wenhao Chen, Yangxiao Wang, Stephanie Reinder, Min Wu, Yong Guan, and Jennifer Newman. The Impact of Exposure Settings in Digital Image Forensics [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3668

IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING


Fake news and deep fakes have been making social and mainstream media headlines. At the same time, engaged scientists strive for find- ing ways to detect forgeries and suspicious manipulations using even the subtlest clues. In this vein, this work proposes a new method for detecting photographic splicing by bringing together the high repre- sentation power of Illuminant Maps and Convolutional Neural Net- works as a way of learning directly from available training data the most important hints of a forgery.

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Authors:
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho
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7 October 2018 - 5:48am
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[1] Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho, "IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3588. Accessed: Nov. 13, 2018.
@article{3588-18,
url = {http://sigport.org/3588},
author = {Thales Pomari; Guillherme Ruppert; Edmar Rezende; Anderson Rocha; Tiago Carvalho },
publisher = {IEEE SigPort},
title = {IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING},
year = {2018} }
TY - EJOUR
T1 - IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING
AU - Thales Pomari; Guillherme Ruppert; Edmar Rezende; Anderson Rocha; Tiago Carvalho
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3588
ER -
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho. (2018). IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING. IEEE SigPort. http://sigport.org/3588
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho, 2018. IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING. Available at: http://sigport.org/3588.
Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho. (2018). "IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING." Web.
1. Thales Pomari, Guillherme Ruppert, Edmar Rezende, Anderson Rocha, Tiago Carvalho. IMAGE SPLICING DETECTION THROUGH ILLUMINATION INCONSISTENCIES AND DEEP LEARNING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3588

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

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Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
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7 October 2018 - 2:43am
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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3587. Accessed: Nov. 13, 2018.
@article{3587-18,
url = {http://sigport.org/3587},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3587
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3587
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3587.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3587

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

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Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
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7 October 2018 - 2:43am
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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3585. Accessed: Nov. 13, 2018.
@article{3585-18,
url = {http://sigport.org/3585},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3585
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3585
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3585.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3585

Towards Camera Identification From Cropped Query Images


PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation.

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Authors:
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon
Submitted On:
7 October 2018 - 2:43am
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[1] Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, "Towards Camera Identification From Cropped Query Images", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3584. Accessed: Nov. 13, 2018.
@article{3584-18,
url = {http://sigport.org/3584},
author = {Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon },
publisher = {IEEE SigPort},
title = {Towards Camera Identification From Cropped Query Images},
year = {2018} }
TY - EJOUR
T1 - Towards Camera Identification From Cropped Query Images
AU - Waheeb Yaqub; Manoranjan Mohanty; Nasir Memon
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3584
ER -
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). Towards Camera Identification From Cropped Query Images. IEEE SigPort. http://sigport.org/3584
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon, 2018. Towards Camera Identification From Cropped Query Images. Available at: http://sigport.org/3584.
Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. (2018). "Towards Camera Identification From Cropped Query Images." Web.
1. Waheeb Yaqub, Manoranjan Mohanty, Nasir Memon. Towards Camera Identification From Cropped Query Images [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3584

CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING


Detection of contrast adjustments in the presence of JPEG post processing is known to be a challenging task. JPEG post-processing is often applied innocently, as JPEG is the most common image format, or it may correspond to a laundering attack when it is purposely applied to erase the traces of manipulation. In this paper, we propose a CNN-based detector for generic contrast adjustment, which is robust to JPEG compression.

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Authors:
Mauro Barni, Andrea Costanzo, Ehsan Nowroozi, Benedetta Tondi
Submitted On:
4 October 2018 - 11:44am
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[1] Mauro Barni, Andrea Costanzo, Ehsan Nowroozi, Benedetta Tondi, "CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/3445. Accessed: Nov. 13, 2018.
@article{3445-18,
url = {http://sigport.org/3445},
author = {Mauro Barni; Andrea Costanzo; Ehsan Nowroozi; Benedetta Tondi },
publisher = {IEEE SigPort},
title = {CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING},
year = {2018} }
TY - EJOUR
T1 - CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING
AU - Mauro Barni; Andrea Costanzo; Ehsan Nowroozi; Benedetta Tondi
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/3445
ER -
Mauro Barni, Andrea Costanzo, Ehsan Nowroozi, Benedetta Tondi. (2018). CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING. IEEE SigPort. http://sigport.org/3445
Mauro Barni, Andrea Costanzo, Ehsan Nowroozi, Benedetta Tondi, 2018. CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING. Available at: http://sigport.org/3445.
Mauro Barni, Andrea Costanzo, Ehsan Nowroozi, Benedetta Tondi. (2018). "CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING." Web.
1. Mauro Barni, Andrea Costanzo, Ehsan Nowroozi, Benedetta Tondi. CNN-BASED DETECTION OF GENERIC CONTRAST ADJUSTMENT WITH JPEG POST-PROCESSING [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/3445

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: Nov. 13, 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: Nov. 13, 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

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