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Deep Learning Techniques for Biometrics

Prof. Lior Wolf
University of Tel-Aviv, Israel

Similar to people identification through human fingerprint analysis, multimedia forensics through device fingerprint analysis has attracted great attention among scientists, practitioners and law enforcement agencies around the world in the past decade. Device information, such as model and serial number, stored in the EXIF are useful for identifying the devices responsible for the creation of the images in question. However, stored separately from the content, the metadata in the EXIF can be removed and manipulated at ease. Device fingerprints deposited in the content by the device provide a more reliable alternative to aid forensic investigations. The hardware or software of each stage in the digital image acquisition process leaves artifects in the content that can be used as device fingerprints to identify the source devices. This talk will start with a brief introduction to various types of device fingerprints, their applications and limitations. A more focused presentation on the application of sensor pattern noise, as a form of device fingerprint, to source camera identification, content-integrity verification and source-oriented image clustering will then be delivered.

 

CO-ORGANIZED BY

COST CA16101 - MULTI-FORESEE

COST CA16101 - MULTI-FORESEE

EU COST CA16101
MULTI-modal Imaging of FOREnsic SciEnce Evidence - tools for Forensic Science - MULTI-FORESEE

European Commission

COST is supported by the EU Framework Programme Horizon 2020



IDENTITY project

EU Horizon 2020
Project IDENTITY


PARTNERS AND SPONSORS

 

IAPR
IAPR Technical Committee on Biometrics (TC4)

 

IEEE

 

Eurasip
European Association for Signal Processing

 

CVPL

OT-MORPHO is now IDEMIA

EAB European Association for Biometrics

 

Biosecure

 

University of Sassari

 

Athena