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Automated Image Metadata Verification

 
: Wittorf, Kyra; Steinebach, Martin; Liu, Huajian

:

Society for Imaging Science and Technology -IS&T-:
IS&T International Symposium on Electronic Imaging Science and Technology 2021. Media Watermarking, Security, and Forensics : January 19-28, 2021, Online
Springfield/Va.: IS&T, 2021
S.274/1-274/6
International Symposium on Electronic Imaging Science and Technology (IS&T) <2021, Online>
Englisch
Konferenzbeitrag
Fraunhofer SIT ()
image attribution; image verification; Open Source Intelligence

Abstract
Nowadays, almost everyone owns a device capable of photography. More and more photos are taken and distributed through the Internet. In the times of analogue photography, pictures were considered to be legally accepted evidence, but today, due to the multitude of possibilities to manipulate digital pictures, this is not necessarily the case. Metadata can provide information about the origin of the image. The prerequisite for this is that they have not been altered. This work shows possibilities how metadata can be extracted and verified. The additional meta information of an image and its standards are of central importance. We introduce a method of comparing metadata with the visual image content. For this purpose, we apply machine learning for automatically classifying information from the image. Finally, an exemplary verification of the metadata by means of the weather is carried out to provide a practical example of how the presented approach works. Based on this example and on the presented concept, verifiers for metadata that verify several aspects can be created in the future. These verifiers can help to detect forged metadata in a forensic investigation.

: http://publica.fraunhofer.de/dokumente/N-638764.html