We hope you can join us on *Thursday, October 5th* at *12:30pm CET* for 
our next 1-hour webinar with *Andreas Uhl* on "Temporal Image Forensics: 
What do we actually learn in data driven approaches?"

[6:30 a.m. New-York] - [12:30 p.m. Paris] - [6:30 p.m. Beijing]

*To join the webinar, please RSVP 
here*:https://cassyni.com/events/4Y8bwmvKGH45dZ1XHUxzLd?cb=functi *
**If you wish to promote a EURASIP journal special issue, conference, 
event, or new image/video database at an upcoming webinar, please reply 
to this email with additional details.

*Title:*Temporal Image Forensics: What do we actually learn in data 
driven approaches?

*Abstract:*The assessment of the age of a given digital image, assuming 
that the EXIF data is not trustworthy, has been traditionally done by 
analyzing sensor defects which occur and accumulate over time. In recent 
work, deep-learning based technology has been increasingly used for the 
age assessment task, which raises the question, which image features are 
actually used by such techniques. Moreover, (public) datasets used in 
corresponding experimentation might be prone to content bias, such that 
instead of age groups, actually content groups are created. We will 
report on recent results on this topic also showing the importance of 
XAI techniques in this field.

*Bio:*Andreas Uhl is a full professor at the Department of Artificial 
Intelligence and Human Interfaces (AIHI), University of Salzburg, where 
he leads the Visual Computing and Security (VISEL) Lab. His research 
interests are in biometrics (with emphasis on visually recorded 
biometric traits), media forensics and medical imaging. He has 
significant interest in transdisciplinary research, e.g. in aquaculture 
monitoring, wood industry, and digital humanities.



[log in to unmask]

If you don't already have a password for the LISTSERV.ACM.ORG server, we recommend
that you create one now. A LISTSERV password is linked to your email
address and can be used to access the web interface and all the lists to
which you are subscribed on the LISTSERV.ACM.ORG server.

To create a password, visit:


Once you have created a password, you can log in and view or change your
subscription settings at: