I am a Postdoctoral Researcher with Drexel University's Multimedia and Information Security Lab (MISL).
Since digital content is easy to create, manipulate and disseminate, we are often left wondering "Who created this content?" and "Can I trust it?" Through my research, I create tools that answer such questions. By making these tools publicly available, we help to provide integrity to the digital information that is important for a modern society.
You can view my CV here.
O. Mayer, M.C. Stamm. Exposing Fake Images with Forensic Similarity Graphs. under review 2020, (Website)
O. Mayer, B. Bayar, M.C. Stamm. Learning Unified Deep-Features for Multiple Forensic Tasks. ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) 2018
O. Mayer, M.C. Stamm. Learned forensic source similarity for unknown camera models. Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2018
O. Mayer, M.C. Stamm. Accurate and efficient forgery detection using lateral chromatic aberration. IEEE Transactions on Information Forensics and Security (TIFS) 2018, doi: 10.1109/TIFS.2018.2799421
O. Mayer, M.C. Stamm. Improved forgery detection with chromatic aberration. Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2016, doi: 10.1109/ICASSP.2016.7472032
O. Mayer, M.C. Stamm. Countering anti-forensics of chromatic aberration. ACM Workshop on Information Hiding and Multimedia Security (IH&MMSec) 2017 doi: 10.1145/3082031.3083242
T.A. Abbot, V.E. Premus, P.A. Abbot, O.A. Mayer. "Receiver operating characteristic for a spectrogram correlator-based humpback whale detector-classifier." The Journal of the Acoustical Society of America, 2012
T. Abbot, O. Mayer, V. Premus, P. Abbot, I. Dyer. "Analysis of most prominent signal features of humpback whale (Megaptera Novaeangliae) vocalizations towards the goal of autonomous acoustic classification." The Journal of the Acoustical Society of America, 2009