| Paper ID | IFS-1.5 |
| Paper Title |
FORENSICABILITY OF DEEP NEURAL NETWORK INFERENCE PIPELINES |
| Authors |
Alexander Schlögl, Tobias Kupek, Rainer Böhme, University of Innsbruck, Austria |
| Session | IFS-1: Multimedia Forensics 1 |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Information Forensics and Security: [MMF] Multimedia Forensics |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
We propose methods to infer properties of the execution envi-ronment of machine learning pipelines by tracing characteris-tic numerical deviations in observable outputs. Results from aseries of proof-of-concept experiments obtained on local andcloud-hosted machines give raise to possible forensic applica-tions, such as the identification of the hardware platform usedto produce deep neural network predictions. Finally, we intro-duce boundary samples that amplify the numerical deviationsin order to distinguish machines by their predicted label only. |
