2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDIFS-1.5
Paper Title FORENSICABILITY OF DEEP NEURAL NETWORK INFERENCE PIPELINES
Authors Alexander Schlögl, Tobias Kupek, Rainer Böhme, University of Innsbruck, Austria
SessionIFS-1: Multimedia Forensics 1
LocationGather.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
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.