Distribution shift
Definition
In machine learning, distribution shift occurs when the statistical characteristics of data the model encounters in deployment differ from those of the data it was trained on. For deepfake detectors, training on one generation of generative models and deploying against a newer generation is a common and serious form of distribution shift.
Related terms
- C2PA
- Coalition for Content Provenance and Authenticity. A cross-industry group that has published an open technical specification for embedding cryptographically signed provenance manifests...
- Daubert standard
- The US federal evidentiary standard (Daubert v. Merrell Dow Pharmaceuticals, 1993) requiring that expert testimony be based on scientifically valid methods with...
- ISO/IEC 17025
- The international standard for testing and calibration laboratories, published jointly by the International Organization for Standardization and the International Electrotechnical Commission. It...
- NIST CFTT
- The National Institute of Standards and Technology Computer Forensics Tool Testing programme. It publishes independent test reports for digital forensic tools, including...
- SWGDE
- Scientific Working Group for Digital Evidence. A US multi-agency body that publishes consensus best-practice documents for digital forensic disciplines, including image authentication,...
Explained in
- Standards and Validation Frameworks in Media ForensicsIn machine learning, distribution shift occurs when the statistical characteristics of data the model encounters in deployment differ from those of the data it...