  It is a growing direction to utilize unintended memorization in ML models to
benefit real-world applications, with recent efforts like user auditing,
dataset ownership inference and forgotten data measurement. Standing on the
point of ML model development, we introduce a process named data origin
inference, to assist ML developers in locating missed or faulty data origin in
training set without maintaining strenuous metadata. We formally define the
data origin and the data origin inference task in the development of the ML
model (mainly neural networks). Then we propose a novel inference strategy
combining embedded-space multiple instance classification and shadow training.
Diverse use cases cover language, visual and structured data, with various
kinds of data origin (e.g. business, county, movie, mobile user, text author).
A comprehensive performance analysis of our proposed strategy contains
referenced target model layers, available testing data for each origin, and in
shadow training, the implementations of feature extraction as well as shadow
models. Our best inference accuracy achieves 98.96% in the language use case
when the target model is a transformer-based deep neural network. Furthermore,
we give a statistical analysis of different kinds of data origin to investigate
what kind of origin is probably to be inferred correctly.
