  Machine Learning (ML) algorithms are susceptible to adversarial attacks and
deception both during training and deployment. Automatic reverse engineering of
the toolchains behind these adversarial machine learning attacks will aid in
recovering the tools and processes used in these attacks. In this paper, we
present two techniques that support automated identification and attribution of
adversarial ML attack toolchains using Co-occurrence Pixel statistics and
Laplacian Residuals. Our experiments show that the proposed techniques can
identify parameters used to generate adversarial samples. To the best of our
knowledge, this is the first approach to attribute gradient based adversarial
attacks and estimate their parameters. Source code and data is available at:
https://github.com/michael-goebel/ei_red
