  Google's Vision API analyses images and provides a variety of output
predictions, one such type is context-based labelling. In this paper, it is
shown that adversarial examples that cause incorrect label prediction and
spoofing can be generated by rotating the images. Due to the black-boxed nature
of the API, a modular context-based pre-processing pipeline is proposed
consisting of a Res-Net50 model, that predicts the angle by which the image
must be rotated to correct its orientation. The pipeline successfully performs
the correction whilst maintaining the image's resolution and feeds it to the
API which generates labels similar to the original correctly oriented image and
using a Percentage Error metric, the performance of the corrected images as
compared to its rotated counter-parts is found to be significantly higher.
These observations imply that the API can benefit from such a pre-processing
pipeline to increase robustness to rotational perturbances.
