Analyzing adversarial performance for image-based models

Augmentations-based adversarial analysis can also be applied to image-based models. The key here is to discover possible degradations of accuracy-based performance in original non-existent conditions in the validation dataset. Here are some examples of components that could be evaluated by augmentations for the image domain:

• Object of interest size: In use cases that use CCTV camera image input, adversarial analysis can help us set up the camera with an appropriate distance so that optimal performance can be achieved. The original image can be iteratively resized into various sizes and overlayed on top of a base black image to perform analysis.
• The roll orientation of the object of interest: Pitch and yaw orientation is not straightforward to augment. However, rotation augmentation can help stress test roll orientation performance. Optimal performance can be enforced by any pose orientation detection model or system...