  Despite the recent advancements in deploying neural networks for image
classification, it has been found that adversarial examples are able to fool
these models leading them to misclassify the images. Since these models are now
being widely deployed, we provide an insight on the threat of these adversarial
examples by evaluating their characteristics and transferability to more
complex models that utilize Image Classification as a subtask.
  We demonstrate the ineffectiveness of adversarial examples when applied to
Instance Segmentation & Object Detection models. We show that this
ineffectiveness arises from the inability of adversarial examples to withstand
transformations such as scaling or a change in lighting conditions. Moreover,
we show that there exists a small threshold below which the adversarial
property is retained while applying these input transformations.
  Additionally, these attacks demonstrate weak cross-network transferability
across neural network architectures, e.g. VGG16 and ResNet50, however, the
attack may fool both the networks if passed sequentially through networks
during its formation.
  The lack of scalability and transferability challenges the question of how
adversarial images would be effective in the real world.
