  Deep Neural Networks have been successfully used for the task of Visual
Question Answering for the past few years owing to the availability of relevant
large scale datasets. However these datasets are created in artificial settings
and rarely reflect the real world scenario. Recent research effectively applies
these VQA models for answering visual questions for the blind. Despite
achieving high accuracy these models appear to be susceptible to variation in
input questions.We analyze popular VQA models through the lens of attribution
(input's influence on predictions) to gain valuable insights. Further, We use
these insights to craft adversarial attacks which inflict significant damage to
these systems with negligible change in meaning of the input questions. We
believe this will enhance development of systems more robust to the possible
variations in inputs when deployed to assist the visually impaired.
