  Explainable Artificial Intelligence (XAI) has in recent years become a
well-suited framework to generate human understandable explanations of black
box models. In this paper, we present a novel XAI visual explanation algorithm
denoted SIDU that can effectively localize entire object regions responsible
for prediction in a full extend. We analyze its robustness and effectiveness
through various computational and human subject experiments. In particular, we
assess the SIDU algorithm using three different types of evaluations
(Application, Human and Functionally-Grounded) to demonstrate its superior
performance. The robustness of SIDU is further studied in presence of
adversarial attack on black box models to better understand its performance.
