  Explainable Artificial Intelligence (XAI) strategies play a crucial part in
increasing the understanding and trustworthiness of neural networks.
Nonetheless, these techniques could potentially generate misleading
explanations. Blinding attacks can drastically alter a machine learning
algorithm's prediction and explanation, providing misleading information by
adding visually unnoticeable artifacts into the input, while maintaining the
model's accuracy. It poses a serious challenge in ensuring the reliability of
XAI methods. To ensure the reliability of XAI methods poses a real challenge,
we leverage statistical analysis to highlight the changes in CNN weights within
a CNN following blinding attacks. We introduce a method specifically designed
to limit the effectiveness of such attacks during the evaluation phase,
avoiding the need for extra training. The method we suggest defences against
most modern explanation-aware adversarial attacks, achieving an approximate
decrease of ~99\% in the Attack Success Rate (ASR) and a ~91\% reduction in the
Mean Square Error (MSE) between the original explanation and the defended
(post-attack) explanation across three unique types of attacks.
