  In the realm of Artificial Intelligence (AI), the importance of Explainable
Artificial Intelligence (XAI) is increasingly recognized, particularly as AI
models become more integral to our lives. One notable single-instance XAI
approach is counterfactual explanation, which aids users in comprehending a
model's decisions and offers guidance on altering these decisions. Specifically
in the context of image classification models, effective image counterfactual
explanations can significantly enhance user understanding. This paper
introduces a novel method for computing feature importance within the feature
space of a black-box model. By employing information fusion techniques, our
method maximizes the use of data to address feature counterfactual explanations
in the feature space. Subsequently, we utilize an image generation model to
transform these feature counterfactual explanations into image counterfactual
explanations. Our experiments demonstrate that the counterfactual explanations
generated by our method closely resemble the original images in both pixel and
feature spaces. Additionally, our method outperforms established baselines,
achieving impressive experimental results.
