  Counterfactual explanations have shown promising results as a post-hoc
framework to make image classifiers more explainable. In this paper, we propose
DiME, a method allowing the generation of counterfactual images using the
recent diffusion models. By leveraging the guided generative diffusion process,
our proposed methodology shows how to use the gradients of the target
classifier to generate counterfactual explanations of input instances. Further,
we analyze current approaches to evaluate spurious correlations and extend the
evaluation measurements by proposing a new metric: Correlation Difference. Our
experimental validations show that the proposed algorithm surpasses previous
State-of-the-Art results on 5 out of 6 metrics on CelebA.
