  Often machine learning models tend to automatically learn associations
present in the training data without questioning their validity or
appropriateness. This undesirable property is the root cause of the
manifestation of spurious correlations, which render models unreliable and
prone to failure in the presence of distribution shifts. Research shows that
most methods attempting to remedy spurious correlations are only effective for
a model's known spurious associations. Current spurious correlation detection
algorithms either rely on extensive human annotations or are too restrictive in
their formulation. Moreover, they rely on strict definitions of visual
artifacts that may not apply to data produced by generative models, as they are
known to hallucinate contents that do not conform to standard specifications.
In this work, we introduce a general-purpose method that efficiently detects
potential spurious correlations, and requires significantly less human
interference in comparison to the prior art. Additionally, the proposed method
provides intuitive explanations while eliminating the need for pixel-level
annotations. We demonstrate the proposed method's tolerance to the peculiarity
of AI-generated images, which is a considerably challenging task, one where
most of the existing methods fall short. Consequently, our method is also
suitable for detecting spurious correlations that may propagate to downstream
applications originating from generative models.
