  In this paper, we dive into the reliability concerns of Integrated Gradients
(IG), a prevalent feature attribution method for black-box deep learning
models. We particularly address two predominant challenges associated with IG:
the generation of noisy feature visualizations for vision models and the
vulnerability to adversarial attributional attacks. Our approach involves an
adaptation of path-based feature attribution, aligning the path of attribution
more closely to the intrinsic geometry of the data manifold. Our experiments
utilise deep generative models applied to several real-world image datasets.
They demonstrate that IG along the geodesics conforms to the curved geometry of
the Riemannian data manifold, generating more perceptually intuitive
explanations and, subsequently, substantially increasing robustness to targeted
attributional attacks.
