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.

通过适应路径影响力的方法，了解了集成梯度（IG）在黑盒深度学习模型中存在的可靠性问题，主要是生成针对视觉模型的嘈杂特征可视化和对敌对归属攻击的脆弱性。我们的实验使用深度生成模型应用于多个真实图像数据集，证明了IG沿测地线符合黎曼数据流形的曲线几何性质，生成更具感知直观性的解释，从而大大增加了针对归属攻击的鲁棒性。

流形综合梯度：特征归因的黎曼几何