A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement. In other words, a CF explainer computes the minimum modifications required to cross the model's decision boundary. Current deep generative CF models often work with user-selected features rather than focusing on the discriminative features of the black-box model. Consequently, such CF examples may not necessarily lie near the decision boundary, thereby contradicting the definition of CFs. To address this issue, we propose in this paper a novel approach that leverages saliency maps to generate more informative CF explanations. Source codes are available at: https://github.com/Amir-Samadi//Saliency_Aware_CF.

翻译：对抗性解释器能够识别输入中最小的修改量，使模型输出变为其补集。换言之，对抗性解释器计算跨越模型决策边界所需的最小修改量。当前的深度生成对抗性模型通常使用用户选择的特征，而非关注黑盒模型的判别性特征，因此生成的对抗性示例可能不邻近决策边界，这与对抗性的定义相矛盾。为解决此问题，本文提出一种利用显著性图生成更具信息量的对抗性解释的新方法。源代码见：https://github.com/Amir-Samadi//Saliency_Aware_CF。