When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks
This work addresses the problem of explainable visual reasoning for researchers and practitioners in AI, offering incremental improvements by combining existing techniques like causal intervention and adversarial examples.
The authors tackled the challenge of understanding causality in deep neural networks by proposing a causal inference framework using do-calculus, which integrates pixel-wise masking and adversarial perturbation to compute causal effects. Experimental results on the Chest X-Ray-14 dataset showed that causal effects are a competitive and robust index compared to conventional methods like class-activation mappings, and they hold promise for detecting adversarial examples.
Discovering and exploiting the causality in deep neural networks (DNNs) are crucial challenges for understanding and reasoning causal effects (CE) on an explainable visual model. "Intervention" has been widely used for recognizing a causal relation ontologically. In this paper, we propose a causal inference framework for visual reasoning via do-calculus. To study the intervention effects on pixel-level features for causal reasoning, we introduce pixel-wise masking and adversarial perturbation. In our framework, CE is calculated using features in a latent space and perturbed prediction from a DNN-based model. We further provide the first look into the characteristics of discovered CE of adversarially perturbed images generated by gradient-based methods \footnote{~~https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvImg}. Experimental results show that CE is a competitive and robust index for understanding DNNs when compared with conventional methods such as class-activation mappings (CAMs) on the Chest X-Ray-14 dataset for human-interpretable feature(s) (e.g., symptom) reasoning. Moreover, CE holds promises for detecting adversarial examples as it possesses distinct characteristics in the presence of adversarial perturbations.