When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks

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.Comment: Noted our camera-ready version has changed the title. "When Causal Intervention Meets Adversarial Examples and Image Masking for Deep Neural Networks" as the v3 official paper title in IEEE Proceeding. Please use it in your formal reference. Accepted at IEEE ICIP 2019. Pytorch code has released on https://github.com/jjaacckkyy63/Causal-Intervention-AE-wAdvIm

Auto-adhesive transdermal drug delivery patches using beetle inspired micropillar structures

The patch described in this paper combines the principles of wet adhesion, which is a widely adopted biological adhesion system in nature, with transdermal drug delivery. A biologically inspired micropillar patch was fabricated that is self-adhesive, reusable, and can sustain a controlled drug release. We successfully preloaded the commercial non-steroidal anti-inflammatory generic drug unguents indomethacin, ketoprofen, diclofenac sodium and etofenamate into a polydimethylsiloxane elastomeric matrix and fabricated drug-containing micropillar patches. When examining the drug release kinetics and friction of the patches, we observed that these drug unguents can be released calculably and regularly for several days. Additionally, the drug unguents released from the patch to its attached surface are critical to increase the strength of the patch's adhesion, which is based on capillary attractive forces and is inspired by beetle feet. Here, we create a novel system combining biomimetics and drug delivery that can be modified for use across the biomedical and engineering spectra. Motivation: the objective of the present study was to characterize a micropillar PDMS patch that was inspired by a beetle's wet adhesion as a platform for conducting in vitro release studies. Commercially available non-steroid anti-inflammatory drugs (NSAIDs) were used as the model drugs for our delivery systems. An emphasis was put on quantitatively evaluating the drug release and friction manifestation of these patches