Adversarial Doodles: Interpretable and Human-drawable Attacks Provide Describable Insights

Abstract

DNN-based image classification models are susceptible to adversarial attacks. Most previous adversarial attacks do not focus on the interpretability of the generated adversarial examples, and we cannot gain insights into the mechanism of the target classifier from the attacks. Therefore, we propose Adversarial Doodles, which have interpretable shapes. We optimize black b\'ezier curves to fool the target classifier by overlaying them onto the input image. By introducing random perspective transformation and regularizing the doodled area, we obtain compact attacks that cause misclassification even when humans replicate them by hand. Adversarial doodles provide describable and intriguing insights into the relationship between our attacks and the classifier's output. We utilize adversarial doodles and discover the bias inherent in the target classifier, such as "We add two strokes on its head, a triangle onto its body, and two lines inside the triangle on a bird image. Then, the classifier misclassifies the image as a butterfly.