Deep neural networks (DNNs) have achieved great success in image classification and recognition compared to previous methods. However, recent works have reported that DNNs are very vulnerable to adversarial examples that are intentionally generated to mislead the predictions of the DNNs. Here, we present a novel freehand sketch-based natural-looking adversarial example generator that we call SketchAdv. To generate a natural-looking adversarial example from a sketch, we force the encoded edge information (i.e., the visual attributes) to be close to the latent random vector fed to the edge generator and adversarial example generator. This preserves the spatial consistency of the adversarial example generated from the random vector with the edge information. In addition, by employing a sketch-edge encoder with a novel sketch-edge matching loss, we reduce the gap between edges and sketches. We evaluate the proposed method on several dominant classes of SketchyCOCO, the benchmark dataset for sketch to image translation. Our experiments show that our SketchAdv produces visually plausible adversarial examples while remaining competitive with other adversarial attack methods.