RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical
  World
Donghua Wang Wen Yao Tingsong Jiang Chao Li Xiaoqian Chen
Physical adversarial attacks against deep neural networks (DNNs) have
recently gained increasing attention. The current mainstream physical attacks
use printed adversarial patches or camouflage to alter the appearance of the
target object. However, these approaches generate conspicuous adversarial
patterns that show poor stealthiness. Another physical deployable attack is the
optical attack, featuring stealthiness while exhibiting weakly in the daytime
with sunlight. In this paper, we propose a novel Reflected Light Attack (RFLA),
featuring effective and stealthy in both the digital and physical world, which
is implemented by placing the color transparent plastic sheet and a paper cut
of a specific shape in front of the mirror to create different colored
geometries on the target object. To achieve these goals, we devise a general
framework based on the circle to model the reflected light on the target
object. Specifically, we optimize a circle (composed of a coordinate and
radius) to carry various geometrical shapes determined by the optimized angle.
The fill color of the geometry shape and its corresponding transparency are
also optimized. We extensively evaluate the effectiveness of RFLA on different
datasets and models. Experiment results suggest that the proposed method
achieves over 99% success rate on different datasets and models in the digital
world. Additionally, we verify the effectiveness of the proposed method in
different physical environments by using sunlight or a flashlight.