  It is well known that the performance of deep neural networks (DNNs) is
susceptible to subtle interference. So far, camera-based physical adversarial
attacks haven't gotten much attention, but it is the vacancy of physical
attack. In this paper, we propose a simple and efficient camera-based physical
attack called Adversarial Color Film (AdvCF), which manipulates the physical
parameters of color film to perform attacks. Carefully designed experiments
show the effectiveness of the proposed method in both digital and physical
environments. In addition, experimental results show that the adversarial
samples generated by AdvCF have excellent performance in attack
transferability, which enables AdvCF effective black-box attacks. At the same
time, we give the guidance of defense against AdvCF by means of adversarial
training. Finally, we look into AdvCF's threat to future vision-based systems
and propose some promising mentality for camera-based physical attacks.
