Over the past decade, deep learning has revolutionized conventional tasks
that rely on hand-craft feature extraction with its strong feature learning
capability, leading to substantial enhancements in traditional tasks. However,
deep neural networks (DNNs) have been demonstrated to be vulnerable to
adversarial examples crafted by malicious tiny noise, which is imperceptible to
human observers but can make DNNs output the wrong result. Existing adversarial
attacks can be categorized into digital and physical adversarial attacks. The
former is designed to pursue strong attack performance in lab environments
while hardly remaining effective when applied to the physical world. In
contrast, the latter focus on developing physical deployable attacks, thus
exhibiting more robustness in complex physical environmental conditions.
Recently, with the increasing deployment of the DNN-based system in the real
world, strengthening the robustness of these systems is an emergency, while
exploring physical adversarial attacks exhaustively is the precondition. To
this end, this paper reviews the evolution of physical adversarial attacks
against DNN-based computer vision tasks, expecting to provide beneficial
information for developing stronger physical adversarial attacks. Specifically,
we first proposed a taxonomy to categorize the current physical adversarial
attacks and grouped them. Then, we discuss the existing physical attacks and
focus on the technique for improving the robustness of physical attacks under
complex physical environmental conditions. Finally, we discuss the issues of
the current physical adversarial attacks to be solved and give promising
directions