所有行业的杰克，没有大师：通过透明补丁攻击解决分布转移和突兀
Jacks of All Trades, Masters Of None: Addressing Distributional Shift and Obtrusiveness via Transparent Patch Attacks
摘要：
我们专注于开发有效的对抗补丁攻击，并首次通过设计新颖的半透明补丁来共同解决攻击成功和引人注目的对抗目标。这项工作的动机是我们追求一个系统的性能分析补丁攻击的鲁棒性方面的几何变换。具体来说，我们首先阐明了a）支持补丁攻击成功的关键因素和b）在转换预期（EoT）形式主义下，训练和测试/部署之间的分布转移的影响。通过将我们的分析集中在三个主要类别的转换（旋转，规模和位置），我们的研究结果提供了可量化的见解有效的补丁攻击的设计，并证明规模，在所有因素中，显着影响补丁攻击的成功。根据这些发现，我们然后专注于解决如何克服在实际物理环境中部署攻击的规模的主要限制：即大型补丁的突兀性。我们的策略是转向不规则形状的，半透明的部分补丁，我们通过一个新的优化过程，共同解决缓解突兀和最大限度地提高效率的对立目标构建的新设计。我们希望我们的研究将有助于鼓励社区更多地关注补丁攻击的侵扰性、规模和成功率等问题。
英文摘要：
We focus on the development of effective adversarial patch attacks and -- for the first time -- jointly address the antagonistic objectives of attack success and obtrusiveness via the design of novel semi-transparent patches. This work is motivated by our pursuit of a systematic performance analysis of patch attack robustness with regard to geometric transformations. Specifically, we first elucidate a) key factors underpinning patch attack success and b) the impact of distributional shift between training and testing/deployment when cast under the Expectation over Transformation (EoT) formalism. By focusing our analysis on three principal classes of transformations (rotation, scale, and location), our findings provide quantifiable insights into the design of effective patch attacks and demonstrate that scale, among all factors, significantly impacts patch attack success. Working from these findings, we then focus on addressing how to overcome the principal limitations of scale for the deployment of attacks in real physical settings: namely the obtrusiveness of large patches. Our strategy is to turn to the novel design of irregularly-shaped, semi-transparent partial patches which we construct via a new optimization process that jointly addresses the antagonistic goals of mitigating obtrusiveness and maximizing effectiveness. Our study -- we hope -- will help encourage more focus in the community on the issues of obtrusiveness, scale, and success in patch attacks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
