On the Importance of Backbone to the Adversarial Robustness of Object Detectors

Object detection is a critical component of various security-sensitive applications, such as autonomous driving and video surveillance. However, existing deep learning-based object detectors are vulnerable to adversarial attacks, which poses a significant challenge to their reliability and safety. Through experiments, we found that existing works on improving the adversarial robustness of object detectors have given a false sense of security. We argue that using adversarially pre-trained backbone networks is essential for enhancing the adversarial robustness of object detectors. We propose a simple yet effective recipe for fast adversarial fine-tuning on object detectors with adversarially pre-trained backbones. Without any modifications to the structure of object detectors, our recipe achieved significantly better adversarial robustness than previous works. Moreover, we explore the potential of different modern object detectors to improve adversarial robustness using our recipe and demonstrate several interesting findings. Our empirical results set a new milestone and deepen the understanding of adversarially robust object detection. Code and trained checkpoints will be publicly available.Comment: 12 page

Development of a Transferable Reactive Force Field of P/H Systems: Application to the Chemical and Mechanical Properties of Phosphorene

ReaxFF provides a method to model reactive chemical systems in large-scale molecular dynamics simulations. Here, we developed ReaxFF parameters for phosphorus and hydrogen to give a good description of the chemical and mechanical properties of pristine and defected black phosphorene. ReaxFF for P/H is transferable to a wide range of phosphorus and hydrogen containing systems including bulk black phosphorus, blue phosphorene, edge-hydrogenated phosphorene, phosphorus clusters and phosphorus hydride molecules. The potential parameters were obtained by conducting unbiased global optimization with respect to a set of reference data generated by extensive ab initio calculations. We extend ReaxFF by adding a 60{\deg} correction term which significantly improves the description of phosphorus clusters. Emphasis has been put on obtaining a good description of mechanical response of black phosphorene with different types of defects. Compared to nonreactive SW potential [1], ReaxFF for P/H systems provides a huge improvement in describing the mechanical properties the pristine and defected black phosphorene and the thermal stability of phosphorene nanotubes. A counterintuitive phenomenon is observed that single vacancies weaken the black phosphorene more than double vacancies with higher formation energy. Our results also show that mechanical response of black phosphorene is more sensitive to defects for the zigzag direction than for the armchair direction. Since ReaxFF allows straightforward extensions to the heterogeneous systems, such as oxides, nitrides, ReaxFF parameters for P/H systems build a solid foundation for the reactive force field description of heterogeneous P systems, including P-containing 2D van der Waals heterostructures, oxides, etc