Object detection, as a fundamental computer vision task, has achieved a
remarkable progress with the emergence of deep neural networks. Nevertheless,
few works explore the adversarial robustness of object detectors to resist
adversarial attacks for practical applications in various real-world scenarios.
Detectors have been greatly challenged by unnoticeable perturbation, with sharp
performance drop on clean images and extremely poor performance on adversarial
images. In this work, we empirically explore the model training for adversarial
robustness in object detection, which greatly attributes to the conflict
between learning clean images and adversarial images. To mitigate this issue,
we propose a Robust Detector (RobustDet) based on adversarially-aware
convolution to disentangle gradients for model learning on clean and
adversarial images. RobustDet also employs the Adversarial Image Discriminator
(AID) and Consistent Features with Reconstruction (CFR) to ensure a reliable
robustness. Extensive experiments on PASCAL VOC and MS-COCO demonstrate that
our model effectively disentangles gradients and significantly enhances the
detection robustness with maintaining the detection ability on clean images.

本文介绍了一种基于对抗神经网络（Adversarial Neural Network）的 Robust Detector，采用了 Adversarial Image Discriminator 和 Consistent Features with Reconstruction 等方法，大大提高了在 PASCAL VOC 和 MS-COCO 数据集上物体检测模型在对抗干扰下的鲁棒性。

对抗智能意识强鲁棒性目标检测器

Adversarially-Aware Robust Object Detector

We develop a novel computationally efficient and general framework for robust
hypothesis testing. The new framework features a new way to construct
uncertainty sets under the null and the alternative distributions, which are
sets centered around the empirical distribution defined via Wasserstein metric,
thus our approach is data-driven and free of distributional assumptions. We
develop a convex safe approximation of the minimax formulation and show that
such approximation renders a nearly-optimal detector among the family of all
possible tests. By exploiting the structure of the least favorable
distribution, we also develop a tractable reformulation of such approximation,
with complexity independent of the dimension of observation space and can be
nearly sample-size-independent in general. Real-data example using human
activity data demonstrated the excellent performance of the new robust
detector.

本研究提出了一种基于 Wasserstein 距离度量的数据驱动假设检验新框架，结构性质使得该模型的复杂度与数据维度和样本大小基本无关，有助于构建鲁棒的检测器。