Controllable Orthogonalization in Training DNNs

真实场景中通过可控图像编辑实施车辆检测器的伪装攻击

In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing

本文提出了一种将车辆伪装攻击视为条件图像编辑问题的新框架，通过图像级和场景级生成策略提升攻击效果，实验表明其在COCO和LINZ数据集上显著降低检测器性能，同时保持车辆结构和提升人类感知的隐蔽性。

Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World

We introduce a method to disentangle controllable and uncontrollable factors of variation by interacting with the world. Disentanglement leads to good representations and is important when applying deep neural networks (DNNs) in fields where explanations are required. This study attempts to improve an existing reinforcement learning (RL) approach to disentangle controllable and uncontrollable factors of variation, because the method lacks a mechanism to represent uncontrollable obstacles. To address this problem, we train two DNNs simultaneously: one that represents the controllable object and…

Transferable, Controllable, and Inconspicuous Adversarial Attacks on Person Re-identification With Deep Mis-Ranking

The success of DNNs has driven the extensive applications of person re-identification (ReID) into a new era. However, whether ReID inherits the vulnerability of DNNs remains unexplored. To examine the robustness of ReID systems is rather important because the insecurity of ReID systems may cause severe losses, e.g., the criminals may use the adversarial perturbations to cheat the CCTV systems. In this work, we examine the insecurity of current best-performing ReID models by proposing a learning-to-mis-rank formulation to perturb the ranking of the system output. As the cross-dataset transferab…