3D object detection plays an important role in autonomous driving; however,
its vulnerability to backdoor attacks has become evident. By injecting
''triggers'' to poison the training dataset, backdoor attacks manipulate the
detector's prediction for inputs containing these triggers. Existing backdoor
attacks against 3D object detection primarily poison 3D LiDAR signals, where
large-sized 3D triggers are injected to ensure their visibility within the
sparse 3D space, rendering them easy to detect and impractical in real-world
scenarios.
In this paper, we delve into the robustness of 3D object detection, exploring
a new backdoor attack surface through 2D cameras. Given the prevalent adoption
of camera and LiDAR signal fusion for high-fidelity 3D perception, we
investigate the latent potential of camera signals to disrupt the process.
Although the dense nature of camera signals enables the use of nearly
imperceptible small-sized triggers to mislead 2D object detection, realizing
2D-oriented backdoor attacks against 3D object detection is non-trivial. The
primary challenge emerges from the fusion process that transforms camera
signals into a 3D space, compromising the association with the 2D trigger to
the target output. To tackle this issue, we propose an innovative 2D-oriented
backdoor attack against LiDAR-camera fusion methods for 3D object detection,
named BadFusion, for preserving trigger effectiveness throughout the entire
fusion process. The evaluation demonstrates the effectiveness of BadFusion,
achieving a significantly higher attack success rate compared to existing
2D-oriented attacks.

通过在训练数据集中注入‘触发’来污染数据，我们提出了一种新的基于 2D 相机的反向攻击 3D 目标检测的方法，命名为 BadFusion。通过在整个融合过程中保留触发器的有效性，BadFusion 相对于现有的基于 2D 的攻击方法，实现了显著更高的攻击成功率。

BadFusion: 针对 3D 目标检测的 2D 定向后门攻击

BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection

Machine vision and image processing are often used with sensors for situation
awareness in autonomous systems, from industrial robots to self-driving cars.
The 3D depth sensors, such as LiDAR (Light Detection and Ranging), Radar, are
great invention for autonomous systems. Due to the complexity of the setup,
LiDAR may not be suitable for some operational environments, for example, a
space environment. This study was motivated by a desire to get real-time
volumetric and change information with multiple 2D cameras instead of a depth
camera. Two cameras were used to measure the dimensions of a rectangular object
in real-time. The R-C-P (row-column-pixel) method is developed using image
processing and edge detection. In addition to the surface areas, the R-C-P
method also detects discontinuous edges or volumes. Lastly, experimental work
is presented for illustration of the R-C-P method, which provides the equations
for calculating surface area dimensions. Using the equations with given
distance information between the object and the camera, the vision system
provides the dimensions of actual objects.

该研究使用图像处理和边缘检测开发了 R-C-P（行 - 列 - 像素）方法，结合给定的距离信息，利用多个 2D 相机实时测量矩形物体的尺寸，提供了计算表面积尺寸的方程。

R-C-P 方法：使用图像处理和机器视觉的自主体积计算方法

R-C-P Method: An Autonomous Volume Calculation Method Using Image  Processing and Machine Vision

Non-contact estimation of respiratory pattern (RP) and respiration rate (RR)
has multiple applications. Existing methods for RP and RR measurement fall into
one of the three categories - (i) estimation through nasal air flow
measurement, (ii) estimation from video-based remote photoplethysmography, and
(iii) estimation by measurement of motion induced by respiration using motion
detectors. These methods, however, require specialized sensors, are
computationally expensive and/or critically depend on selection of a region of
interest (ROI) for processing. In this paper a general framework is described
for estimating a periodic signal driving noisy LTI channels connected in
parallel with unknown dynamics. The method is then applied to derive a
computationally inexpensive method for estimating RP using 2D cameras that does
not critically depend on ROI. Specifically, RP is estimated by imaging the
changes in the reflected light caused by respiration-induced motion. Each
spatial location in the field of view of the camera is modeled as a
noise-corrupted linear time-invariant (LTI) measurement channel with unknown
system dynamics, driven by a single generating respiratory signal. Estimation
of RP is cast as a blind deconvolution problem and is solved through a method
comprising subspace projection and statistical aggregation. Experiments are
carried out on 31 healthy human subjects by generating multiple RPs and
comparing the proposed estimates with simultaneously acquired ground truth from
an impedance pneumograph device. The proposed estimator agrees well with the
ground truth device in terms of correlation measures, despite variability in
clothing pattern, angle of view and ROI.

本文提出了一种基于 2D 相机且不受关注区域限制的计算廉价的方法估算周期性驱动的噪声 LTI 通道，并将其应用于呼吸模式 (RP) 的识别。通过将各个空间位置建模为噪声受到污染的线性时不变的（LTI）带有未知系统动力学的测量通道，来实现 RP 的估算， 然后将其作为盲卷积问题，并通过包括子空间投影和统计聚合的方法来解决。本方法在 31 名健康人类主体上进行实验，与同时获取的障碍性肺囊容积测定仪的基础真相相比，该估算器具有良好的相关性措施，尽管在衣物图案，视野角度和 ROI 的可变性方面。