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首页自动驾驶中针对LiDAR感知的敌对传感器攻击研究
"这篇论文是CCS-2019会议上的一篇研究,主要探讨了针对自动驾驶汽车(AVs)中LiDAR(光检测和测距)感知系统的对抗性传感器攻击。研究者首次深入研究了基于LiDAR的感知安全问题,而以前的研究主要集中在摄像头感知上。他们将攻击设定为在受害AV前方制造虚假障碍物,利用机器学习的目标检测漏洞进行欺骗。" 文章详细阐述了研究背景和目标,指出LiDAR在自动驾驶中的关键作用,以及其安全性的重要性。研究人员发现,简单的LiDAR欺骗不足以误导感知系统,因为现代系统通常依赖于机器学习模型来识别和理解周围环境。因此,他们提出将攻击策略化,通过优化问题的形式来设计输入扰动,目标是使机器学习模型误判。 为了实施这些攻击,研究者构建了一个优化模型,并设计了相应的算法。他们分析了仅使用优化和结合全局采样的方法的局限性,成功将攻击成功率提升到约75%。此外,论文通过案例研究展示了攻击在实际驾驶决策层面的影响,提出了两种可能危害道路安全和车辆机动性的攻击场景。 在防御策略方面,论文讨论了在AV系统、传感器和机器学习模型层面上可能的防御措施。作者团队包括来自密歇根大学和加州大学欧文分校的研究人员,如Yulong Cao、Chaowei Xiao、Kevin Fu和Z. Morley Mao等人,他们在安全和计算机科学领域有着深厚的背景。 这篇论文揭示了LiDAR感知系统在自动驾驶汽车中的脆弱性,并提供了关于如何通过优化攻击策略来欺骗这些系统的深入见解。同时,它也引发了对更强大防御机制的需求,以保护自动驾驶汽车免受此类攻击的威胁。未来的研究可能会进一步探索这些防御策略,并寻找在不影响性能的情况下增强系统鲁棒性的方法。
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Figure 1: Overview of the data processing pipeline for LiDAR-based perception in Baidu Apollo.
Step 1: Pre processing
. The raw LiDAR sensor input is called
3D point cloud and we denote it as
X
. The dimension of
X
is
n ×
4,
where
n
denotes the number of data points and each data point
is a 4-dimension vector with the 3D coordinates,
w
x
,
w
y
, and
w
z
,
and the intensity of the point. In the pre-processing step,
X
is rst
transformed into an absolute coordinate system. Next, the Region
of Interest (ROI) lter removes unrelated portions of the 3D point
cloud data, e.g., those that are outside of the road, based on HDMap
information. Next, a feature generation process generates a feature
matrix
x (
8
×
512
×
512
)
, which is the input to the subsequent
machine learning model. In this process, the ROI-ltered 3D point
cloud within the range (60 meters by default) is mapped to 512
×
512
cells according to the
w
x
and
w
y
coordinates. In each cell, the
assigned points are used to generate 8 features as listed in Table 1.
Step 2: DNN-based object detection.
A Deep Neural Network
(DNN) then takes the feature matrix
x
as input and produces a set
of output metrics for each cell, e.g., the probability of the cell being
a part of an obstacle. These output metrics are listed in Table 2.
Step 3: Post processing.
The clustering process only considers
cells with objectness values (one of the output metrics listed in Table
2) greater than a given threshold (0
.
5 by default). Then, the process
constructs candidate object clusters by building a connected graph
using the cells’ output metrics. Candidate object clusters are then l-
tered by selecting clusters with average positiveness values (another
output metric) greater than a given threshold (0
.
1 by default). The
box builder then reconstructs the bounding box including height,
width, length of an obstacle candidate from the 3D point cloud
assigned to it. Finally, the tracker integrates consecutive frames of
processed results to generate tracked obstacles, augmented with
additional information such as speed, acceleration, and turning
rates, as the output of the LiDAR-based perception.
With the information of perceived obstacles such as their po-
sitions, shapes, and obstacle types, the Apollo system then uses
such information to make driving decisions. The perception output
is further processed by the prediction module which predicts the
future trajectories of perceived obstacles, and then the planning
module which plans the future driving routes and makes decisions
such as stopping, lane changing, yielding, etc.
Feature Description
Max height Maximum height of points in the cell.
Max intensity Intensity of the brightest point in the cell.
Mean height Mean height of points in the cell.
Mean intensity Mean intensity of points in the cell.
Count Number of points in the cell.
Direction Angle of the cell’s center with respect to the origin.
Distance Distance between the cell’s center and the origin.
Non-empty
Binary value indicating whether the cell is empty or occupied.
Table 1: DNN model input features.
Metrics Description
Center oset
Oset to the predicted center of the cluster the cell belongs
to.
Objectness The probability of a cell belonging to an obstacle.
Positiveness The condence score of the detection.
Object height The predicted object height.
Class probability
The probability of the cell being a part of a vehicle, pedes-
trian, etc.
Table 2: DNN model output metrics.
2.2 LiDAR Sensor and Spoong Attacks
To understand the principles underlying our security analysis
methodology, it is necessary to understand how the LiDAR sen-
sor generates a point cloud and how it is possible to alter it in a
controlled way using spoong attacks.
LiDAR sensor.
A LiDAR sensor functions by ring laser pulses
and capturing their reections using photodiodes. Because the
speed of light is constant, the time it takes for the echo pulses to
reach the receiving photodiode provides an accurate measurement
of the distance between a LiDAR and a potential obstacle. By ring
the laser pulses at many vertical and horizontal angles, a LiDAR
generates a point cloud used by the AV systems to detect objects.
LiDAR spoong attack.
Sensor spoong attacks use the same
physical channels as the targeted sensor to manipulate the sen-
sor readings. This strategy makes it very dicult for the sensor
system to recognize such attack, since the attack doesn’t require
any physical contact or tampering with the sensor, and it doesn’t
interfere with the processing and transmission of the digital sensor
measurement. These types of attack could trick the victim sensor
to provide seemingly legitimate but actually erroneous data.
LiDAR has been shown to be vulnerable to laser spoong attacks
in prior work. Petit et al. demonstrated that a LiDAR spoong
attack can be performed by replaying the LiDAR laser pulses from
a dierent position to create fake points further than the location of
the spoofer [
42
]. Shin et al. showed that it is possible to generate a
fake point cloud at dierent distances, even closer than the spoofer
location [
44
]. In this paper, we build upon these prior work to study
the eect of this attack vector on the security of AV perception.
2.3 Adversarial Machine Learning
Neural networks.
A neural network is a function consisting of
connected units called (articial) neurons that work together to rep-
resent a dierentiable function that outputs a distribution. A given
neural network (e.g., classication) can be dened by its model
architecture and parameters
ϕ
. An optimizer such as Adam [
35
]
is used to update the parameters
ϕ
with respect to the objective
function L.
Adversarial examples.
Given a machine learning model
M
, in-
put
x
and its corresponding label
y
, an adversarial attacker aims
Session 10A: Cyberphysical Security
CCS ’19, November 11–15, 2019, London, United Kingdom
2269
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