CAMOU: L EARNING A V EHICLE C AMOUFLAGE FOR P HYSICAL A DVERSARIAL A TTACK ON O BJECT D ETECTORS IN THE W ILD
A BSTRACT
In this paper we conduct an intriguing experimental study about the physical adversarial attack on object detectors in the wild.
In particular, we learn a camouﬂage pattern to hide vehicles from being detected by state-of-the-art convolutional neural network based detectors.
Our approach alternates between two threads.
In the ﬁrst, we train a neural approximation function to imitate how a simulator applies a camouﬂage to vehicles and how a vehicle detector performs given images of the camouﬂaged vehicles.
In the second, we minimize the approximated detection score by searching for the optimal camouﬂage.
Experiments show that the learned camouﬂage can not only hide a vehicle from the image-based detectors under many test cases but also generalizes to different environments, vehicles, and object detectors
I NTRODUCTION
Is it possible to paint a unique pattern on a vehicle’s body and hence hide it from being detected by surveillance cameras?
We conjecture the answer is afﬁrmative mainly for two reasons.
First, deep neural networks will be widely used in modern surveillance and autonomous driving systems for automatic vehicle detection.
Second, unfortunately, these neural networks are intriguingly vulnerable to adversarial examples (Akhtar & Mian, 2018).
Szegedy et al.
(2013) found that adding imperceptible perturbations to clean images can result in the failure of neural networks trained for image classiﬁcation.
This motivates a rich line of work on developing defense techniques for the neural networks (Akhtar & Mian, 2018) and powerful attack methods to defeat those defenses (Athalye et al., 2018a).
Moreover, the adversarial attack has been extended to other tasks, such as semantic segmentation (Arnab et al., 2018), object detection (Xie et al., 2017), image captioning (Chen et al., 2018a), etc.
It is worth noting that the adversarial examples in the works mentioned above are not physical, i.e., the adversary directly manipulates image pixels.
Although it is arguably more challenging to create physical adversarial objects than to produce adversarial images, some existing works have shown promising results with adversarial patches (Brown et al., 2017), stop signs (Eykholt et al., 2018b;
Chen et al., 2018b), and small objects like baseballs and 3D turtle models (Athalye et al., 2018b).
To this end, we are reasonably optimistic about designing a special pattern to camouﬂage a 3D car, in order to make it difﬁcult to detect by the deep learning based vehicle detectors.
It is undoubtedly challenging to run experiments in the real world considering ﬁnancial and time constraints.
In this paper, we instead demonstrate results using a simulation engine (Unreal) with a high-ﬁdelity 3D sedan model and a 3D SUV.
Fig.
1 shows that the vehicle in the simulation is photo-realistic such that, even covered with random camouﬂage, it can still be detected by the Mask
R-CNN detector (He et al., 2017) trained on COCO (Lin et al., 2014).
The simulation engine enables us to test the physically adversarial cars under a considerable spectrum of environmental conditions: lighting, backgrounds, camera-to-object distances, viewing angles, occlusions, etc.
In contrast, existing experiments on physical adversarial attacks are all executed in simpliﬁed scenarios.
Eykholt et al.
(2018b),
Eykholt et al.
(2018a), and Chen et al.
(2018b) attack neural classiﬁers and detectors of stop signs.
While projective transformations could be used to render the planar stop signs to various images, it is more involved to image the nonplanar 3D vehicles considered in this paper; we learn a neural approximation function instead.
Athalye et al.
(2018b) synthesize objects (e.g., baseball, turtle, etc.) which are adversarial within a small range of camera-to-object distances and viewing angles.
Given a 3D vehicle model in the simulation engine, we learn a camouﬂage for it by following the expectation over transformation (EoT) principle ﬁrst formalized by Athalye et al.
(2018b).
The main idea is to consider a variety of transformations under which the camouﬂage can consistently hide the vehicle from a neural detector.
A transformation imitates the imaging procedure and produces an image of the 3D vehicle model in the simulated environment.
If a camouﬂage works under many transformations seen in the training phase, it is expected to also generalize to unseen transformations in the test phase.
One of the major challenges is that the simulator’s image generation procedure is non-differentiable.
A seemingly plausible solution is to train a neural network to approximate this procedure.
The network takes as input the environment, a camouﬂage pattern, and the 3D vehicle model and outputs an image as close as possible to the one rendered by the simulator.
Although this approach is viable, it is extremely difﬁcult to generate high-resolution images.
State-of-the-art methods (e.g., RenderNet (Nguyen-Phuoc et al., 2018)) can only generate simple 3D objects without any backgrounds.
We tackle the above challenge by drawing the following observation.
In EoT (Athalye et al., 2018b;
Chen et al., 2018b), the gradients propagate back to the physical object from the detector/classiﬁer’s decision values.
If we jointly consider the object detector and the imaging procedure of the simulator as a whole black box, it is easier to learn a function to approximate this black box’s behavior than to train the image generation neural network.
Hence, we learn a substitute neural network which takes as input a camouﬂage, the vehicle model, and the environment and outputs the vehicle detector’s decision value.
Equipped with this substitute network, we can readily run the EoT algorithm (Athalye et al., 2018b) over our simulator in order to infer an adversarial camouﬂage for the vehicles.
Finally, we make some remarks about the signiﬁcance and potential impact of this work.
In the real world, multiclass visual object detection neural networks (He et al., 2017; Redmon & Farhadi, 2018) have become the cornerstone of multiple industrial applications, such as surveillance systems (Nair et al., 2018), autonomous driving (Huval et al., 2015), and military systems (Pellerin, 2017).
Among these applications, cars are one of the most crucial objects.
Attacking vehicle detectors in the physical world will be enormously valuable and impactful from the perspective of the malicious adversaries.
Compared with the stop sign, it is legal in the United States to paint a car while defacing a stop sign is criminal.
This poses a more signiﬁcant threat to autonomous driving systems since anyone has access to perturb public machine learning based systems legally.
This observation motivates us to focus our approach on cars.
We limit our camouﬂage within the legally paintable car body parts, which means that we will leave discriminative visual cues, such as the tires, windows, grille, lights, etc. unaltered for the detectors.
R ELATED W ORK 2. A DVERSARIAL A TTACK AND ITS G ENERALIZATION
Currently, the adversarial attack is powerful enough to attack image classiﬁcation (MoosaviDezfooli et al., 2017), object detection (Arnab et al., 2018), semantic segmentation (Xie et al., 2017), audio recognition (Carlini & Wagner, 2018) and even bypass most of the defense mechanism (Athalye et al., 2018a).
The mainstream adversarial machine learning research focuses on the in silico ones or the generalization within in silico (Liu et al., 2017).
Such learned perturbations are practically unusable in the real world as shown in the experiments by Lu et al.
(2017b), who found that almost all perturbation methods failed to prevent a detector from detecting real stop signs.
The ﬁrst physical world adversarial attack by Kurakin et al.
(2016) found perturbations remain effective on the printed paper.
Eykholt et al.
(2018b) found a way to train perturbations that remain effective against a classiﬁer on real stop signs for different viewing angles and such.
Athalye et al.
(2018b) trained another perturbation that successfully attacks an image classiﬁer on 3D-printed objects.
However Lu et al.
(2017c) found that Eykholt et al.
(2018b)’s perturbation does not fool the object detectors YOLO9000 (Redmon & Farhadi, 2017) and Faster RCNN (Ren et al., 2015).
They argue that fooling an image classiﬁer is different and easier than fooling an object detector because the detector is able to propose object bounding boxes on its own.
In the meanwhile, Lu et al.
(2017a)’s
and
Chen et al.
(2018b)’s work could be generalized to attack the stop sign detector in the physical world more effectively.
However, all the above methods aim to perturb detecting the stop signs.
Blackbox attack is another relevant topic.
Among the current blackbox attack literature, Papernot et al.
(2017) trained a target model substitution based on the assumption that the gradient between the image and the perturbation is available.
We do not have the gradient since the simulation is nondifferentiable.
Chen et al.
(2017) proposed a coordinate descent to attack the model.
However, we found that our optimization problem is time-consuming, noisy (see Sec.
G.2), empirically nonlinear and non-convex (see Sec.
H).
Time constraints made this approach unavailable to us since it requires extensive evaluations during coordinate descent.
Besides, coordinate descent generally requires precise evaluation at each data-point.
2. S IMULATION AIDED M ACHINE L EARNING
Since the dawn of deep learning, data collection has always been of fundamental importance as deep learning model performance is generally correlated with the amount of training data used.
Given the sometimes unrealistically expensive annotation costs, some machine learning researchers use synthetic data to train their models.
This is especially true in computer vision applications since state-of-the-art computer-generated graphics are truly photo-realistic.
Ros et al.
; Richter et al.
(2017) proposed synthetic datasets for semantic segmentation.
Gaidon et al.
(2016) proposed virtual KITTI as a synthetic replica of the famous KITTI dataset (Geiger et al., 2012) for tracking, segmentation, etc.
And Varol et al.
(2017) proposed using synthetic data for human action learning.
Zhang et al.
; Bousmalis et al.
(2017) adapt RL-trained robot grasping models from the synthetic environment to the real environment.
Tremblay et al.
(2018) trained a detection network using the same Unreal engine that we are using.
P ROBLEM S TATEMENT
In this paper, we investigate physical adversarial attack on state-of-the-art neural network based object detectors.
The objective is to ﬁnd a camouﬂage pattern such that, when it is painted on the body of a vehicle,
the Mask R-CNN (He et al., 2017) and YOLO (Redmon & Farhadi, 2018)
detectors fail to detect the vehicle under a wide spectrum of variations (e.g., locations, lighting conditions, viewing angles, etc.).
We learn the camouﬂage in a black-box fashion, without the need for accessing the detectors’ network architecture or weights.
Expectation-over-transformation (EoT).
We formalize the physical adversarial attack problem with the EoT framework (Athalye et al., 2018b).
Denote by t a transformation which converts a camouﬂage pattern c to a real photo.
Such a transformation actually represents an involved procedure: paint the pattern to a vehicle’s body, drive the vehicle to a location, conﬁgure a camera, and take a picture of the vehicle.
The transformation conveniently abstracts away enormous factors (e.g., quality of painting, camera, etc.), each of which could play a role in this procedure.
Denote by V t ( c ) the detection score (e.g., by Mask-RCNN) over an image which is a result of a transformation t and a camouﬂage c .
The physical adversarial attack problem is then described by
[Equation]
where T is a distribution over all the possible transformations { t } .
In other words, we search for a camouﬂage c that minimizes the vehicle detection score in expectation.
Transformation in simulation.
Due to ﬁnancial and time constraints, we conduct our study with the photo-realistic Unreal 4 game engine.
It supplies us sufﬁcient conﬁguration parameters, such as the resolution and pattern of the camouﬂage, 3D models of vehicles, parameters of cameras and environments, etc.
Fig.
2 shows a camouﬂage pattern, a high-ﬁdelity 3D model of Toyota Camry, a corner of the virtual city used in this paper, and ﬁnally the picture taken by a camera after we apply the camouﬂage to the Camry and drive it to that corner — in other words, the rightmost image is the result of a certain transformation t acting on the three images on the left of Fig. 2.
A PPROACH
In this section, we present two key techniques for solving the problem min c E t V t ( c ) (cf.
Eq.
(1) and the text there).
One is to estimate the expectation E t by an empirical mean over many transformations.
The other is to train a neural network to clone the joint behavior V t ( c ) of the black-box detector and the non-differentiable simulator.
4. S AMPLING TRANSFORMATIONS TO ESTIMATE E t
Recall that a transformation t speciﬁes a particular procedure from applying the camouﬂage c to a vehicle until the corresponding image captured by a camera.
In the context of the simulation engine, the camouﬂage c is ﬁrst programmed as textures, which are then warped onto the 3D model of an object.
The simulator can teleport the object to different locations in the environment.
The simulator also has multiple cameras to photograph the teleported object from various distances and viewing angles.
We identify some key factors involved in this procedure, including vehicle, location, camera-toobject distance, and viewing angle.
The combinations of them also give rise to variations along other dimensions.
For instance, the lighting condition changes from one location to another.
The vehicle of interest is occluded to different degrees in the images captured by the cameras.
Denote by T S all the sampled transformations that are used for learning the camouﬂage.
Fig.
3 illustrates the positions where the cameras are placed.
We can see how the viewing angles and camera-to-object distances vary.
The ones shown in the green color are used in the training.
We left out some cameras shown in the red color for the testing.
Since it is computationally expensive to utilize the cameras, we randomly arrange them in different heights and distances to create as much variations as possible instead of traversing all possible height-distance combinations.
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I
III
IV
II I II
III
IV
4. L EARNING A CLONE NETWORK V θ ( c, t ) TO APPROXIMATE V t ( c )
If we unroll the detection score V t ( c ) , it contains two major components.
The ﬁrst renders an image based on the camouﬂage c by following the procedure speciﬁed by the transformation t .
The second obtains the detection score of a vehicle detector (e.g., Mask-RCNN) over the rendered image.
The ﬁrst component is non-differentiable while the second one could be a black box in practice.
Therefore, we propose to consider them jointly as a single black box.
Furthermore, we learn a clone neural network V θ ( c, t ) to imitate the input-output behavior of this extended black box.
As the transformation t itself is involved and hard to represent, we instead input its consequence to the network: a background image and the cropped foreground of the vehicle.
Fig.
4b shows the network architecture.
It takes as input a camouﬂage pattern c , the background image due to a sampled transformation t , and the cropped foreground.
The network V θ
( c, t ) has a single output to approximate the detection score V t ( c ) .
To this end, we are ready to write down an approximate form of problem (1), arg
[Equation]
[Equation]
Thanks to the differentiable network V θ ( c, t ) with respect to the camouﬂage c , we can solve the problem above by standard (stochastic) gradient descent.
It is important to note that the ﬁdelity of problem (2) depends on the size and diversity of the sampled set of transformations T S as well as the quality of the clone network V θ ( c, t ) .
It is straightforward to generate a large training set for the clone network by randomizing the camouﬂages and transformations and “labeling” the resulting images with the detection scores.
However, if the optimal camouﬂage is unfortunately not covered by this training set, a discrepancy would occur when we solve problem (2).
In other words, the network may fail to well approximate the detection scores at the region around the optimal camouﬂage.
We address this discrepancy by an alternative learning algorithm as follows.
4. J OINTLY LEARNING THE CLONE NETWORK AND THE OPTIMAL CAMOUFLAGE
We alternatively learn the clone network V θ ( c, t ) and solve the problem (2).
Once a new camouﬂage pattern is found due to optimizing problem (2), it is converted to multiple images by the training
[Figure]
transformations T S .
We obtain the detection score for each of the images by querying a detector.
The camouﬂage pattern, along with the detection scores are then added to the training set for learning the clone network V θ ( c, t ) .
Fig.
4a illustrates this process.
Implementation details.
Denote by H [ p, q ] := − p log q − (1 − p ) log(1 − q ) the cross-entropy loss.
We alternately solve the following two problems,
[Equation]
where C is the collection of all camouﬂages in the training set for learning the clone network V θ ( c, t ) , and s :
=
V t ( c ) is the detection score corresponding to the camouﬂage c and the transformation t .
The (cid:96) 2 regularization λ (cid:107) θ (cid:107) 2 over the clone network’s weights is essential.
Without this term, the two loss functions may cause oscillations or degeneration of the solutions.
We set λ
= 10 in the experiments.
Since the approximation accuracy of the clone network near the optimal camouﬂage is more important than in other regions, we weigh the newly added samples to the training settimes higher than the old ones at each iteration.
Algorithm 1 in the Appendix gives more details.
E XPERIMENTS
Since the primary objective of this paper is to learn camouﬂage patterns that deceive vehicle detectors, we introduce two baseline camouﬂage patterns in the experiments: 6 most popular car colors and 800 random camouﬂages in different resolutions.
We then analyze how the resolution of the camouﬂage affects the detection performance.
For the vehicles, we employ two 3D models: a
Toyota Camry and a virtual SUV.
In addition to the main comparison results, we also test the transferability of the learned camouﬂage across different car models, environments, camera positions, and object detectors.
5. E XPERIMENT S ETUP
We describe the detailed experimental setup in this section, including the simulator, vehicle detector, evaluation metrics, and the baseline camouﬂage patterns.
5.1. T HE SIMULATOR
As shown in Fig.
5a, we use the Unreal engine to build our ﬁrst simulation environment from the photo-realistic DownTown environment.
It is modeled after the downtown Manhattan in New York.
There are skyscrapers, cars, trafﬁc signs, a park, and roads in this environment, resembling a typical urban environment.
We sample 32 different locations along the streets.
Eight cameras perch at each location, each taking pictures of the size 720 × 360 .
The relative position of a camera is indexed by the viewing angle and camera-to-object distance, as shown in Fig.
In addition, we install another 16 cameras to test the generalization of the learned camouﬂage across viewing angles, distances, etc.
In total, we use 18 locations for training and another 18 for testing.
Note that the vehicles are invisible to some cameras due to occlusion.
[Figure]
Our second environment is based on a totally different countryside scene called Landscape Mountains, shown in Fig.
5b.
The roads lie on high-altitude mountains and cross bridges, forest, snow, and a lake.
We use this scene to test the transferability of our camouﬂage across different environments; this scene is not used in the training.
Like the DownTown environment, we sample 18 locations along the roads for the purpose of testing.
The two vehicles used in the experiments are shown in Fig.
One is a 2015 Toyota Camry XLE sold in the European Union.
The other is a virtual SUV from AirSim (Shah et al., 2017).
It is worth noting that the Toyota sedan appears multiple times in the MS-COCO dataset (Lin et al., 2014)
(cf.
some examples in Fig.
Since the object detectors are trained on MS-COCO, the Camry is more challenging to hide than the virtual SUV.
5.1. V EHICLE DETECTORS
We study two state-of-the-art detectors: Mask R-CNN (He et al., 2017) and YOLOv3-SPP (Redmon & Farhadi, 2018).
Mask R-CNN is one of the most powerful publicly available object detectors; it currently ranks in the 4th place in the MS COCO detection leaderboard.
Both detectors are pretrained on MS COCO.
For Mask R-CNN, we use the one implemented by Abdulla (2017).
Its backbone network is ResNet-101 (He et al., 2016).
YOLOv3 has comparable performance with
Mask R-CNN.
Its network architecture is very different from Mask R-CNN’s, causing challenges to the transfer of the camouﬂage between the two detectors.
We use the spatial pyramid pooling (SPP) variant of YOLOv3 in the experiments.
In the rest of the paper, we experiment with Mask R-CNN except the transfer experiments in Sec. 5.4.
5.1. E VALUATION M ETRICS
We adopt two metrics to evaluate the detection performance.
The ﬁrst one is a variation of the
Intersection over Union proposed by Everingham et al. (2015).
The IoU between a predicted box and the groundtruth bounding box is deﬁned as IoU ( A, B ) =
A ∩ B A ∪ B .
Since IoU was originally proposed to evaluate the results of multi-object detection, as opposed to the single vehicle detection in our context, we modify it to the following to better capture the vehicle of interest: max p ∈ P IoU ( p, GT ) , where P is the set of all detection proposals in an image.
We average this quantity across all the test images and denote by mIoU the mean value.
Our second metric is precision at 0.5 or P@ 0 . 5 .
Everingham et al.
(2015) set a 0.5 threshold for the detection IoU in the PASCAL VOC detection challenge to determine whether a detection is a hit or miss.
We report the percentage of the hit detections out of all observations as our P@ 0 .
We also report the relative precision drop of the camouﬂage against the baseline colors whenever possible.
5.1. B ASELINES
Our ﬁrst baseline is when a vehicle is colored in popular real car colors.
We select 6 basic car colors (red, black, silver, grey, blue, and white) which cover over 90% of the car colors worldwide (Axalta).
We obtain their RGB values according to the X11 Color Names.
As the second baseline, we generate 800 random camouﬂages in different resolutions ranging in { 2 i × 2 i ; i ∈ [1 .. 8] } .
Since we ﬁnd that camouﬂages with strong contrasts work better, we generate half of the camouﬂages using RGB values ∈ [0 , 255] and the other half using RGB values ∈ { 0 , 255 } .
After we obtain these camouﬂages and the corresponding detection scores, we use those with proper resolutions to initialize the training set for the clone network V θ ( c, t ) .
The two baselines partially resolve the concern one might have that the learned camouﬂage successfully attacks the detector not because it exploits the CNN’s structural weakness, but because it takes advantage of the domain gap between the real data used to train the detector and our simulated data used to test the detector.
Results show that the baselines could not fail the detectors under most test cases, indicating that the detectors are fairly resilient to the domain gap between the simulated imagery and the real data (at least for the vehicle detection task).
5. R ESOLUTION OF THE C AMOUFLAGES
We ﬁrst report the random camouﬂages’ performance on hiding the Camry from the Mast R-CNN detector in the DownTown environment.
Fig.
7 shows the results.
The ﬁrst observation is that, al-
256 Camou Resolution 69% 70% 71% 72% 73% 74% 75% 76%
D e t e c t i o n m I
o U mean mIoU (a) mIoU.
256 Camou Resolution 76% 78% 80% 82% 84% 86%
D e t e c t i o n P r e c
i s i o n @ 0 .mean
Precision (b) Precision.
though random camouﬂages are visually very different from conventional car paintings (cf.
Fig.
8), the Mask R-CNN detector is still able to detect most of them.
Another slightly counter-intuitive yet interesting observation is that the detector’s performance does not always decrease as the camouﬂages’ resolutions increase.
This is probably because some ﬁne-grained patterns displayed by the high-resolution camouﬂage become harder to observe from a distance.
Since the high-resolution camouﬂage does not bring any additional beneﬁts beyond the 16 × 16 resolution, we use camouﬂages of size 16 × 16 in the remaining experiments.
5. C AMOUFLAGING T OYOTA C AMRY IN THE URBAN ENVIRONMENT
Here we report the results on detecting the Camry in the urban environment.
Table 1 summarizes the results for the Camry respectively with the baseline colors, random camouﬂages, and our learned camouﬂage.
We can see from the table that the Mask R-CNN is surprisingly robust against different types of random camouﬂages.
The random camouﬂages’ detection scores are close to the baseline colors’.
Moreover, the standard deviation of the random camouﬂages’ scores is also very low, indicating that the difference in the random camouﬂages does not really change the detector’s performance.
Note that we apply the camouﬂage to the body of the Camry, leaving tires, windows, grilles, etc.
as informative visual cues to the detector.
Despite that, our learned camouﬂage reduces the precision by around 30% over baseline colors in both training and testing scenes in the urban environment.
[Table] [Table]
Virtual SUV.
Appendix A presents the results on the virtual SUV.
The learned camouﬂage reduces
Mask R-CNN’s detection precision by around 40% from the precision for the baseline colors.
5. T RANSFERABILITY EXPERIMENTS
We show that the camouﬂage learned to attack Mask R-CNN can actually also defeat YOLOv to a certain degree.
The results are reported in Table 2.
Similarly, appendices A to D report the transferabilities of the learned camouﬂage across different environments, vehicles, camera viewing angles, and distances from the camera to the object.
5. Q UALITATIVE R ESULTS
We present some of our detection results on the baseline colors, random camouﬂages, and the learned camouﬂages for the Camry in Fig.
We can draw a lot of interesting observations from the qualitative results which were hidden by the quantitative results.
We ﬁnd that there are 3 types of successful attacks: (1) Camouﬂages lower the objectiveness of the car and the car region is not proposed or only partially proposed as a candidate for the classiﬁer of the detector; (2) The car region is successfully proposed but misclassiﬁed (e.g., to kite, cake, truck, or potted plant as shown in the examples) or the classiﬁcation score is too low to pass the threshold for the detection score; (3) The car region is successfully proposed and classiﬁed, but the camouﬂage results in an incorrect detection which largely overlaps with the car (cf.
the 5th row where the regions covering the car are detected as a car and a boat, respectively).
One can see that the context or background plays a vital role in object detection.
Although some images capture the Camry by the same pose, the detector makes completely different predictions for them.
Besides, these qualitative results imply that the detector works in a way different from human vision.
In the Landscape environment, our learned camouﬂage has the strongest contrast to the background compared to other baseline patterns.
However, the detector is still sometimes not able to detect the camouﬂaged car or SUV.
C ONCLUSION
In this paper, we investigate whether it is possible to physically camouﬂage 3D objects of complex shapes, i.e., vehicles, in order to hide them from state-of-the-art object detectors.
We conduct extensive experimental studies with a photo-realistic simulation engine.
We propose to use a clone network to mimic the simulator and the detector’s joint response to the 3D vehicles.
Then, we infer a camouﬂage for a 3D vehicle by minimizing the output of the clone network.
Our learned camouﬂage signiﬁcantly reduces the detectability of a Toyota Camry and a SUV.
Moreover, we ﬁnd that the camouﬂage is transferable across different environments.
For future work, We plan to look into possible ways to white-box the entire process so as to propose a more effective camouﬂage.
A CKNOWLEDGMENT
This work was in part supported by the NSF grants IIS-1212948, IIS-1566511, and a gift from Uber
Technologies Inc.
A V IRTUAL SUV IN U RBAN A REA
We then report the camouﬂage performance on the newly modeled SUV in the urban environment in Table
[Table]
Judging by the results
[Table]
it is clear that the Mask R-CNN is a very generalized detector.
The SUV’s baseline color and random camouﬂage detection scores are even higher than the Camry correspondence although the detector has never seen it before.
This might be because the SUV has much less polygon and is built to resemble the shape of a general SUV as shown in Fig.
However, the price for not seeing this vehicle during training is that Mask R-CNN is more likely to be affected by the camouﬂage.
The standard deviation of the random camouﬂage mIoU/ precision is higher (3.26/3.48 vs. 0.8/1.2) than Camry.
And our camouﬂage achieves lower detection precision despite both baseline colors and random camouﬂage’s detection scores are higher than Camry’s.
The detectability is reduced by almost 1.5 times of Camry’s results in this case.
This experiment shows that Mask R-CNN might well generalize to unseen vehicles, but it is easier to get attacked.
B T RANSFERABILITY ACROSS E NVIRONMENTS
Another important question is that what if we transfer the camouﬂage to a not only previously unseen but a totally different environment?
To quantitatively answer this question, we build the
Landscape environment (Fig. 5b) to test our camouﬂages trained in urban environment (Fig. 5a) using the Camry vehicle.
The results are reported in Table.
Some qualitative results are shown in
Fig.
Given the barren landscape with few objects in sight, the detector detects the car better than it did in the urban environment for both baseline colors (82.04 vs. 77.57) and random camouﬂages (77. vs. 71.42) possibly due to the absence of distractions.
However, our directly transferred camouﬂage is still able to beat both of them by more than 46% regarding both detection mIoU and precision without any ﬁne-tuning.
It is interesting to notice that the Camry with grey color looks almost identical to the background in this environment (Fig. 11).
However, it still could be perfectly detected.
Meanwhile, our leaned camouﬂage results in far better stealth despite it has a sharp contrast to the background.
[Table]
[Table]
C T RANSFERABILITY ACROSS V EHICLES
It will be impractical to retrain a speciﬁc camouﬂage for each vehicle whenever we need it.
Hence it would be interesting to look into the transferability between vehicles in this scenario.
We swap the camouﬂages of SUV and Camry and see how they would perform in the urban environment.
We present the testing precision after swapping in Table.
First, both camouﬂages are deﬁnitely transferable.
It is also interesting to see that the Camry learned camouﬂage is not as good as the SUV learned camouﬂage even when being applied on the Camry itself.
This might be due to the fact that the SUV resembles a car with more generic features and hence learning camouﬂage on SUV is less likely to encounter local minima during optimization.
D T RANSFERABILITY ACROSS V IEWING P OSITION
One of the possibly most concerning questions is whether the learned camouﬂage is robust to the change of the camera position.
To answer this question, we set up another 16 new cameras, as shown in Fig.
3, surrounding the vehicle in different relative locations.
We then test the learned camouﬂage’s performance on these new cameras.
The results are shown in Table 6.
Our camouﬂage performance drop has a slightly decrease of 5% from Table 1.
This indicates that the change of the camera locations would impact the performance, but the performance drop is still way beyond the standard deviation of random camouﬂages’ scores.
Given that the new camera views cover more perspectives as shown in Fig. 3, this result is reasonable.
E I MPACT OF C LONE N ETWORK Q UALITY
How does the clone network’s quality affect the camouﬂage’s performance?
Is the alternative optimization necessary?
We quantitatively show the ﬁrst 300 simulation calls of our system in Fig.
9 and meanwhile evaluate the camouﬂages proposed by the clone network.
Note that initially the clone network has already been trained with 800 random camouﬂages.
However, the proposed camouﬂage’s score does not fall until the new camouﬂages from iteration scheme join the optimization.
This suggests that without our iterative optimization mechanism, the clone network could only ﬁnd camouﬂage with mIoU around 70%, which is the same as the random camouﬂage.
Those new samples serve as the hard samples.
They gradually calibrate the clone network’s global minima to V t () ’s and help it to generate better camouﬂages.
Note that although the score descends quicker during the ﬁrst 50 iterations, it does not ﬁnd the global best camouﬂage until near the end (296th iteration).
This graph also shows the classiﬁcation score is a suitable choice to be minimized as it is highly correlated with mIoU.
Note that the system automatically re-initialize the clone network’s parameters to prevent it from falling into local minima during the optimization whenever we add a new sample to the training set
C .
Hence, there are some spikes in the graph.
[Figure]
We can see how the new samples helped the clone network to the minimal.
F D ETECTION A TTENTION
How exactly does our camouﬂage work against the detector?
We partially answer this question by visualizing Mask-RCNN’s completely/partially successful detection attention on our grey and camouﬂaged Camry.
Since our Mask-RCNN implementation does not explicitly yield failed detection’s prediction, we are unable to visualize the failed detection’s attention w.r.t.
the input image if the failed detection does not exist.
There are two main visualization approaches: Grad-CAM (Selvaraju et al., 2017) and saliency (Simonyan et al., 2013).
Approximately speaking, grad-CAM visualizes the gradient of output w.r.t.
penultimate (pre-fully-connected layer) convolutional layer feature map output; Saliency visualizes the gradient of output w.r.t.
initial input image.
Grad-CAM is generally considered superior as the last convolutional layer’s feature map contains much more abstracted semantic information, leading to less noisy visualization.
However, we ﬁnd that it is hard to deﬁne the single “penultimate layer” in Mask-RCNN: It has multiple penultimate layers, tracing back to different stages of the network, prior to the ROI pooling layer.
Each of those penultimate layers contains varying levels of information.
We choose to use saliency in this case.
It is clear how to deﬁne the “attention” in the image classiﬁcation scenario:
It is the gradient heatmap of the classiﬁcation score scalar w.r.t.
the entire input image.
On the other hand, an end-to-end detection neural network often yields multiple structure predictions.
Each structure contains bounding box and classiﬁcation score, etc.
We choose to visualize the gradient of the best bounding-box’s classiﬁcation score w.r.t.
the input image.
Our visualizations are presented in Fig.
It is surprising that the window, roof and upper bodies are playing the predominant role in car detection.
Upper body attention exists even when the upper body is not included in the detection bounding box (row 3).
Given that the classiﬁcation stage makes the classiﬁcation decision based on the proposed feature map box region, such attention must have been already included in the detection proposal before the ROI layer, i.e. detection stage.
This may also explain why it is easier to fail the front-viewing and the rear-view detectors: front-view (row 1) and the rear-view (row 5) detectors place their attention on the hood and trunk, where camouﬂage pattern is presented.
A partially successful attack (row 4) was carried out by wiping out detector’s attention on the car hood.
On the other hand, the side-view detector is harder to attack since the detector merely places any attention of the car body (row 2) where the camouﬂages are mainly located.
However, our camouﬂages on the roof could still fail the side-view detector partially (row 3).
Since we only visualize the (partially) successful detections, there are still many cases to explore.
G S IMULATION S ETUP G. S IMULATION I MPLEMENTATION
The iterative optimization framework works on two Nvidia GTX 1080 Ti in our experiment.
We use one to run the detector and another one to run the simulation and training/ prediction of evaluation network.
The simulation was implemented partially using AirSim by Shah et al.
(2017) and UnrealEnginePython.
All submodules are implemented asynchronously to run in parallel and are communicating with each other using RPC/ RPyC.
All the camouﬂages and the baseline colors are implemented either using or based on the ofﬁcial Unreal Automotive Material.
Each evaluation in the simulation, which is the most time-consuming part, takes around 15 to 20 second.
G. S IMULATION E RROR
Despite our best effort, we observe V t ( c ) come with a standard deviation of 0 .
due to the inherent and mostly necessary random processes in the rendering (i.e., Monte Carlo in path tracing, etc.)
This unfortunately makes V t ( c ) a noisy function.
We reduce this error by repeat sampling V t ( c ) 5 times whenever we use it.
H N ON - LINEARITY AND N ON - CONVEXITY
Since this is a blackbox optimization problem, it is important to examine some important features of the E t V t ( · ) .
We ﬁrst verify its convexity via the convexity deﬁnition.
We test the convexity of E t V t ( · ) by testing the convexity of subsampled correspondence 1 | T S | (cid:80) t ∈ T S V t ( · ) via: ∀ c 1 , c 2 ∈ C : | T S | (cid:88) t ∈ T S
V t ( c 1 + c ) ≤ | T S | (cid:88) t ∈ T S
V t V T S ( c 1 ) + V T S ( c 2 ) (4) where C is a set of camouﬂages.
We sampled 1000 pairs of random camouﬂages from C and half of them do not meet the equation.
Hence 1 | T S | (cid:80) t ∈ T S V t ( · ) is nonconvex.
Besides, we ﬁnd a simple linear MLP is insufﬁcient to approximate 1 | T S | (cid:80) t ∈ T S V t ( · ) , which empirically shows it is nonlinear.
I S UPPLEMENTAL F IGURES
Input : Clone network parameter V θ ( · ) ; Simulation and detection V T S ( · ) ; Transformation T S which are parameterized as rendered background and foreground images; Regularization tradeoff α ; Random camouﬂage set C R .
weights
[Table]
N o t d e t ec t e d “ t r u c k ” “ po tt e d p l a n t ”
P a r ti a ll y d e t ec t e d “ bo a t ” & “ca r ” “ca k e” & “ca r ”
N o t d e t ec t e d “ t r u c k ”
N o t d e t ec t e d “ k it e”
N o t d e t ec t e d
Grey
Random
[Figure]