Below are the abstract, introduction, and conclusion of a computer science research paper. Please summarize the main contribution of the work in a single sentence. Your response should include the summary and no additional text.

Paper text:

It is widely known that well-designed perturbations can cause state-of-the-art machine learning classifiers to mis-label an image, with sufficiently small perturbations that are imperceptible to the human eyes.
However, by detecting the inconsistency between the image and wrong label, the human observer would be alerted of the attack.
In this paper, we aim to design attacks that not only make classifiers generate wrong labels, but also make the wrong labels imperceptible to human observers.
To achieve this, we propose an algorithm called LabelFool which identifies a target label similar to the ground truth label and finds a perturbation of the image for this target label.
We first find the target label for an input image by a probability model, then move the input in the feature space towards the target label.
Subjective studies on ImageNet show that in the label space, our attack is much less recognizable by human observers, while objective experimental results on ImageNet show that we maintain similar performance in the image space as well as attack rates to state-of-the-art attack algorithms.
Deep neural networks are powerful learning models that achieve state-of-the-art pattern recognition performance in classification tasks (Krizhevsky et al., 2012b; LeCun et al., 2010; He et al., 2016) .
Nevertheless, it is found that adding well-designed perturbations to original samples can make classifiers of deep neural networks fail (Szegedy et al., 2013) .
These kinds of samples are called adversarial samples.
Techniques for generating adversarial samples are called attackers.
We think the ideal attacker should satisfy three levels of requirements.
The first requirement is fooling networks which means making classifiers fail to classify an image successfully.
For example, a dog image can be classified as a cat after added some well-designed perturbations.
There are a number of methods for achieving a high attack rate (Goodfellow et al., 2015; Carlini & Wagner, 2017; Dong et al., 2018) .
The second requirement for the ideal attacker is the imperceptibility in the image space.
This means the magnitude of perturbations in the pixel level needs to be as tiny as possible so that it is imperceptible to human eyes.
For example, additive perturbations are minimized with l p norm to generate imperceptible adversarial samples (Seyed-Mohsen et al., 2016) .
Extreme cases also exist where only changing one or a few pixels (Su et al., 2019; Modas et al., 2019) can make classifiers fail.
Moosavi-Dezfooli et al. (2017) even show the existence of universal (image-agnostic) perturbations.
The third requirement for the ideal attacker, which is newly proposed in this paper, is the imperceptibility of the error made by the classifier in the label space.
It means making the classifier to mis-classify an image as the label which is similar to its ground truth, so that people won't notice the misclassification.
For example, in Figure 1 , a human user will probably ignore the mis-classification if an attacker caused a "church" to be mis-classified as a "monastery" as the third attacker does.
However, a human user will easily notice the mistake if an attacker caused a "church" to be misclassified as a "dome" as the second attacker does or caused an apparent perturbation in the image space as the first attacker does.
In real applications, a human user will take defensive measures as soon as he notices the attack.
Therefore making the whole attack process imperceptible is crucial for letting observers' guard down.
Tiny perturbations in the image space but large perturbations in the label space can muddle through on the input terminal.
But as soon as observers check on the output terminal and see the obviously-incorrect label for an input, they will realize that the classifier fail due to some attacks and take defensive measures immediately, just as Figure 1 shows.
This justifies the power of attacks which also confuse people in the label space.
So the imperceptibility in the label space is quite important.
However, to our best knowledge, few attackers have realized this point.
In this paper, we propose an untargeted-attack algorithm called LabelFool, to perturb an image to be mis-classified as the label which is similar to its ground truth, so that people won't notice the misclassification.
In the meantime, LabelFool also guarantees the imperceptibility in the image space as well as maintaining a high attack rate in fooling classifiers.
There are two steps by which we accomplish our goal.
The first step is to choose a target label which is similar to the input image's ground truth.
The second step is to perturb the input to be classified as this target label.
The way is finding the classification boundary between the current label and the target label, and then moving the input towards this boundary until it is classified as the target label.
We conduct a subjective experiment on ImageNet (Deng et al., 2009 ) which shows that adversarial samples generated by our method are indeed much less recognizable in the label space by human observers than other attacks.
We also perform objective experiments on ImageNet to demonstrate that adversarial samples generated by LabelFool still guarantee the imperceptibility in the image space as well as maintaining a high attack rate in fooling classifiers.
Conclusion.
In this study, we pay attention to tiny perturbations in the label space.
To our best knowledge, we are the first one who points out the importance of the imperceptibility in the label space for adversarial samples.
Furthermore, we explore a feasible method named LabelFool to identify a target label "similar" with an input image's ground truth and perturb the input image to be mis-classified as this target label so that a human observer will overlook the misclassification and lower the vigilance of defenses.
Our experiments show that, while LabelFool is a little behind DeepFool in the image space, it is much imperceptible in the label space to human observers.
Since we adopt Importance Sampling instead of MLE only in traditional method, the success rate of attack also get gains.
Further discussion.
In this paper, we just propose a feasible way to generate adversarial samples which can confuse people in the label space.
However, there is room for improvement in our approach.
Our results provide the following avenues for future research.
• The perceptual features can be optimized by a well-designed loss function which can improve the accuracy rate in finding nearest label ulteriorly.
• We only consider perceptual distance in this paper, but semantic distance also has its significance for reference of confusing people in the label space.
We may take the semantic tree into consideration and make a trade off between perceptual distance and semantic distance in future research.
A AN INTERFACE PRESENTATION OF THE SUBJECTIVE EXPERIMENT Figure 6 shows the interface of our subjective experiments.
Figure 7 shows three examples for animal classes to demonstrate that LabelFool makes fine-grained changes but other methods make some ridiculous changes instead.
C ORIGINAL DATA FOR FIGURE 3 AND 4 Table 3 is the original data for Figure 3 .
The original data of perceptibility, perceptual similarity, PieAPP in Figure 4 is reported in Table 4 , 5, 6 respectively.
It is provided for the sake of convince if anyone wants to rewrite Figure 3 or 4.
This is an example to illustrate why our method has the highest attack rate.
We only give an example of DeepFool and LabelFool.
SparseFool and FGSM have similar effects with DeepFool.
Figure 8 is an example where the classifier fail to give a correct classification for the input image x.
The ground truth of x is class 2 while the predicted class is class 3.
In this example, DeepFool takes class 3 as the true class.
Then DeepFool finds the nearest class to class 3 in the feature space which is class 2 in this example, and moves the input image towards class 2.
When the perturbed image is classified as class 2 which is different from the predicted class, DeepFool considers the attack succeed and stops the algorithm.
However, it fails to attack actually because class 2 is the true class of x.
A trick on adversarial samples so that the mis-classified labels are imperceptible in the label space to human observers