CN112085055A - Black box attack method based on migration model Jacobian array feature vector disturbance - Google Patents
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Abstract
The invention provides a black box attack method based on migration model Jacobian array feature vector disturbance, and belongs to the technical field of machine learning system security and black box attack. The method comprises the steps of firstly determining a black box model to be attacked and a migration pre-training model, obtaining an original sample to be attacked and a label of the original sample, continuously applying disturbance to the original sample, continuously updating the disturbance through iterative computation by using a singular value decomposition result of a Jacobian matrix calculated by the migration pre-training model, and finally enabling the sample after the disturbance is applied to be not corresponding to a correct label through black box model classification. The method has the characteristic that only one migratable pre-training network is needed without any training sample, and can greatly improve the attack efficiency of the traditional black box model.
Description
Technical Field
The invention belongs to the technical field of machine learning system security and black box attack, and particularly provides a black box attack method based on transfer model Jacobian array feature vector disturbance.
Background
With the development of deep learning, the security problem related to the deep learning system gradually raises the attention of the machine learning community. Since the provider of the general deep learning system does not disclose the specific implementation inside the system, the black box attack is often an effective attack means for the deep learning system. Specifically, the black box attack constructs a series of system input samples through iteration, gradually reduces the recognition degree of the deep learning system to the samples under the condition that the difference between each input sample and the sample to be attacked is small, and finally outputs classification complete errors when the samples are input for a certain time. In robust learning, we call the input sample at the end of this process the challenge sample. The common evaluation criteria of the black box model are attack efficiency, including the average number of times of inquiring the black box model to attack each sample, the average disturbance distance of the counterattack sample relative to the original sample to be attacked and the overall attack success rate.
The black box attack has rich application in practical scenes, for example, in computer vision, the black box attack can slightly disturb a specific image, so that a neural network which originally can correctly classify an original image can make wrong classification judgment on the disturbed image, and a human visual system cannot usually detect the difference between the images before and after disturbance. The exploration aiming at the black box attack of the image can promote the deeper exploration of robust learning in the machine learning boundary so as to prevent misjudgment of a deep learning system in the practical computer vision application, such as an intelligent driving system, a face recognition system and the like.
Conventional black box attack techniques include black box attacks based on white box network migration and black box attacks based on zeroth order gradient optimization. In the former scheme, a white-box network is generally trained by some training samples, and then each step of iteration of black-box attack is guided by using known white-box network parameters, which is characterized in that a large number of pre-training samples are needed, and the pre-training samples are preferably closer to the classification task of the black-box network; in the latter scheme, the gradient of the black box network at a certain input point is estimated by a sampling mode usually by using the thought of zero-order gradient optimization, so that the gradient is decreased to search for a countermeasure sample iteratively.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a black box attack method based on migration model Jacobian array feature vector disturbance. The method has the characteristic that only one migratable pre-training network is needed without any training sample, and can greatly improve the attack efficiency of the traditional black box model.
The invention provides a black box attack method based on migration model Jacobian array feature vector disturbance, which is characterized by comprising the following steps:
1) determining a black box model F to be attacked;
determining migration pre-training models
Wherein the function h is from the input layer to the characterization layer of the migration pre-training model, and the function g is from the characterization layer to the output layer;
selecting an input sample to be attacked and a label corresponding to the input sample to be attacked as (x, y), wherein x represents the input sample to be attacked, and y is the label corresponding to x; taking the input sample to be attacked as an original sample; setting disturbance step length alpha and selecting the number K of eigenvectors in each round;
2) inputting an original sample x into a black box model F, and calculating a corresponding output probability vector p ═ p (· | x) of the original sample passing through the black box model F;
let 0 denote the perturbation applied to the original sample, generating sample x +;
3) inputting the sample x + into a migration pre-training model, and calculating a Jacobian matrix J (J) of a function h corresponding to the sampleh(x+)；
4) Performing singular value decomposition on the Jacobian matrix J obtained in the step 3) to obtain corresponding first K normalized right eigenvalue vectors V1，...，VKLet i equal to 1;
5) the value of i is determined: if i is less than or equal to K, entering the step 6); otherwise, returning to the step 3) again;
6) iteratively calculating disturbance, and finally enabling the sample x + not to correspond to the correct label y through black box model classification, wherein the method comprises the following specific steps:
6-1) let x + be along vector ViThe negative direction moving length of the model F is the distance of the single disturbance step length alpha, and the corresponding negative output probability vector p of the black box model F is calculatedneg＝p(·|x+-αVi)；
6-2) judging whether p is satisfiedneg，y＜pyWherein p isyRepresenting the output probability, p, corresponding to the label y in the vector pneg，y: representing a vector pnegThe output probability corresponding to the middle label y; if yes, the negative disturbance is effective, and the step 6-3) is carried out, otherwise, the step 6-4) is carried out;
6-3) updating the probability vector p ═ pnegLet i be i +1 and update the perturbation as- α ViEntering step 6-8);
6-4) along vector ViThe forward direction moving length is the distance of single disturbance step length alpha, and the corresponding forward output probability vector p of the black box model is calculatedpos＝p(·|x++αVi)；
6-5) judging whether p is satisfiedpos，y＜pyWherein p ispos，yRepresenting a vector pnegThe output probability corresponding to the middle label y; if yes, the positive disturbance is effective, and the step 6-6) is carried out; otherwise, entering step 6-7);
6-6) updating the probability vector p ═ pposLet i be i +1, update the perturbation to + α ViEntering step 6-8);
6-7) keeping the probability vector p and the disturbance unchanged by setting i to i +1, and entering a step 6-8);
6-8) determination y ≠ argmaxy′py′Whether or not: if yes, the label corresponding to the maximum probability component in the probability vector p is not y, the black box attack is successful, and the step 7) is carried out; if not, the black box attack is unsuccessful, and the step 5) is returned again;
7) and returning the disturbance as effective disturbance for making the black box model F make error classification judgment on the original sample x, wherein the sample x + at the moment is a countermeasure sample of the black box model F, and ending the method.
The invention has the characteristics and beneficial effects that:
the invention provides a novel black box attack method. By using the method, an attacker only needs one pre-trained model network structure and network parameters, and higher attack efficiency and lower attack cost are achieved. The invention does not need any pre-training sample to further adjust the network, thereby saving the time and cost for acquiring the training sample and training. A more efficient black box attack is guided by this pre-trained model. Experiments show that the attack efficiency of the black box attack based on the zero-order gradient optimization can be better than that of the black box attack based on the zero-order gradient optimization through the information of the pre-training model, and the black box attack technology is simpler and more convenient than the black box attack technology based on the white box network migration and needing to be trained in practical application due to the fact that any pre-training sample does not need to be collected.
The invention relates to a large application scene of attacking pictures in computer vision, which is specifically implemented by adding a synthesized micro disturbance to a target picture, so that a neural network wrongly classifies the disturbed pictures, and a human visual system can hardly distinguish the changes of the pictures before and after the disturbance because the disturbance is small enough.
The conditions for the user to use the method are as follows: in a black box model attack scenario, the information that the user can utilize is a pre-trained white box model, and the task relationship between the white box model and the black box model is very close. In addition, it is difficult to collect training data related to this task. For example, one black-box image classification system using a convolutional neural network architecture is attacked. The pre-training model of the image can be easily obtained, a plurality of pre-training models formed by training on ImageNet exist, and if the pre-training models are subjected to fine adjustment or structural modification, a large number of image samples need to be acquired, the training process is time-consuming, and the application scene of rapidly realizing the attack on the black box image classification system is very unfavorable. And our method can just as well adapt to this scenario.
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FIG. 1 is an overall flow diagram of the method of the present invention.
Detailed Description
The black box attack method based on the disturbance of the Jacobian matrix characteristic vector of the migration model provided by the invention is described in detail by combining the attached drawings and an embodiment as follows:
the invention provides a black box attack method based on migration model Jacobian array feature vector disturbance, which is suitable for any universal black box attack model. In this embodiment, ResNet-50 is used to perform black box attack on ImageNet image samples, and pretrained ResNet-18 is used as a migration pretrained model (where the migration pretrained model and the black box model belong to the same class model; for example, if the black box model to be attacked is an image classification model, the migration pretrained model is also an image classification model, and when the task correlation of the two models is stronger, the performance of the method of the present invention is better).
The invention provides a black box attack method based on migration model Jacobian array feature vector disturbance, the whole process is shown as figure 1, and the method comprises the following steps:
1) determining a black box model F to be attacked, wherein the model F is ResNet-50 of the black box, and the structure and parameters of the black box model are unknown due to the characteristics of the black box;
determining
This example refers to ResNet-18 with white-box, where the function h is from the input layer to the characterization layer of the migration pre-trained model (512-dimensional characterization layer after ResNet-18 has been subjected to successive convolutional layers and average pooling), and the function g is from the characterization layer to the output layer;
selecting an input sample to be attacked and a label corresponding to the input sample to be attacked as (x, y), wherein x represents the input sample to be attacked, and y is the label corresponding to x; taking the input sample to be attacked as an original sample, selecting an image sample in an ImageNet verification set as the input sample to be attacked and acquiring a corresponding label; setting a disturbance step length a and selecting a feature vector number K for each round (the value of K is related to the dimension x, and the higher the dimension x is, the larger the value of K is, and K is 100 in this embodiment).
2) Inputting an original sample x into a black box model F, and calculating a corresponding output probability vector p ═ p (· | x) of the original sample passing through the black box model F;
let 0 denote the perturbation applied to the original sample, which is updated as the algorithm iterates, generating sample x +.
3) Inputting the sample x + into a migration pre-training model, and calculating a Jacobian matrix J (J) of a function h corresponding to the sampleh(x+)。
4) Performing singular value decomposition on the Jacobian matrix J obtained in the step 3) to obtain corresponding first K normalized right eigenvalue vectors V1，...，VKLet i equal to 1;
5) the value of i is determined: if i is less than or equal to K, entering the step 6); otherwise, returning to the step 3), at this time, the disturbance updating iteration of the current round is completely finished, and the jacobian matrix needs to be recalculated at the updated sample x + and the next round of disturbance updating iteration is started;
6) iteratively calculating disturbance, and finally enabling the sample x + not to correspond to the correct label y through black box model classification, wherein the method comprises the following specific steps:
6-1) let x + be along vector ViThe negative direction moving length of the model F is the distance of the single disturbance step length alpha, and the corresponding negative output probability vector p of the black box model F is calculatedneg＝p(·|x+-αVi)；
6-2) judging whether p is satisfiedneg，y＜pyWherein p isyRepresenting the output probability, p, corresponding to the label y in the vector pneg，y: representing a vector pnegThe output probability corresponding to the middle label y; if yes, then the judgment probability of the black box model on the real label of the disturbed sample can be reduced by negative disturbance, the step 6-3 is carried out if the negative disturbance is effective, and otherwise, the step 6-4 is carried out);
6-3) updating the probability vector p ═ pnegLet i be i +1 and update the perturbation as- α ViEntering step 6-8);
6-4) along vector ViThe forward direction moving length is the distance of single disturbance step length alpha, and the corresponding forward output probability vector p of the black box model is calculatedpos＝p(·|x++αVi)；
6-5) judging whether p is satisfiedpos，y＜pyWherein p ispos，yRepresenting a vector pnegMiddle labely corresponding output probability; if so, indicating that the judgment probability of the black box model on the real label of the disturbed sample can be reduced by the forward disturbance, and entering the step 6-6 if the forward disturbance is effective; otherwise, entering step 6-7);
6-6) updating the probability vector p ═ pposLet i be i +1, update the perturbation to + α ViEntering step 6-8);
6-7) at this time, both positive disturbance and negative disturbance can not reduce the judgment probability of the black box model on the true label of the disturbed sample, so that only i is set as i +1, the probability vector p and the disturbance are kept unchanged, and the step 6-8) is carried out;
6-8) determination y ≠ argmaxy′py′Whether the label corresponding to the maximum probability component in the probability vector p is satisfied or not is represented on the right side of the equation, and the function of the equation is to determine whether the label corresponding to the maximum probability component in the probability vector p at this time is no longer the original label y of the original sample x: if yes, the black box attack is successful, and the step 7) is carried out; if not, the black box attack is unsuccessful, and the step 5) is returned again;
7) at this time, the disturbance applied to the original sample can make an error classification judgment on the black box model F, the disturbance is returned as an effective disturbance which makes the black box model F make the error classification judgment on the original sample x, the sample x + at this time is a countermeasure sample of the black box model F (in this embodiment, the countermeasure sample is a picture which makes ResNet-50 of the black box make the error classification), and the method ends.
Claims (1)
1. A black box attack method based on migration model Jacobian array eigenvector disturbance is characterized by comprising the following steps:
1) determining a black box model F to be attacked;
determining migration pre-training models
Wherein the function h is from the input layer to the characterization layer of the migration pre-training model, and the function g is from the characterization layer to the output layer;
selecting an input sample to be attacked and a label corresponding to the input sample to be attacked as (x, y), wherein x represents the input sample to be attacked, and y is the label corresponding to x; taking the input sample to be attacked as an original sample; setting disturbance step length alpha and selecting the number K of eigenvectors in each round;
2) inputting an original sample x into a black box model F, and calculating a corresponding output probability vector p ═ p (· | x) of the original sample passing through the black box model F;
let 0 denote the perturbation applied to the original sample, generating sample x +;
3) inputting the sample x + into a migration pre-training model, and calculating a Jacobian matrix J (J) of a function h corresponding to the sampleh(x+)；
4) Performing singular value decomposition on the Jacobian matrix J obtained in the step 3) to obtain corresponding first K normalized right eigenvalue vectors V1，...，VKLet i equal to 1;
5) the value of i is determined: if i is less than or equal to K, entering the step 6); otherwise, returning to the step 3) again;
6) iteratively calculating disturbance, and finally enabling the sample x + not to correspond to the correct label y through black box model classification, wherein the method comprises the following specific steps:
6-1) let x + be along vector ViThe negative direction moving length of the model F is the distance of the single disturbance step length alpha, and the corresponding negative output probability vector p of the black box model F is calculatedneg＝p(·|x+-αVi)；
6-2) judging whether p is satisfiedneg，y＜pyWherein p isyRepresenting the output probability, p, corresponding to the label y in the vector pneg，y: representing a vector pnegThe output probability corresponding to the middle label y; if yes, the negative disturbance is effective, and the step 6-3) is carried out, otherwise, the step 6-4) is carried out;
6-3) updating the probability vector p ═ pnegLet i be i +1 and update the perturbation as- α ViEntering step 6-8);
6-4) along vector ViThe forward direction moving length is the distance of single disturbance step length alpha, and the corresponding forward output probability vector p of the black box model is calculatedpos＝p(·|x++αVi)；
6-5) judging whether p is satisfiedpos，y＜pyWherein p ispos，yRepresenting a vector pnegThe output probability corresponding to the middle label y; if yes, the positive disturbance is effective, and the step 6-6) is carried out; otherwise, entering step 6-7);
6-6) updating the probability vector p ═ pposLet i be i +1, update the perturbation to + α ViEntering step 6-8);
6-7) keeping the probability vector p and the disturbance unchanged by setting i to i +1, and entering a step 6-8);
6-8) determination y ≠ argmaxy′py′Whether or not: if yes, the label corresponding to the maximum probability component in the probability vector p is not y, the black box attack is successful, and the step 7) is carried out; if not, the black box attack is unsuccessful, and the step 5) is returned again;
7) and returning the disturbance as effective disturbance for making the black box model F make error classification judgment on the original sample x, wherein the sample x + at the moment is a countermeasure sample of the black box model F, and ending the method.
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| CN113380255B (en) * | 2021-05-19 | 2022-12-20 | 浙江工业大学 | Voiceprint recognition poisoning sample generation method based on transfer training |
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| CN114693732A (en) * | 2022-03-07 | 2022-07-01 | 四川大学华西医院 | Weak and small target detection and tracking method |
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| CN116523032A (en) * | 2023-03-13 | 2023-08-01 | 之江实验室 | Image text double-end migration attack method, device and medium |
| CN116523032B (en) * | 2023-03-13 | 2023-09-29 | 之江实验室 | Image text double-end migration attack method, device and medium |
| CN116504069A (en) * | 2023-06-26 | 2023-07-28 | 中国市政工程西南设计研究总院有限公司 | Urban road network capacity optimization method, device and equipment and readable storage medium |
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