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Method and device for generating small adversarial patches

A patch and small technology, applied in the field of machine learning, can solve the problem of small feature difference and performance loss

Active Publication Date: 2021-03-30
BEIJING REALAI TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing anti-patch attack performance improvement methods do not focus on solving the performance loss caused by the smaller feature difference.

Method used

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  • Method and device for generating small adversarial patches
  • Method and device for generating small adversarial patches
  • Method and device for generating small adversarial patches

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0050] see figure 1 and figure 2 , providing a small-scale anti-patch generation method, which can be used for products or projects that generate anti-patch based on neural network gradient descent. The small-scale anti-patch generation method includes the following steps:

[0051] S1. Given an adversarial patch image, randomly initialize the adversarial patch image;

[0052] S2. Add the initialized confrontation patch image to the pasting area selected on the target object in the training data, and make it into a confrontation sample;

[0053] S3. Sending the adversarial samples into the deep learning model for extracting adversarial features, and sending benign samples without added adversarial patch images into the deep learning model for extracting benign features;

[0054] S4. Input the adversarial features and the benign features together into a feature enhancement loss function to perform loss calculation, and obtain a loss result;

[0055] S5. Add the loss result t...

Embodiment 2

[0095] see image 3 , providing a small-scale anti-patch generation device, using embodiment 1 or any possible implementation of the small-scale anti-patch generation method, including:

[0096] The anti-patch initialization module 1 is used to randomly initialize the anti-patch image for a given anti-patch image;

[0097] The confrontation sample generation module 2 is used to add the initialized confrontation patch image to the pasting area selected on the target object object in the training data, and make it into a confrontation sample;

[0098]The confrontation feature extraction module 3 is used to transport the confrontation sample into the deep learning model for confrontation feature extraction;

[0099] The benign feature extraction module 4 is used to feed the benign samples without added confrontation patch images into the deep learning model for benign feature extraction;

[0100] The loss result acquisition module 5 is used to jointly input the confrontation fe...

Embodiment 3

[0106] A computer-readable storage medium is provided, wherein the computer-readable storage medium stores program codes for Deepfake detection generated by small-scale anti-patches, and the program codes are included in the implementation of Embodiment 1 or any possible implementation thereof. Instructions for small adversarial patch generation methods.

[0107] The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server, a data center, etc. integrated with one or more available media. The available medium may be a magnetic medium (for example, a floppy disk, a hard disk, or a magnetic tape), an optical medium (for example, DVD), or a semiconductor medium (for example, a solid state disk (SolidStateDisk, SSD)) and the like.

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Abstract

A small confrontation patch generation method and device, by randomly initializing the confrontation patch image, adding the initialized confrontation patch image to the selected pasting area on the target object in the training data to make a confrontation sample; sending the confrontation sample into the depth The learning model performs adversarial feature extraction, and the benign samples without adversarial patch images are sent to the deep learning model for benign feature extraction; the adversarial features and benign features are jointly input into the feature enhancement loss function for loss calculation, and the loss result is obtained; It is added to the model loss function, and the pixel value of the adversarial patch is updated by the optimizer after backpropagation; after a preset number of iterations, the adversarial patch makes the deep learning model output the wrong result, and the adversarial patch processing process is ended. The invention can make the size of the confrontation patch in the physical world smaller, reduce the production cost, reduce the identifiability of the confrontation patch, and make it easier to break through the detection-based defense method.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a method and device for generating a small-scale confrontation patch. Background technique [0002] An adversarial patch is an adversarial example used to attack a deep learning model in the physical world and make the model output wrong results. The anti-patch with a large area has a significant attack effect, but the anti-patch with a large area is easy to be identified, and it is difficult to break through the defense method based on feature detection. Therefore, reducing the area and reducing the recognition degree have become the development trend of anti-patch. However, shrinking the area of ​​the adversarial patch will sharply reduce the attack success rate. [0003] At this stage, methods to improve the effect of anti-patch attacks can be found in some work on attacking deep learning models in the physical world based on anti-patch. These works include the phy...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06N3/08G06N3/04G06K9/46
CPCG06N3/084G06V10/40G06N3/045
Inventor 李连吉田天
Owner BEIJING REALAI TECH CO LTD
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