A step size self-adaptive attack resisting method based on model extraction

Abstract

The invention discloses a step size self-adaptive attack resisting method based on model extraction. The step size self-adaptive attack resisting method comprises the following steps: step 1, constructing an image data set; Step 2, training a convolutional neural network for the image set IMG to serve as a to-be-attacked target model, step 3, calculating a cross entropy loss function, realizing model extraction of the convolutional neural network, and initializing a gradient value and a step length g1 of an iterative attack; Step 4, forming a new adversarial sample x1; 5, recalculating the cross entropy loss function, and updating the step length of adding the confrontation noise in the next step by using the new gradient value; Step 6, repeatedly the process of inputting images, calculating cross entropy loss function, computing the step size, updating the adversarial sample; repeatedly operating the step 5 for T-1 timeS, obtaining a final iteration attack confrontation sample x'i, and inputting the confrontation sample into the target model for classification to obtain a classification result N (x'i). Compared with the prior art, the method has the advantages that a better attackeffect can be achieved, and compared with a current iteration method, the method has higher non-black box attack capability.

Description

technical field [0001] The invention relates to the field of machine learning security technology, in particular to a non-black-box anti-iterative attack method oriented to a deep image recognition system. Background technique [0002] In recent years, with the continuous progress and development of machine learning theory and technology, especially breakthroughs in the fields of computer vision and multimedia, technologies such as medical image processing, biological image recognition, and face recognition have been widely used. However, the rapid development of the field of machine learning also brings many security problems. In systems closely related to security and privacy, such as autonomous driving, health systems, and financial systems, the security of machine learning poses a potential threat to people's vital interests and even life. Therefore, how to maintain the security of machine learning systems and how to protect user privacy has become the basis for the dev...

AI Technical Summary

Problems solved by technology

At the same time, the existing methods use the gradient information obtained by the model extraction at each step only to calculate the sign of the gradient value

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