Method for a generative adversarial network with different hierarchical function combinations

Abstract

The invention discloses a method for a generative adversarial network with different hierarchical function combinations, training data is destroyed through data poisoning, and finally the whole learning process is destroyed, so that the poisoning effect of a poisoning sample is effectively improved. According to the method, a combined loss function is used, and data are processed in different stages, so that the purpose of misrecognition is achieved. Meanwhile, in order to further achieve the effect of picture misrecognition, comparative learning in the field of self-supervised learning is applied to the process of intelligent model training. According to the method, a data pair mode is used during data loading, and a feature integration mode is used in a training process to process the data, so that the poison ability during data pollution is enhanced, and the attack effect under the condition that an attack pattern is not identified by naked eyes as far as possible is ensured.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for confronting generation networks with different level function combinations. Background technique [0002] In recent years, with the advancement of deep learning technology, it has even surpassed human-level performance in many tasks, so it has been widely used in daily life, such as face recognition, fingerprint recognition and network security, as well as those with high failure costs. applications such as autonomous driving. However, studies have shown that deep neural networks are extremely vulnerable to adversarial examples. By adding some small perturbations to images or detection targets, attackers can cause neural networks to learn inaccurate features and eventually get wrong prediction results. The process is called an "adversarial attack," in which an adversarial example is a modified version of a clean image that is deliberately perturbed (such as...

AI Technical Summary

Problems solved by technology

For example, DeepConfuse can successfully deceive the image recognition system. However, since the influence of features, gradients and other levels are not comprehensively considered, the accuracy and generation efficiency still need to be improved.

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