Research And Application Of Defense-GAN Method Based On Image Recongnition In Federated Learning

In recent years,Federated Learning has attracted much attention because it solves the problem of data silos in machine learning to a certain extent.It allows organizations to jointly train models without sharing private data,ensuring data privacy and data security.However,studies have shown that the process of Federated Learning is still subject to various attacks,leaking the privacy of participants in model training.Among them,the attack based on Generative Adversarial Network poses a great threat to the privacy security of Federated Learning image recognition,causing the victim to be exposed without knowing it.This is undoubtedly a challenge for many fields that need to apply Federated Learning to build shared and customized image models,so it is extremely important to build a safe Federated Learning framework.For the above problems,this paper proposes the following three innovative points.First,in response to the privacy leakage issue in the current Federated Learning image recognition model,this paper proposes a defense method Defense-GAN,which uses the SHAP image masking algorithm to against Generative Adversarial Network attacks under Federated Learning by comparing different image processing algorithms.Under this defense method,the attacker cannot learn the real image data distribution.Finally,the effectiveness of the proposed defense method is verified by comparing it with a Federated Learning image recognition model without applying any defense method.Second,since the defense method Defense-GAN using image masking will have a certain impact on the accuracy of the Federated Learning model,this paper proposes an improved Defense-GAN defense architecture that can improve the accuracy of the model.By using CutMix to crop and stitch the original image and the masked image,a new image dataset is formed,which is used for federated model training and improves the generalization ability of the model.At the same time,different image datasets are constructed for federated model training,and the accuracy of the final model is compared.It is found that the proposed defense method can not only defend against Generative Adversarial Network attacks in Federated Learning,but also ensure that the accuracy of the model is barely affected.Third,for the application of Federated Learning,due to reasons such as intellectual property rights,privacy protection,and data security,data cannot be directly aggregated to train machine learning models in many application fields.Therefore,this paper conducts detailed research and analysis on Federated Learning in face recognition.At the same time,this paper applies the proposed Defense-GAN defense method to specific scenarios and conducts a detailed analysis of the security risks of Generative Adversarial Network attacks in this scenario,discussing the defense role of the proposed Defense-GAN defense method in it.This paper mainly proposes a defense method against Generative Adversarial Network attack under Federated Learning image recognition,and the proposed defense method can not only resist the attack,protect the privacy of the client,but also ensure that the accuracy of the federated model will not be greatly affected.