  In recent years, the neural network backdoor hidden in the parameters of the
federated learning model has been proved to have great security risks.
Considering the characteristics of trigger generation, data poisoning and model
training in backdoor attack, this paper designs a backdoor attack method based
on federated learning. Firstly, aiming at the concealment of the backdoor
trigger, a TrojanGan steganography model with encoder-decoder structure is
designed. The model can encode specific attack information as invisible noise
and attach it to the image as a backdoor trigger, which improves the
concealment and data transformations of the backdoor trigger.Secondly, aiming
at the problem of single backdoor trigger mode, an image poisoning attack
method called combination trigger attack is proposed. This method realizes
multi-backdoor triggering by multiplexing combined triggers and improves the
robustness of backdoor attacks. Finally, aiming at the problem that the local
training mechanism leads to the decrease of the success rate of backdoor
attack, a dual model replacement backdoor attack algorithm based on federated
learning is designed. This method can improve the success rate of backdoor
attack while maintaining the performance of the federated learning aggregation
model. Experiments show that the attack strategy in this paper can not only
achieve high backdoor concealment and diversification of trigger forms under
federated learning, but also achieve good attack success rate in multi-target
attacks.door concealment and diversification of trigger forms but also achieve
good results in multi-target attacks.
