以神经网络模型为载体的鲁棒隐写方法

Robust steganography for neural network models

神经网络已广泛应用于各个领域,神经网络模型隐写是近年来学术界新兴的研究方向。嵌入容量与鲁棒性是神经网络模型隐写的重要指标,但难以同时兼顾。为此,本文提出了一种以神经网络模型为载体的鲁棒模型隐写方法。不明显降低模型原始任务性能的情况下,发送者在训练过程中将秘密信息嵌入到神经网络中,而不是在神经网络训练完成后通过修改网络参数嵌入。接收者使用解码网络提取秘密信息,解码网络的参数使用唯一的嵌入密钥生成,因此无需秘密地向接收者传送解码网络。此外,本文还引入了RS码,提高数据提取的鲁棒性。实验结果表明,所提出的模型隐写方法将嵌入容量增大了66.6%的同时增强了鲁棒性。

Neural networks have been extensively utilized in various fields,steganography for neural network is a research emerging direction in academia in recent years.Embedding capacity and robustness are important indicators for steganography.But balancing embedding capacity and robustness is challenging.This paper proposes a robust steganography for neural network models.Embedding secret data into neural network without visibly reducing the performance of the original task.This is achieved by embedding secret data during the training process instead of modifying the network parameters after training.Receivers can obtain the secret data from data decoding networks,the parameters of data decoding networks are generated using the embedding keys.In this way,it is unnecessary to transmit the decoding networks secretly.Additionally,introducing reed-solomon codes to improve data extraction robustness.Experimental results reveal that the robust steganography for neural models improves robustness while maintaining superior embedding capacity.