  The utilization of personal sensitive data in training face recognition (FR)
models poses significant privacy concerns, as adversaries can employ model
inversion attacks (MIA) to infer the original training data. Existing defense
methods, such as data augmentation and differential privacy, have been employed
to mitigate this issue. However, these methods often fail to strike an optimal
balance between privacy and accuracy. To address this limitation, this paper
introduces an adaptive hybrid masking algorithm against MIA. Specifically, face
images are masked in the frequency domain using an adaptive MixUp strategy.
Unlike the traditional MixUp algorithm, which is predominantly used for data
augmentation, our modified approach incorporates frequency domain mixing.
Previous studies have shown that increasing the number of images mixed in MixUp
can enhance privacy preservation but at the expense of reduced face recognition
accuracy. To overcome this trade-off, we develop an enhanced adaptive MixUp
strategy based on reinforcement learning, which enables us to mix a larger
number of images while maintaining satisfactory recognition accuracy. To
optimize privacy protection, we propose maximizing the reward function (i.e.,
the loss function of the FR system) during the training of the strategy
network. While the loss function of the FR network is minimized in the phase of
training the FR network. The strategy network and the face recognition network
can be viewed as antagonistic entities in the training process, ultimately
reaching a more balanced trade-off. Experimental results demonstrate that our
proposed hybrid masking scheme outperforms existing defense algorithms in terms
of privacy preservation and recognition accuracy against MIA.
