  In the contemporary of deep learning, where models often grapple with the
challenge of simultaneously achieving robustness against adversarial attacks
and strong generalization capabilities, this study introduces an innovative
Local Feature Masking (LFM) strategy aimed at fortifying the performance of
Convolutional Neural Networks (CNNs) on both fronts. During the training phase,
we strategically incorporate random feature masking in the shallow layers of
CNNs, effectively alleviating overfitting issues, thereby enhancing the model's
generalization ability and bolstering its resilience to adversarial attacks.
LFM compels the network to adapt by leveraging remaining features to compensate
for the absence of certain semantic features, nurturing a more elastic feature
learning mechanism. The efficacy of LFM is substantiated through a series of
quantitative and qualitative assessments, collectively showcasing a consistent
and significant improvement in CNN's generalization ability and resistance
against adversarial attacks--a phenomenon not observed in current and prior
methodologies. The seamless integration of LFM into established CNN frameworks
underscores its potential to advance both generalization and adversarial
robustness within the deep learning paradigm. Through comprehensive
experiments, including robust person re-identification baseline generalization
experiments and adversarial attack experiments, we demonstrate the substantial
enhancements offered by LFM in addressing the aforementioned challenges. This
contribution represents a noteworthy stride in advancing robust neural network
architectures.
