Convolutional Neural Networks (CNNs) are well-known for their vulnerability
to adversarial attacks, posing significant security concerns. In response to
these threats, various defense methods have emerged to bolster the model's
robustness. However, most existing methods either focus on learning from
adversarial perturbations, leading to overfitting to the adversarial examples,
or aim to eliminate such perturbations during inference, inevitably increasing
computational burdens. Conversely, clean training, which strengthens the
model's robustness by relying solely on clean examples, can address the
aforementioned issues. In this paper, we align with this methodological stream
and enhance its generalizability to unknown adversarial examples. This
enhancement is achieved by scrutinizing the behavior of latent features within
the network. Recognizing that a correct prediction relies on the correctness of
the latent feature's pattern, we introduce a novel and effective Feature
Pattern Consistency Constraint (FPCC) method to reinforce the latent feature's
capacity to maintain the correct feature pattern. Specifically, we propose
Spatial-wise Feature Modification and Channel-wise Feature Selection to enhance
latent features. Subsequently, we employ the Pattern Consistency Loss to
constrain the similarity between the feature pattern of the latent features and
the correct feature pattern. Our experiments demonstrate that the FPCC method
empowers latent features to uphold correct feature patterns even in the face of
adversarial examples, resulting in inherent adversarial robustness surpassing
state-of-the-art models.

通过分析网络中的潜在特征行为，我们引入一种新颖而有效的特征模式一致性约束（FPCC）方法，以增强潜在特征维持正确特征模式的能力，从而使模型在面对对抗性示例时具有内在的对抗鲁棒性，超过最先进的模型。