对比卷积网络与模糊神经混合模型的抗干扰能力
A Comparative Study of Adversarial Robustness in CNN and CNN-ANFIS Architectures
- 用ANFIS替换全连接层，提升模型可解释性
- ResNet18加ANFIS后抗攻击能力增强，但VGG反而变弱
- 适合关注模型安全性和可解释性的研究者参考
卷积神经网络（CNN）在图像分类中表现优异，但缺乏可解释性且易受对抗攻击。神经模糊混合模型如DCNFIS将CNN分类器替换为自适应神经模糊推理系统（ANFIS），以提高可解释性，但其鲁棒性尚未充分研究。本文在MNIST、Fashion-MNIST、CIFAR-10和CIFAR-100数据集上，对比标准CNN（ConvNet、VGG、ResNet18）与其对应的ANFIS增强版本，在基于梯度的（PGD）和无梯度的（Square）攻击下的表现。结果表明，引入ANFIS并未一致提升干净准确率，且对鲁棒性的影响具有架构依赖性：ResNet18-ANFIS表现出更好的对抗鲁棒性，而VGG-ANFIS通常劣于其基线模型。这些发现说明，神经模糊增强可在特定架构中提升鲁棒性，但并非普遍适用。
原文摘要 · Abstract (English)
Convolutional Neural Networks (CNNs) achieve strong image classification performance but lack interpretability and are vulnerable to adversarial attacks. Neuro-fuzzy hybrids such as DCNFIS replace fully connected CNN classifiers with Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to improve interpretability, yet their robustness remains underexplored. This work compares standard CNNs (ConvNet, VGG, ResNet18) with their ANFIS-augmented counterparts on MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 under gradient-based (PGD) and gradient-free (Square) attacks. Results show that ANFIS integration does not consistently improve clean accuracy and has architecture-dependent effects on robustness: ResNet18-ANFIS exhibits improved adversarial robustness, while VGG-ANFIS often underperforms its baseline. These findings suggest that neuro-fuzzy augmentation can enhance robustness in specific architectures but is not universally beneficial.
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