A Comparative Study of Adversarial Robustness in CNN and CNN-ANFIS Architectures

A team of artificial intelligence researchers published a comparative study on the arXiv preprint server in February 2025, evaluating the adversarial robustness of standard Convolutional Neural Networks (CNNs) against hybrid CNN-ANFIS architectures to address critical vulnerabilities in modern image classification systems. The investigation focuses on whether integrating Adaptive Neuro-Fuzzy Inference Systems (ANFIS) into traditional models can mitigate the common trade-off between high-performance accuracy and the susceptibility to malicious input perturbations. By replacing traditional fully connected layers with neuro-fuzzy logic, the researchers aimed to determine if these hybrid models could provide a more stable and interpretable alternative for high-stakes computer vision applications. The research specifically benchmarks well-known architectures, including ConvNet, VGG, and ResNet18, against their ANFIS-augmented counterparts, often referred to as Deep Convolutional Neuro-Fuzzy Inference Systems (DCNFIS). While CNNs have long been the industry standard for tasks such as object recognition and medical imaging, they are frequently criticized for their 'black box' nature and their tendency to fail when faced with adversarial attacks—minor, often invisible changes to data that can lead a model to misclassify an image. The study addresses a significant gap in current literature, as the defensive capabilities of neuro-fuzzy hybrids have remained largely underexplored compared to their interpretability benefits. Preliminary findings from the study suggest that the structural differences in how CNNs and CNN-ANFIS models process and classify information result in varying levels of resilience. While neuro-fuzzy layers are traditionally valued for providing human-readable logic through fuzzy rules, this new analysis provides a technical deep dive into how these rules handle the noise and intentional distortions characteristic of adversarial environments. This comparative approach is essential for developers seeking to deploy secure AI in environments where security and transparency are equally as important as raw predictive power.