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Luu, T.J.

Assessing and Improving DNN Robustness Against Adversarial Examples From the Perspective of Fully Connected Layers

inforesearchPeer-Reviewed

researchsecurity

Source: IEEE Xplore (Security & AI Journals)March 25, 2026

Summary

Deep neural networks (machine learning models with many layers that process information) are vulnerable to adversarial examples, which are inputs slightly modified to fool the AI into making wrong predictions. This paper proposes adding a redundant fully connected layer (a type of neural network component that connects all inputs to all outputs) with a special loss function to make these networks more robust against attacks while maintaining accuracy on normal inputs.

Solution / Mitigation

The source describes a defense mechanism but does not present it as a deployed fix or patch. It is a research proposal for a novel component (redundant fully connected layer with a cosine similarity-based loss function) that can be added to existing models. N/A -- no mitigation discussed in source.