1 citations · 1 across the 13 of their papers we have counts for
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Detecting Adversarial Data via Provable Adversarial Noise Amplification
Furkan Mumcu, Yasin Yilmaz
The nonuniform and growing impact of adversarial noise across the layers of deep neural networks has been used in the literature, without a formal mathematical justification, to de…
Robustness of Agentic AI Systems via Adversarially-Aligned Jacobian Regularization
Furkan Mumcu, Yasin Yilmaz
As Large Language Models (LLMs) transition into autonomous multi-agent ecosystems, robust minimax training becomes essential yet remains prone to instability when highly non-linear…
Agentic AI-Empowered Dynamic Survey Framework
Furkan Mumcu, Lokman Bekit, Michael J. Jones +2
Survey papers play a central role in synthesizing and organizing scientific knowledge, yet they are increasingly strained by the rapid growth of research output. As new work contin…
Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers
Furkan Mumcu, Yasin Yilmaz
Deep Neural Networks (DNNs) are notoriously vulnerable to adversarial input designs with limited noise budgets. While numerous successful attacks with subtle modifications to origi…
Detecting Adversarial Examples
Furkan Mumcu, Yasin Yilmaz
Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial examples. While numerous successful adversarial attacks have been proposed, defenses against these attac…