1 citations · 1 across the 13 of their papers we have counts for
13 papers
Submission Responsibility Matters: Role-Aware Submission Quotas under Coauthorship
Furkan Mumcu, Yasin Yilmaz
Author-level submission quotas are increasingly used to control growing peer-review load. Recent coauthorship-sensitive quota rules improve over fixed per-author limits by reducing…
Is Video Anomaly Detection Misframed? Evidence from LLM-Based and Multi-Scene Models
Furkan Mumcu, Michael J. Jones, Anoop Cherian +1
Recent video anomaly detection research has expanded rapidly with an emphasis on general models of normality intended to work across many different scenes. While this focus has led…
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…
Socially-Weighted Alignment: A Game-Theoretic Framework for Multi-Agent LLM Systems
Furkan Mumcu, Yasin Yilmaz
Deploying large language model (LLM) agents in shared environments introduces a fundamental tension between individual alignment and collective stability: locally rational decision…
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…
Leveraging Multimodal LLM Descriptions of Activity for Explainable Semi-Supervised Video Anomaly Detection
Furkan Mumcu, Michael J. Jones, Anoop Cherian +1
Existing semi-supervised video anomaly detection (VAD) methods often struggle with detecting complex anomalies involving object interactions and generally lack explainability. To o…
MLLM-Guided Autonomous Object Detection for Open-World Understanding
Furkan Mumcu, Michael J. Jones, Anoop Cherian +1
Object detection typically relies on predefined category vocabularies, while open-world and open-vocabulary approaches remain limited by generic unknown labels or externally specif…
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…
ComplexVAD: Detecting Interaction Anomalies in Video
Furkan Mumcu, Michael J. Jones, Yasin Yilmaz +1
Existing video anomaly detection datasets are inadequate for representing complex anomalies that occur due to the interactions between objects. The absence of complex anomalies in…
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…
Multimodal Attack Detection for Action Recognition Models
Furkan Mumcu, Yasin Yilmaz
Adversarial machine learning attacks on video action recognition models is a growing research area and many effective attacks were introduced in recent years. These attacks show th…
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems
Furkan Mumcu, Keval Doshi, Yasin Yilmaz
Anomaly detection in videos is an important computer vision problem with various applications including automated video surveillance. Although adversarial attacks on image understa…