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2102.04150
Cited By

Exploiting epistemic uncertainty of the deep learning models to generate
adversarial samples

v1v2 (latest)

Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples

Multimedia tools and applications (MTA), 2021

8 February 2021

Ömer Faruk Tuna

Ferhat Ozgur Catak

ArXiv (abs)PDF HTML

Papers citing "Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples"

13 / 13 papers shown

Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models

Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models

382

2

0

11 Jun 2025

LightDefense: A Lightweight Uncertainty-Driven Defense against Jailbreaks via Shifted Token Distribution

LightDefense: A Lightweight Uncertainty-Driven Defense against Jailbreaks via Shifted Token Distribution

367

2

0

02 Apr 2025

Relationship between Uncertainty in DNNs and Adversarial Attacks

Relationship between Uncertainty in DNNs and Adversarial Attacks

Abigail Adeniran

Adewale Adeyemo

Adewale Adeyemo

424

0

0

20 Sep 2024

SPUQ: Perturbation-Based Uncertainty Quantification for Large Language
Models

SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models

Lalla Mouatadid

360

36

0

04 Mar 2024

Practical Adversarial Attacks Against AI-Driven Power Allocation in a
Distributed MIMO Network

Practical Adversarial Attacks Against AI-Driven Power Allocation in a Distributed MIMO Network

Ömer Faruk Tuna

Fehmí Emre Kadan

150

6

0

23 Jan 2023

Defensive Distillation based Adversarial Attacks Mitigation Method for
Channel Estimation using Deep Learning Models in Next-Generation Wireless
Networks

Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless NetworksIEEE Access (IEEE Access), 2022

Ferhat Ozgur Catak

174

42

0

12 Aug 2022

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for
multiple uncertainty types and tasks

MUAD: Multiple Uncertainties for Autonomous Driving, a benchmark for multiple uncertainty types and tasksBritish Machine Vision Conference (BMVC), 2022

Rémi Kazmierczak

Séverine Dubuisson

346

29

0

02 Mar 2022

The Adversarial Security Mitigations of mmWave Beamforming Prediction
Models using Defensive Distillation and Adversarial Retraining

The Adversarial Security Mitigations of mmWave Beamforming Prediction Models using Defensive Distillation and Adversarial Retraining

Ferhat Ozgur Catak

291

11

0

16 Feb 2022

Unreasonable Effectiveness of Last Hidden Layer Activations for
Adversarial Robustness

Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial RobustnessAnnual International Computer Software and Applications Conference (COMPSAC), 2022

Ömer Faruk Tuna

Ferhat Ozgur Catak

265

5

0

15 Feb 2022

Attribution of Predictive Uncertainties in Classification Models

Attribution of Predictive Uncertainties in Classification ModelsConference on Uncertainty in Artificial Intelligence (UAI), 2021

Alec E. Barns-Graham

362

8

0

19 Jul 2021

Security Concerns on Machine Learning Solutions for 6G Networks in
mmWave Beam Prediction

Security Concerns on Machine Learning Solutions for 6G Networks in mmWave Beam PredictionPhysical Communication (Phys. Commun.), 2021

Ferhat Ozgur Catak

290

57

0

09 May 2021

Adversarial Machine Learning Security Problems for 6G: mmWave Beam
Prediction Use-Case

Adversarial Machine Learning Security Problems for 6G: mmWave Beam Prediction Use-CaseInternational Black Sea Conference on Communications and Networking (BlackSeaCom), 2021

Ferhat Ozgur Catak

196

28

0

12 Mar 2021

Closeness and Uncertainty Aware Adversarial Examples Detection in
Adversarial Machine Learning

Closeness and Uncertainty Aware Adversarial Examples Detection in Adversarial Machine LearningComputers & electrical engineering (CEE), 2020

Ömer Faruk Tuna

Ferhat Ozgur Catak

356

13

0

11 Dec 2020

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