24 citations · 37 across the 4 of their papers we have counts for
7 papers
Exact Feature Collisions in Neural Networks
Utku Ozbulak, Manvel Gasparyan, Shodhan Rao +2
Predictions made by deep neural networks were shown to be highly sensitive to small changes made in the input space where such maliciously crafted data points containing small pert…
Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems
Utku Ozbulak, Baptist Vandersmissen, Azarakhsh Jalalvand +3
Given their substantial success in addressing a wide range of computer vision challenges, Convolutional Neural Networks (CNNs) are increasingly being used in smart home application…
Regional Image Perturbation Reduces Norms of Adversarial Examples While Maintaining Model-to-model Transferability
Utku Ozbulak, Jonathan Peck, Wesley De Neve +3
Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks. In thi…
Perturbation Analysis of Gradient-based Adversarial Attacks
Utku Ozbulak, Manvel Gasparyan, Wesley De Neve +1
After the discovery of adversarial examples and their adverse effects on deep learning models, many studies focused on finding more diverse methods to generate these carefully craf…
Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
Utku Ozbulak, Arnout Van Messem, Wesley De Neve
Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples…
Not All Adversarial Examples Require a Complex Defense: Identifying Over-optimized Adversarial Examples with IQR-based Logit Thresholding
Utku Ozbulak, Arnout Van Messem, Wesley De Neve
Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning. Adversarial attacks, which produce adversarial examples, increase th…