24 citations · 81 across the 24 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
How the Softmax Output is Misleading for Evaluating the Strength of Adversarial Examples
Utku Ozbulak, Wesley De Neve, Arnout Van Messem
Even before deep learning architectures became the de facto models for complex computer vision tasks, the softmax function was, given its elegant properties, already used to analyz…