„A Comparison of Various Optimization Strategies for Generating Adversarial Patches,“ Bachelorarbeit oder Masterarbeit, D. Eisermann (Betreuung), F. Kargl (Prüfer), Inst. of Distr. Sys., Ulm Univ., 2024 –
Verfügbar.
This thesis will explore the effectiveness of different optimization strategies in the generation of adversarial patches. Adversarial patches are small, intentionally designed perturbations that can cause machine learning models, particularly in computer vision, to misclassify inputs. The primary objective of this research is to compare various optimization techniques, such as gradient-based methods, evolutionary algorithms, and reinforcement learning, to determine which methods are most effective and efficient in creating these patches. The outcome of this research could significantly enhance our understanding of model vulnerabilities and contribute to the development of more robust machine learning systems.