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Found 4 papers, 0 papers with code
Fool the Hydra: Adversarial Attacks against Multi-view Object Detection Systems
Adversarial patches exemplify the tangible manifestation of the threat posed by adversarial attacks on Machine Learning (ML) models in real-world scenarios.
Attention Deficit is Ordered! Fooling Deformable Vision Transformers with Collaborative Adversarial Patches
The latest generation of transformer-based vision models has proven to be superior to Convolutional Neural Network (CNN)-based models across several vision tasks, largely attributed to their remarkable prowess in relation modeling.
Jedi: Entropy-based Localization and Removal of Adversarial Patches
Jedi tackles the patch localization problem from an information theory perspective; leverages two new ideas: (1) it improves the identification of potential patch regions using entropy analysis: we show that the entropy of adversarial patches is high, even in naturalistic patches; and (2) it improves the localization of adversarial patches, using an autoencoder that is able to complete patch regions from high entropy kernels.
Adversarial Attacks in a Multi-view Setting: An Empirical Study of the Adversarial Patches Inter-view Transferability
In this paper, we study the effect of view angle on the effectiveness of an adversarial patch.
