Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Autonomous Vehicles
Poisoning attacks against public datasets lead to major concerns, such as (i) misclassification of perceived objects when the poisoned data is used for training and (ii) embedding of backdoors that may eventually be triggered later on, when specific conditions in the system apply over the learned models. Its impact over data augmentation models is unclear. While data augmentation reduces the likelihood of poisoning attack success, some valid questions remain. Is data augmentation affecting the impact of poisoning attacks? can it increase the number of poisoned samples or injected backdoors? We
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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- LinkedLinked via arxiv author · 85%Marwan Lazrag →
“Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Au”
- LinkedLinked via arxiv author · 85%Badis Hammi →
“Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Au”
- LinkedLinked via arxiv author · 85%Lorena Gonzalez-Manzano →
“Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Au”
- LinkedLinked via arxiv author · 85%Joaquin Garcia-Alfaro →
“Assessing the Operational Impact of Poisoning Attacks over Augmented 3D Point Cloud Public Datasets for Connected and Au”
- FuzzySimilar title/name (fuzzy) · 84%huggingface/datasets →
“Fuzzy title match (0.92): “Assessing the Operational Impact of Poisoning Attacks over A” ≈ “huggingface/datasets””