ARMOR: Manifold-Oriented Training for Adversarially Robust Aerial Object Detection under Data Scarcity

arXiv:2608.29510 · cs.CV, cs.CR, cs.LG · Submitted 2026-08-30 · Read on arXiv

cs.CV, cs.CR, cs.LG

Submitted: 2026-08-30

Updated: 2026-09-05

Comments: 10 pages, 5 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Robustness — 56× in this paper · explained in 5 episodes
• Fine-Tuning — 27× in this paper · explained in 5 episodes
• Adversarial Robustness — 22× in this paper · explained in 3 episodes
• Adversarial Training — 19× in this paper · explained in 1 episode
• RANDOM PATCHES — 12× in this paper
• Generalization — 9× in this paper · explained in 13 episodes
• Generative Model — 7× in this paper · explained in 2 episodes
• Adaptation — 6× in this paper · explained in 1 episode
• Adversarial Attacks — 6× in this paper · explained in 1 episode
• Adversarial Examples — 6× in this paper · explained in 2 episodes
• Data Augmentation — 6× in this paper · explained in 7 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• RANDOMIZED SMOOTHING — 5× in this paper
• Adversarial Noise — 4× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Computer Vision and Pattern Recognition — 3× in this paper
• Distribution Shift — 3× in this paper · explained in 5 episodes
• Generalizability — 3× in this paper · explained in 6 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Accuracy — 2× in this paper · explained in 1 episode
• Generative Modeling — 2× in this paper · explained in 3 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Transfer Learning — 2× in this paper · explained in 10 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Defense — 1× in this paper · explained in 1 episode
• Algorithmic Stability — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• You Only Look Once — 1× in this paper
• Zero-Shot Transfer — 1× in this paper · explained in 3 episodes

Sources

• Non-Robust Features are Not Always Useful in One-Class Classification

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