Research map / Adversarial robustness and security / Attacks on text and graphs
Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use affinity-targeted adversarial light signals to cause targeted misclassification | ImageNet source objects under sampled physical transformations and signal offsets · ImageNet, five source classes and seven affinity targets per source | attack success rate: 10%–99% | Mixed |
Closest papers by meaning
- Shadows Can Be Dangerous: Stealthy and Effective Physical-World Adversarial Attack by Natural Phenomenon (CVPR 2022 · similarity 0.91)
- Adversarial camera stickers: A physical camera-based attack on deep learning systems (ICML 2019 · similarity 0.90)
- ProjAttacker: A Configurable Physical Adversarial Attack for Face Recognition via Projector (CVPR 2025 · similarity 0.87)
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- UV-Attack: Physical-World Adversarial Attacks on Person Detection via Dynamic-NeRF-based UV Mapping (ICLR 2025 · similarity 0.86)
- Revisiting Adversarial Patches for Designing Camera-Agnostic Attacks against Person Detection (NeurIPS 2024 · similarity 0.86)
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