1.Adversarial Texture for Fooling Person Detectors in the Physical World | CVPR2022 |
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2. APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection | ECCV2020 |
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3. Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World | CVPR2021 |
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4. Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles | CVPR 2020 |
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5. Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink | CVPR2021 |
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6. ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector paper
7. Universal Physical Camouflage Attacks on Object Detectors | CVPR2022 |
[paper](Universal Physical Camouflage Attacks on Object Detectors)
8. Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon | CVPR2022 |
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9. Adversarial T-shirt | ECCV2020 |
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10. Adversarial Texture for Fooling Person Detectors in the Physical World | CVPR2022 |
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11.DTA: Physical Camouflage Attacks using Differentiable Transformation Network | CVPR2022 |
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12.Meta-Attack: Class-agnostic and Model-agnostic Physical Adversarial Attack | ICCV2021 |
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13.DPatch: An Adversarial Patch Attack on Object Detectors
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14.Naturalistic Physical Adversarial Patch for Object Detectors | ICCV2021 |
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15.Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks | WACV2022 |
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16.Perceptual-Sensitive GAN for Generating Adversarial Patches | AAAI2019 |
1.Fooling automated surveillance cameras:adversarial patches to attack person detection | CVPR2019 |
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2. Fooling detection alone is not enough: Adversarial attack against multiple object tracking | ICLR2020 |
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3.Parallel rectangle flip attack: A query-based black-box attack against object detection | ICCV2021 |
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1.Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection | CVPR2022 |
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2.Towards adversarially robust object detection | ICCV2019 |
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3.A Self-supervised Approach for Adversarial Robustness | CVPR2020 |
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4.PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking |USENIX Security 2022|
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5.Certified Defenses for Adversarial Patches
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6.ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers | Neurips2021 |
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7.PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier
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8.Role of Spatial Context in Adversarial Robustness for Object Detection | CVPR2020 |
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