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Found 8 papers, 5 papers with code
Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling
In order to craft natural-looking adversarial clothes that can evade person detectors at multiple viewing angles, we propose adversarial camouflage textures (AdvCaT) that resemble one kind of the typical textures of daily clothes, camouflage textures.
Amplification trojan network: Attack deep neural networks by amplifying their inherent weakness
Recent works found that deep neural networks (DNNs) can be fooled by adversarial examples, which are crafted by adding adversarial noise on clean inputs.
Language-Driven Anchors for Zero-Shot Adversarial Robustness
Previous researches mainly focus on improving adversarial robustness in the fully supervised setting, leaving the challenging domain of zero-shot adversarial robustness an open question.
On the Privacy Effect of Data Enhancement via the Lens of Memorization
We demonstrate that the generalization gap and privacy leakage are less correlated than those of the previous results.
Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World
We simulated the process from cloth to clothing in the digital world and then designed the adversarial "QR code" pattern.
An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks
Compared with rate-based artificial neural networks, Spiking Neural Networks (SNN) provide a more biological plausible model for the brain.
Adversarial Texture for Fooling Person Detectors in the Physical World
Experiments showed that these clothes could fool person detectors in the physical world.
Infrared Invisible Clothing: Hiding From Infrared Detectors at Multiple Angles in Real World
We simulated the process from cloth to clothing in the digital world and then designed the adversarial "QR code" pattern.
