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Found 8 papers, 3 papers with code
Universal Perturbation-based Secret Key-Controlled Data Hiding
In this paper, we propose a novel universal perturbation-based secret key-controlled data-hiding method, realizing data hiding with a single universal perturbation and data decoding with the secret key-controlled decoder.
Adversarial Examples in the Physical World: A Survey
However, current research on physical adversarial examples (PAEs) lacks a comprehensive understanding of their unique characteristics, leading to limited significance and understanding.
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World
In this paper, we propose a novel Reflected Light Attack (RFLA), featuring effective and stealthy in both the digital and physical world, which is implemented by placing the color transparent plastic sheet and a paper cut of a specific shape in front of the mirror to create different colored geometries on the target object.
Impact of Light and Shadow on Robustness of Deep Neural Networks
Deep neural networks (DNNs) have made remarkable strides in various computer vision tasks, including image classification, segmentation, and object detection.
A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications
Then, we extract the copyright information from the encoded copyrighted image with the devised copyright decoder.
Differential Evolution based Dual Adversarial Camouflage: Fooling Human Eyes and Object Detectors
In the first stage, we optimize the global texture to minimize the discrepancy between the rendered object and the scene images, making human eyes difficult to distinguish.
A Survey on Physical Adversarial Attack in Computer Vision
Then, we discuss the existing physical attacks and focus on the technique for improving the robustness of physical attacks under complex physical environmental conditions.
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
To bridge the gap between digital attacks and physical attacks, we exploit the full 3D vehicle surface to propose a robust Full-coverage Camouflage Attack (FCA) to fool detectors.
