Search Results for author:
Found 22 papers, 4 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.
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations
To bridge the gap between AutoML and semantic adversarial attacks, we propose a novel method called multi-objective evolutionary search of variable-length composite semantic perturbations (MES-VCSP).
Financial sentiment analysis using FinBERT with application in predicting stock movement
We apply sentiment analysis in financial context using FinBERT, and build a deep neural network model based on LSTM to predict the movement of financial market movement.
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation
Finally, we propose a multi-fidelity online surrogate during optimization to further decrease the search cost.
Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World
Infrared imaging systems have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern.
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.
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design
Therefore, we propose a multi-objective memetic algorithm for auto adversarial attack optimization design, which realizes the automatical search for the near-optimal adversarial attack towards defensed models.
$A^{3}D$: A Platform of Searching for Robust Neural Architectures and Efficient Adversarial Attacks
To alleviate these problems, in this paper, we first propose a novel platform called auto adversarial attack and defense ($A^{3}D$), which can help search for robust neural network architectures and efficient adversarial attacks.
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise.
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.
RBUE: A ReLU-Based Uncertainty Estimation Method of Deep Neural Networks
Deep Ensemble is widely considered the state-of-the-art method which can estimate the uncertainty with higher quality, but it is very expensive to train and test.
Towards Time-Aware Knowledge Graph Completion
In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts.
