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Found 9 papers, 2 papers with code
A Survey on Deep Learning for Theorem Proving
Theorem proving is a fundamental aspect of mathematics, spanning from informal reasoning in mathematical language to rigorous derivations in formal systems.
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).
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.
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.
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 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.
Heat Source Layout Optimization Using Automatic Deep Learning Surrogate and Multimodal Neighborhood Search Algorithm
In satellite layout design, heat source layout optimization (HSLO) is an effective technique to decrease the maximum temperature and improve the heat management of the whole system.
$A^{3}D$: 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.
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.
