3 citations · 7 across the 9 of their papers we have counts for
6 papers · 1 filter
Multi-objective Evolutionary Search of Variable-length Composite Semantic Perturbations
Jialiang Sun, Wen Yao, Tingsong Jiang +1
Deep neural networks have proven to be vulnerable to adversarial attacks in the form of adding specific perturbations on images to make wrong outputs. Designing stronger adversaria…
Impact of Light and Shadow on Robustness of Deep Neural Networks
Chengyin Hu, Weiwen Shi, Chao Li +4
Deep neural networks (DNNs) have made remarkable strides in various computer vision tasks, including image classification, segmentation, and object detection. However, recent resea…
Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation
Jialiang Sun, Wen Yao, Tingsong Jiang +1
Neural architecture search (NAS) has emerged as one successful technique to find robust deep neural network (DNN) architectures. However, most existing robustness evaluations in NA…
Differential Evolution based Dual Adversarial Camouflage: Fooling Human Eyes and Object Detectors
Jialiang Sun, Tingsong Jiang, Wen Yao +2
Recent studies reveal that deep neural network (DNN) based object detectors are vulnerable to adversarial attacks in the form of adding the perturbation to the images, leading to t…
A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design
Jialiang Sun, Wen Yao, Tingsong Jiang +1
The phenomenon of adversarial examples has been revealed in variant scenarios. Recent studies show that well-designed adversarial defense strategies can improve the robustness of d…
FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack
Donghua Wang, Tingsong Jiang, Jialiang Sun +5
Physical adversarial attacks in object detection have attracted increasing attention. However, most previous works focus on hiding the objects from the detector by generating an in…