4 citations · 10 across the 10 of their papers we have counts for
10 papers
Improving the Transferability of Adversarial Examples by Feature Augmentation
Donghua Wang, Wen Yao, Tingsong Jiang +3
Despite the success of input transformation-based attacks on boosting adversarial transferability, the performance is unsatisfying due to the ignorance of the discrepancy across mo…
Universal Multi-view Black-box Attack against Object Detectors via Layout Optimization
Donghua Wang, Wen Yao, Tingsong Jiang +2
Object detectors have demonstrated vulnerability to adversarial examples crafted by small perturbations that can deceive the object detector. Existing adversarial attacks mainly fo…
Universal Perturbation-based Secret Key-Controlled Data Hiding
Donghua Wang, Wen Yao, Tingsong Jiang +1
Deep neural networks (DNNs) are demonstrated to be vulnerable to universal perturbation, a single quasi-perceptible perturbation that can deceive the DNN on most images. However, t…
Adversarial Examples in the Physical World: A Survey
Jiakai Wang, Xianglong Liu, Jin Hu +6
Deep neural networks (DNNs) have demonstrated high vulnerability to adversarial examples, raising broad security concerns about their applications. Besides the attacks in the digit…
RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World
Donghua Wang, Wen Yao, Tingsong Jiang +2
Physical adversarial attacks against deep neural networks (DNNs) have recently gained increasing attention. The current mainstream physical attacks use printed adversarial patches…
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…
A Plug-and-Play Defensive Perturbation for Copyright Protection of DNN-based Applications
Donghua Wang, Wen Yao, Tingsong Jiang +3
Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the requirement of copyright protection of such application's prod…
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 Survey on Physical Adversarial Attack in Computer Vision
Donghua Wang, Wen Yao, Tingsong Jiang +2
Over the past decade, deep learning has revolutionized conventional tasks that rely on hand-craft feature extraction with its strong feature learning capability, leading to substan…
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…