Query-efficient decision-based black-box patch attack
Deep neural networks (DNNs) have been showed to be highly vulnerable to imperceptible
adversarial perturbations. As a complementary type of adversary, patch attacks that …
adversarial perturbations. As a complementary type of adversary, patch attacks that …
Towards a robust deep neural network against adversarial texts: A survey
Deep neural networks (DNNs) have achieved remarkable success in various tasks (eg,
image classification, speech recognition, and natural language processing (NLP)). However …
image classification, speech recognition, and natural language processing (NLP)). However …
Restricted black-box adversarial attack against deepfake face swapping
DeepFake face swapping presents a significant threat to online security and social media,
which can replace the source face in an arbitrary photo/video with the target face of an …
which can replace the source face in an arbitrary photo/video with the target face of an …
Decision-based adversarial attack with frequency mixup
It has been widely observed that deep neural networks are highly vulnerable to adversarial
examples. Decision-based attacks could generate adversarial examples based solely on top …
examples. Decision-based attacks could generate adversarial examples based solely on top …
A novel robustness-enhancing adversarial defense approach to AI-powered sea state estimation for autonomous marine vessels
Sea state information is significant for the guide of maritime activities of autonomous vessels.
The sea state estimation (SSE) model, powered by artificial intelligence (AI), has shown …
The sea state estimation (SSE) model, powered by artificial intelligence (AI), has shown …
Security issues and defensive approaches in deep learning frameworks
H Chen, Y Zhang, Y Cao, J Xie - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
Deep learning frameworks promote the development of artificial intelligence and
demonstrate considerable potential in numerous applications. However, the security issues …
demonstrate considerable potential in numerous applications. However, the security issues …
Towards a robust deep neural network in texts: A survey
Deep neural networks (DNNs) have achieved remarkable success in various tasks (eg,
image classification, speech recognition, and natural language processing (NLP)). However …
image classification, speech recognition, and natural language processing (NLP)). However …
Transferable black-box attack against face recognition with spatial mutable adversarial patch
Deep Neural Networks (DNNs) are vulnerable to adversarial patch attacks, which raises
security concerns for face recognition systems using DNNs. Previous attack methods focus …
security concerns for face recognition systems using DNNs. Previous attack methods focus …
Revisiting gradient regularization: Inject robust saliency-aware weight bias for adversarial defense
Despite regularizing the Jacobians of neural networks to enhance model robustness has
directly theoretical correlation with model prediction stability, a large defense performance …
directly theoretical correlation with model prediction stability, a large defense performance …
Adversarial exposure attack on diabetic retinopathy imagery grading
Diabetic Retinopathy (DR) is a leading cause of vision loss around the world. To help
diagnose it, numerous cutting-edge works have built powerful deep neural networks (DNNs) …
diagnose it, numerous cutting-edge works have built powerful deep neural networks (DNNs) …