Structure invariant transformation for better adversarial transferability
Given the severe vulnerability of Deep Neural Networks (DNNs) against adversarial
examples, there is an urgent need for an effective adversarial attack to identify the …
examples, there is an urgent need for an effective adversarial attack to identify the …
Biasasker: Measuring the bias in conversational ai system
Powered by advanced Artificial Intelligence (AI) techniques, conversational AI systems, such
as ChatGPT, and digital assistants like Siri, have been widely deployed in daily life …
as ChatGPT, and digital assistants like Siri, have been widely deployed in daily life …
Boosting transferability in vision-language attacks via diversification along the intersection region of adversarial trajectory
Vision-language pre-training (VLP) models exhibit remarkable capabilities in
comprehending both images and text, yet they remain susceptible to multimodal adversarial …
comprehending both images and text, yet they remain susceptible to multimodal adversarial …
Boosting adversarial transferability by block shuffle and rotation
K Wang, X He, W Wang… - Proceedings of the IEEE …, 2024 - openaccess.thecvf.com
Adversarial examples mislead deep neural networks with imperceptible perturbations and
have brought significant threats to deep learning. An important aspect is their transferability …
have brought significant threats to deep learning. An important aspect is their transferability …
Boosting adversarial transferability by achieving flat local maxima
Transfer-based attack adopts the adversarial examples generated on the surrogate model to
attack various models, making it applicable in the physical world and attracting increasing …
attack various models, making it applicable in the physical world and attracting increasing …
Rethinking the backward propagation for adversarial transferability
Transfer-based attacks generate adversarial examples on the surrogate model, which can
mislead other black-box models without access, making it promising to attack real-world …
mislead other black-box models without access, making it promising to attack real-world …
Improving the transferability of adversarial examples with arbitrary style transfer
Deep neural networks are vulnerable to adversarial examples crafted by applying human-
imperceptible perturbations on clean inputs. Although many attack methods can achieve …
imperceptible perturbations on clean inputs. Although many attack methods can achieve …
Typography leads semantic diversifying: Amplifying adversarial transferability across multimodal large language models
Recently, Multimodal Large Language Models (MLLMs) achieve remarkable performance in
numerous zero-shot tasks due to their outstanding cross-modal interaction and …
numerous zero-shot tasks due to their outstanding cross-modal interaction and …
Resilience and security of deep neural networks against intentional and unintentional perturbations: Survey and research challenges
In order to deploy deep neural networks (DNNs) in high-stakes scenarios, it is imperative
that DNNs provide inference robust to external perturbations-both intentional and …
that DNNs provide inference robust to external perturbations-both intentional and …
Enhancing transferability of adversarial examples through mixed-frequency inputs
Recent studies have shown that Deep Neural Networks (DNNs) are easily deceived by
adversarial examples, revealing their serious vulnerability. Due to the transferability …
adversarial examples, revealing their serious vulnerability. Due to the transferability …