Results 301 to 310 of about 5,380,268 (331)

Towards Transferable Targeted Adversarial Examples

Computer Vision and Pattern Recognition, 2023
Transferability of adversarial examples is critical for black-box deep learning model attacks. While most existing studies focus on enhancing the transferability of untargeted adversarial attacks, few of them studied how to generate transferable targeted
Zhibo Wang, Hongshan Yang, Yunhe Feng, Hengchang Guo, Zhifei Zhang, Kui Ren   +5 more
semanticscholar   +1 more source

A Survey on Transferability of Adversarial Examples across Deep Neural Networks

Trans. Mach. Learn. Res., 2023
The emergence of Deep Neural Networks (DNNs) has revolutionized various domains by enabling the resolution of complex tasks spanning image recognition, natural language processing, and scientific problem-solving.
Jindong Gu, Xiaojun Jia, Pau de Jorge, Wenqain Yu, Xinwei Liu, A. Ma, Yuan Xun, Anjun Hu, Ashkan Khakzar, Zhijiang Li, Xiaochun Cao, Philip H. S. Torr   +11 more
semanticscholar   +1 more source

MagNet: A Two-Pronged Defense against Adversarial Examples

Conference on Computer and Communications Security, 2017
Deep learning has shown impressive performance on hard perceptual problems. However, researchers found deep learning systems to be vulnerable to small, specially crafted perturbations that are imperceptible to humans.
Dongyu Meng, Hao Chen
semanticscholar   +1 more source

On The Generation of Unrestricted Adversarial Examples

2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 2020
Adversarial examples are inputs designed by an adversary with the goal of fooling the machine learning models. Most of the research about adversarial examples have focused on perturbing the natural inputs with the assumption that the true label remains unchanged.
Mehrgan Khoshpasand, Ali A. Ghorbani 0001   +1 more
openaire   +1 more source

Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models via Diffusion Models

IEEE Transactions on Information Forensics and Security
Adversarial attacks, particularly targeted transfer-based attacks, can be used to assess the adversarial robustness of large visual-language models (VLMs), allowing for a more thorough examination of potential security flaws before deployment.
Qing Guo, Shanmin Pang, Xiaojun Jia, Yang Liu, Qing Guo   +4 more
semanticscholar   +1 more source

Enhancing Transferability of Adversarial Examples Through Mixed-Frequency Inputs

IEEE Transactions on Information Forensics and Security
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 can attack across multiple models with different architectures,
Yaguan Qian, Ke Chen, Bin Wang, Zhaoquan Gu, Shoulin Ji, Wei Wang, Yanchun Zhang   +6 more
semanticscholar   +1 more source

On the Salience of Adversarial Examples

2019
Adversarial examples are beginning to evolve as rapidly as the deep learning models they are designed to attack. These intentionally-manipulated inputs attempt to mislead the targeted model while maintaining the appearance of innocuous input data. Countermeasures against these attacks that take a global approach tend to be lossy to the original data ...
openaire   +1 more source

Adversarial Examples for Malware Detection

2017
Machine learning models are known to lack robustness against inputs crafted by an adversary. Such adversarial examples can, for instance, be derived from regular inputs by introducing minor—yet carefully selected—perturbations.
Kathrin Grosse, Nicolas Papernot, Praveen Manoharan, Michael Backes, Patrick McDaniel   +4 more
openaire   +1 more source

Advops: Decoupling Adversarial Examples

Pattern Recognition, 2023
Donghua Wang 0001, Wen Yao 0001, Tingsong Jiang, Xiaoqian Chen   +3 more
openaire   +1 more source

Learning defense transformations for counterattacking adversarial examples

Neural Networks, 2023
Jiezhang Cao, Mingkui Tan
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