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How online studies must increase their defences against AI. [PDF]

Commun Psychol
Anders G, Buder J, Papenmeier F, Huff M.
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Learning Universal Adversarial Perturbation by Adversarial Example

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Deep learning models have shown to be susceptible to universal adversarial perturbation (UAP), which has aroused wide concerns in the community. Compared with the conventional adversarial attacks that generate adversarial samples at the instance level, UAP can fool the target model for different instances with only a single perturbation, enabling us to
Maosen Li, Yanhua Yang, Kun Wei, Xu Yang 0019, Heng Huang   +4 more
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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
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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 ...
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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
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Advops: Decoupling Adversarial Examples

Pattern Recognition, 2023
Donghua Wang, Wen Yao, Tingsong Jiang, Xiaoqian Chen   +3 more
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Unauthorized AI cannot recognize me: Reversible adversarial example

Pattern Recognition, 2023
Weiming Zhang, Kazuto Fukuchi, Youhei Akimoto   +2 more
exaly  

Interpreting Universal Adversarial Example Attacks on Image Classification Models

IEEE Transactions on Dependable and Secure Computing, 2023
Yi Ding, Fuyuan Tan, Ji Geng
exaly