Hypnopaedia-Aware Machine Unlearning via Psychometrics of Artificial Mental Imagery
Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger...
Saved in:
| Main Authors: | Ching-Chun Chang, Kai Gao, Shuying Xu, Anastasia Kordoni, Christopher Leckie, Isao Echizen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11025476/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
An overview of machine unlearning
by: Chunxiao Li, et al.
Published: (2025-06-01) -
Determining factors of individual and organizational unlearning in the generation and realization of ideas: a multigroup analysis from organizational structure
by: Vanessa Itacaramby Pardim, et al.
Published: (2024-08-01) -
Private Data Protection with Machine Unlearning in Contrastive Learning Networks
by: Kongyang Chen, et al.
Published: (2024-12-01) -
Private Data Protection With Machine Unlearning for Next-Generation Networks
by: Kongyang Chen, et al.
Published: (2025-01-01) -
Scrub-and-Learn: Category-Aware Weight Modification for Machine Unlearning
by: Jiali Wang, et al.
Published: (2025-05-01)