Summary of Unicad: a Unified Approach For Attack Detection, Noise Reduction and Novel Class Identification, by Alvaro Lopez Pellicer et al.
UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification
by Alvaro Lopez Pellicer, Kittipos Giatgong, Yi Li, Neeraj Suri, Plamen Angelov
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed UNICAD framework addresses the limitations of Deep Neural Networks (DNNs) by providing an adaptive solution for detecting and recovering from various adversarial attacks. Unlike existing solutions which focus on specific attack scenarios or classification accuracy, UNICAD integrates multiple techniques to tackle a wide range of attacks while maintaining classification performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UNICAD is designed to help real-world systems detect and recover from unexpected situations. It uses different methods to protect against many types of attacks without sacrificing how well it can classify things. |
Keywords
* Artificial intelligence * Classification
