Summary of Medbn: Robust Test-time Adaptation Against Malicious Test Samples, by Hyejin Park et al.
MedBN: Robust Test-Time Adaptation against Malicious Test Samples
by Hyejin Park, Jeongyeon Hwang, Sunung Mun, Sangdon Park, Jungseul Ok
First submitted to arxiv on: 28 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Computer Vision and Pattern Recognition (cs.CV)
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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 median batch normalization (MedBN) method aims to address the vulnerability of test-time adaptation (TTA) models against malicious examples. Existing TTA methods excel at adapting to test data variations but are exposed to security threats when a small proportion of the test batch is manipulated. MedBN leverages the robustness of the median for statistics estimation within the batch normalization layer during test-time inference, making it algorithm-agnostic and suitable for integration with existing TTA frameworks. The method demonstrates consistent performance in maintaining robustness across different attack scenarios on benchmark datasets such as CIFAR10-C, CIFAR100-C, and ImageNet-C. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MedBN is a new way to make machine learning models more secure. Right now, these models are good at getting better when they see new data, but that makes them vulnerable to bad data that someone might try to trick them with. MedBN uses a special type of math called the median to keep the model safe from these attacks. This method is useful because it can be used with many different types of machine learning models and works well on lots of different kinds of data. |
Keywords
* Artificial intelligence * Batch normalization * Inference * Machine learning
