Summary of Mgmd-gan: Generalization Improvement Of Generative Adversarial Networks with Multiple Generator Multiple Discriminator Framework Against Membership Inference Attacks, by Nirob Arefin
MGMD-GAN: Generalization Improvement of Generative Adversarial Networks with Multiple Generator Multiple Discriminator Framework Against Membership Inference Attacks
by Nirob Arefin
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A new Generative Adversarial Network (GAN) framework, called Multiple Generators and Multiple Discriminators (MGMD-GAN), is proposed to address the vulnerability of original GANs to Membership Inference Attacks. The MGMD-GAN model learns the mixture distribution of all training data partitions, reducing the generalization gap and making it less susceptible to attacks. The proposed framework consists of multiple generators and discriminators, which are trained on disjoint partitions of the training data. This approach is evaluated through experimental analysis and compared with other GAN frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new kind of computer model called MGMD-GAN is designed to make it harder for bad guys to figure out if a piece of data came from a certain group or not. This type of attack is called Membership Inference Attack. The old way of making this kind of model was too easy for attackers to guess where the data came from, so scientists came up with a new idea: instead of just one big model, they use many small models that work together. Each of these small models looks at a different part of the training data. This makes it harder for attackers to figure out which group the data belongs to. The scientists tested this new model and compared it to other ways of making GANs. |
Keywords
» Artificial intelligence » Gan » Generalization » Generative adversarial network » Inference
