1. Introduction

2. Background on DGMs
2.1 Notations
2.2 DGMs: GANs and VAEs
2.3 A Comparison of DGMs and DDMs

3. Threat Models for Attacking GANs and VAEs
3.1 Adversary’s Goals
3.2 The Adversary’s Prior Knowledge

4. GANs: Attacks Against Generators

5. GANs: Attacks Against Discriminators

6. VAEs: Attacks Against Decoders and Encoders

7. Datasets: Poisoning Attacks

8. Defense Methods
8.1 Defenses for the Model Parts
8.2 Defenses for Model Outputs
8.3 Defenses for Training Data

9. Outlook and Future Directions
9.1 New Possible Attacks
9.2 Possible Defenses
9.3 Attacks and Defenses for Federated DGMs

10. Conclusion