1 Introduction  
2 Background  
2.1 Historical Overview of Image Data Augmentation  
2.2 Traditional Image Augmentation Techniques  
2.3 Development of Automated Data Augmentation (AutoDA)  
3 Automated Data Augmentation Techniques  
3.1 Key Components  
3.2 Overall Workflow  
3.3 Two Stages of AutoDA  
3.4 Datasets  
4 Taxonomy of Image AutoDA Methods  
4.1 Two-stage Methods  
4.2 One-stage Methods  
5 Two-stage Approaches  
5.1 Gradient-free Optimization  
5.2 Gradient-based Optimization  
5.3 Search-free  
6 One-stage Approaches  
6.1 Gradient-based Optimization  
7 Discussion  
7.1 Search Space Formulation  
7.2 Optimal Augmentation Transformations  
7.3 Unsupervised Evaluation  
7.4 Biased or Noisy Data  
7.5 Application Domains  
8 Conclusion