Update README.md

Author: ffhibnese

README.md

@@ -54,7 +54,7 @@ The reason why we developed this toolbox is that the research line of **MI** suf
 |[SecretGen](./src/modelinversion/attack/SecretGen/)|SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination|[ECCV'2022](https://link.springer.com/chapter/10.1007/978-3-031-20065-6_9#Abs1)|whitebox, blackbox|instance-level, data augmentation|
 |[BREPMI](./src/modelinversion/attack/BREPMI/)|Label-Only Model Inversion Attacks via Boundary Repulsion|[CVPR'2022](https://openaccess.thecvf.com/content/CVPR2022/html/Kahla_Label-Only_Model_Inversion_Attacks_via_Boundary_Repulsion_CVPR_2022_paper.html)|blackbox|boundary repelling, label-only|
 |[Mirror](./src/modelinversion/attack/Mirror/)|MIRROR: Model Inversion for Deep Learning Network with High Fidelity|[NDSS'2022](https://www.ndss-symposium.org/ndss-paper/auto-draft-203/)|whitebox, blackbox|both gradient-free and gradient-based, genetic algorithm|
-|[PPA](./src/modelinversion/attack/PPA/)|Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks|[ICML'2022](https://arxiv.org/pdf/2201.12179.pdf)|whitebox||
+|[PPA](./src/modelinversion/attack/PPA/)|Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks|[ICML'2022](https://arxiv.org/pdf/2201.12179.pdf)|whitebox|Initial selection, pre-trained GANs, results selection|
 |[PLGMI](./src/modelinversion/attack/PLGMI/)|Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network|[AAAI'2023](https://ojs.aaai.org/index.php/AAAI/article/view/25442)|whitebox|pseudo-labels, data augmentation, special loss function|
 |[C2FMI](./src/modelinversion/attack/C2FMI/)|C2FMI: Corse-to-Fine Black-box Model Inversion Attack|[TDSC'2023](https://ieeexplore.ieee.org/abstract/document/10148574)|whitebox, blackbox|gradient-free, two-stage|
 |[Lomma](./src/modelinversion/attack/Lomma/)|Re-Thinking Model Inversion Attacks Against Deep Neural Networks|[CVPR'2023](https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Re-Thinking_Model_Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2023_paper.html)|blackbox|special loss, model augmentation|