# PPA-SAM: Plug-and-Play Adversarial Segment Anything Model for 3D Tooth Segmentation
## 2 Related Work  
### Visual Fundamental Models  
Convolutional Neural Networks (CNNs) play a pivotal role in computer vision, demonstrating remarkable success in tasks such as image classification, object detection, and semantic segmentation. Recently, U-shaped segmentation networks, as exemplified by the U-Net architecture proposed by [PERSON] et al. [17], consist primarily of an encoder and a decoder, forming a U-shaped structure. The encoder is responsible for feature extraction, while the decoder maps features back to the segmented result of the original input image. U-Net finds broad applications in fields such as medical image segmentation and satellite image analysis. Subsequently, numerous networks based on U-Net improvements have emerged [18; 19; 20]. For example, UNet++ [19] adopts a nested structure and dense connections to better capture multi-scale and hierarchical features, thereby enhancing the expressiveness of U-Net. It is widely applied in tasks such as medical image segmentation and natural image segmentation. Simultaneously, the Transformer, as a model capable of capturing global information and pixel relationships in images, has garnered significant attention from researchers for its integration into the field of image processing. TransUNet [18], based on the attention mechanism of the Transformer, has the ability to capture global contextual information, thereby contributing to a better understanding of the overall structure of images. [PERSON] et al. [21] introduced the Vision Transformer (ViT), which utilizes a self-attention mechanism to process images. Prior to inputting images into ViT, they are segmented into blocks and then mapped into a sequence of vectors suitable for processing by a Transformer. This approach enables ViT to excel in handling images of different sizes and effectively capturing long-range contextual information. The success of ViT underscores the significance of self-attention mechanisms in the field of computer vision. However, traditional U-shaped segmentation networks and Transformer-based networks often require extensive training datasets, leading to relatively poor generalization performance. With the continuous development of techniques such as adversarial training [22; 23; 24] and pre-training [19; 25], the robustness and generalization capabilities of deep learning models have significantly improved. Adversarial training enhances the robustness of neural networks against adversarial attacks by introducing adversarial examples during the training process, thereby preventing adversarial inputs. This method is widely applied in various domains such as image classification, speech recognition, and natural language processing. It makes models more resilient to targeted perturbations, preventing the model from being misled. Pretraining involves training a model on a large-scale dataset to learn generic feature representations, thereby enhancing its performance on related tasks. Widely applied in the fields of image processing and natural language processing, this approach strengthens the model's generalization capabilities. CLIP [26], through pre-training on large-scale image-text pairs, successfully learned the connections between images and text, demonstrating strong generalization capabilities. Generative Pre-trained Transformer (GPT) is a language model based on the Transformer architecture. Through self-supervised learning pretraining on extensive text data, it acquires universal language representations. GPT excels in various natural language processing tasks, including text generation, text classification, and language understanding, attracting significant attention due to its versatility and generalization capabilities. OpenAI's DALL-E 2 [27] adopted a pre-training mechanism similar to GPT [28], achieving significant improvements in generating diversity, image quality, and understanding text descriptions, expanding the model's application range and performance.