---
title: Image Segmentation
slug: BixN-image-segmentation
docTags: 
createdAt: 2023-07-19T04:04:05.313Z
---

The below input parameters are for different attack types. To start working with the APIs, see [\<POST> Model Analysis](docId\:RPrtAbi5J-w_pPq9D-w1R).

### File upload format

- **Data**: The processed data, ready to be passed to the model for prediction, should be saved in a folder.

[Download sample data](https://aisdocs.blob.core.windows.net/reference/upload/Image/ImageSegmentation/left_images.zip)

- **Label**: A CSV file should be created with two columns: "image" and "label." The first column should contain the image name, and the second column should contain the label. The label should be in integer format. Check sample label file attached.

[Downloab sample label](https://aisdocs.blob.core.windows.net/reference/upload/Image/ImageSegmentation/left_groundTruth.zip)

- **Model**: The model should be saved in either .h5 or TensorFlow format with full architecture. Full architecture is needed when loading the model to the platofrm for assessment either in encrypted or unencrypted. This can be ignored when model is hosted as an API.

[Download sample model](https://aisdocs.blob.core.windows.net/reference/upload/Image/ImageSegmentation/og_model.zip)

## Common parameters

The below table parameters are common for Extraction Attack type.&#x20;

| Parameter                      | Data type | Description                                                                                                    | Remark                                                                                                                                                        |
| ------------------------------ | --------- | -------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| model\_Id                      | String    | Model\_id received during model registration.<br />We need to provide this model ID in query parameter in URL. | You have to do model registration only once for a model to perform model analysis. This will help you track the no of api call made, and it's success metric. |
| **Request Body (Json format)** |           |                                                                                                                |                                                                                                                                                               |
| normalize\_data                | String    | Model trained on Normalized data.                                                                              | if model is trained on normalized data, then set this parameter as "yes" else "no".                                                                           |
| input\_dimensions              | String    | Provide input dimension of the image                                                                           | the parameter should be string in the format "(height, weight, channel)" For example 28\*28\*1                                                                |
| number\_of\_classes            | String    | Number of prediction classes.                                                                                  | the parameter should be string. Example : 10 (Range >0 & \<=200)                                                                                              |
| model\_framework               | String    | Original model is built with tensorflow framework.                                                             | curretly supported framework are: tensorflow, scikit-learn, keras. (Option:\[tensorflow])                                                                     |



:::ExpandableHeading
## Extraction parameters

| Parameter                      | Data type | Description                                                                                                                                                                                      | Remark                                                                                                                                                                                                                                                                                    |
| ------------------------------ | --------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| **Request Body (Json format)** |           |                                                                                                                                                                                                  |                                                                                                                                                                                                                                                                                           |
| attack\_type                   | String    | You can select the attack type either Blackbox or Greybox.                                                                                                                                       | **Blackbox**: for performing model analysis, no information about model or data will be used.                                              **Greybox**: information about data will be leverage for creation of attack data              **Note:&#x20;**&#x6F;nly 2-5 % of data is needed |
| number\_of\_attack\_queries    | String    | Number of attack queries that model will be<br />subjected to.                                                                                                                                   | generally Heigher the number of attack queries, better would be the analysis. And it would take more time to process. (Range:  >0 & \<=400000)                                                                                                                                            |
| vulnerability\_threshold       | String    | Threshold percent of stolen model accuracy<br />at which defense model should be generated.                                                                                                      | Threshold percent of stolen model accuracy<br />at which defense model should be generated<br />(Range :  0.0 - 1)                                                                                                                                                                        |
| model\_api\_details            | String    | If use\_model\_api is Yes, then provide API<br />details of hosted model as encrypted JSON<br />string is mandatory                                                                              | provide this only if use\_model\_api is "yes".                                                                                                                                                                                                                                            |
| use\_model\_api                | String    | Use model API to train your model instead of uploading the model as a zip file.                                                                                                                  | when this parameter is yes, you don't have to upload model as zip. You can pass api url along with other verification credential in json file.                                                                                                                                            |
| defense\_bestonly <br />       | String    | Choose to train your model until it achieves the best results or above 95% accuracy.                                                                                                             | when selected **"yes"**, it will train N number of model and select best model. Ofcourse this will take longer time. If **"no"**, then once defense model accuracy reached above 95% It will stop                                                                                         |
| encryption\_strategy           | Int       | Choose a encryption strategy for you model. if model is uploaded directly as a<br />zip pick 0, 1 if model is encryted as .pyc and<br />uploaded as a zip. Ignore if use\_model\_api is<br />Yes | select 0: pass tensorflow model as it is, select 1: pass encrypted model. It could be .pyc file                                                                                                                                                                                           |
:::

:::hint{type="info"}
To access all sample artifacts, please visit [Artifacts](docId\:iJNEOCXoStabvvrsq11fa).&#x20;

- For specific artifact details,  refer&#x20;
  - Vulnerability Report : [Vulnerability Report](docId\:hL0uT2MWlCBkt8F97fr-W)    &#x20;
  - Sample Attacks : [Sample Attacks](docId:4G1mjM5lQjfm8t5WBVwpr)
  - Defense Report: [Defense Report](docId\:VtzlTtpja2VSF2j0STLSQ)                   &#x20;
  - Defense Model: [Defense Model](docId\:xSbxmZXW4vv14-8NmBF8M)
:::

:::hint{type="info"}
**Note**: For Image segmentation, supported attack types are - Extraction
:::