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https://github.com/deamoner/privyfilter

Privy filter is made to give the method for removing/accommodating for unethical bias in data.
https://github.com/deamoner/privyfilter

bias deidentify direct-identifiers machine-learning photo-privacy privacy racism

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Privy filter is made to give the method for removing/accommodating for unethical bias in data.

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README 

        
# Photo Filter for racial/ethical bias

### Filtering photos for bias(racial, gender) for machine learning.

### Privy filter is your photo dataset augmentor and analyzer for unethical bias(Race, Gender, Age)

## Built with :heart: by [Matthew Davis](https://www.linkedin.com/in/tech-lead-matt-davis/) - [linkedin](https://www.linkedin.com/in/tech-lead-matt-davis/) - [github](https://github.com/Deamoner) - [Medium](https://medium.com/@mdavis_71283) - [Youtube](https://www.youtube.com/channel/UCJNZxBqs8ElqouPqAkZLlqg) - [Facebook](https://www.facebook.com/matthewjamesdavis/) - [Privy Filter Demo](https://privyfilter.herokuapp.com/)

Filtering photos for bias(racial, gender) for machine learning.



### Demo



Privy Filter: [Info and Demo Site](https://privyfilter.herokuapp.com/)



### Use Cases

- Train Machine Learning Models minimizing unethical bias inherent in underlying photo dataset - IE Race, Gender, Age Discrimination

- Data Augmentation of Photo datasets to balance unethical data bias

- Create and Identify Unit Test Photos for datasets to ensure continual testing of bias in classification type problems

- Ethical Machine Learning - Remove Identity/Generalize Identinity/Generalize Race/Gender/Skincolor/Generalize



Privy filter is your photo dataset augmentor and analyzer for unethical bias(Race, Gender, Age)



![privyfilter detect and blurr faces](https://github.com/Deamoner/privyfilter/raw/master/TestPics/privyfilter.jpg)

![privyfilter detect and blurr faces](https://github.com/Deamoner/privyfilter/raw/master/Results/pipeline.png)



### Latest Release:

v0.2 - Sythetic Face Generation and Face Swapping



### Roadmap



#### v0.1 - Basic Direct Identifier Scrubbing - Done



- Face Detect - Needs improvement

- Face Scrub



#### v0.1.6 - Basic Meta Data Scrubber - Done



- RemoveMetaData(imgPath)



#### v0.2.0 - Identify face and replace - Done



- getRandomFakeFace : get Random Synthetic Face - done - needs tmp folder setting

- peopleObject - getFaceInfo - done - face model not loading on

- peopleObject - swapFace(img1, img2)



#### v0.3.0 - ProcessAll and Use Case Direct Finishing



- processOne() - review demographics of current photo - suggest additional photos

- generatePhoto - from one photo and demographic info - generate those new photo demographic utilizing synthetic faces

- processDir() - process all the photos in the directory - first calculate the statistics and distributions - and parameterize a directory to save all sythetic balanced photos.

- Update DemoSite with Use Case direct notes and usability examples



#### v0.4.0 - Identify other identifiers and bias features



- Detect skin - Dataset: http://cs-chan.com/downloads_skin_dataset.html

- AdjustSkin - Can adjust skincolor to generate new dataset photo

- Update processone stats and generatephoto to utilize skincolor data and manipulation

- Update demosite with video

- Demo of CelebA Dataset



#### v0.5 - Direct Aging and Gender



- Test Face Aging Algo

- Test Gender Changing Algo

- Testing Race Direct Synthetic Style Transfer

- Fixes for DetectSkin and AdjustSkin



#### v0.6 - Improve/Increase Original Photoset data quality retention through improve synthetic matching



- Face pose matching for synthetic face replacement

- Review accuracy of underlying demographic model

- New CelebA Demo



#### v0.7 - Increase compatibility of pipeline for photosets



- Support for multi face photo - Scoping - Document API updates

- Support for people but no face in photos - recognize poses without faces



#### v0.8 - Reliability Fixes



- Back to baseline of usecase documentation

- Use Case Automated Testing

-



### Methodology



Scrub all identifying and data possible for bias from the dataset keeping a featureset that still allows for useful machine learning.



1. Identify Features - Faces, People

2. Remove Unethical Bias Data - New Synthetic Faces, Adjust Skin, Gender and Age Transformation

3. Create alternatives synthetic photos for balanced training



## References Work



- https://towardsdatascience.com/survey-d4f168791e57

- https://brie.berkeley.edu/sites/default/files/brie_wp_2018-3.pdf

- https://www.brookings.edu/research/algorithmic-bias-detection-and-mitigation-best-practices-and-policies-to-reduce-consumer-harms/

- https://healthitanalytics.com/news/eliminating-racial-bias-in-algorithm-development



### Process for Features



What needs to be done for roadmap Contribution:

1. Scoping

2. Test Implementation for AI Model pieces - Colab Notebook of Models with Validation Data

3. Library wrapping and Implementation

4. Library Publish



Tasks:

- [x] Face Detect - Colab Testing - Included in the Notebook folder in the repo.

- [X] Facescrub - Colab Testing - Simplest De-identification - Simple blur - Commonly available

- [X] Face Location Extraction

- [X] Basic Library Object Implementation

- [X] v0.1 Release for basic Direct Identifiers

- [X] Pose Extraction Notebook

- [X] Basic Skin Extraction

- [X] Synthetic Face Generation

- [X] Face Swap Testing

- [X] Face Swap Integration into main library

- [X] Fixes for model automated download

- [X] Clean up API for people and privyfilter library to match specs.

- [X] Replace Faces - Replace a face in a photo

- [ ] Scope out API for calculation and methodology of race/gender face demographic balancing

- [ ] Functions to process one photo for demographics and balanced demographics needed return

- [ ] Function generateSytheticPhoto - create the photo required

- [ ] Improved SkinExtraction Protocol

- [ ] Skin Adjustment Methodology

- [ ] Fixes for face detection to

- [ ] v0.2 Release for George Floyd Implementation



## Installation



Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Privy - Privacy Photo Filter library.



```bash

pip install privyfilter

```



## Basic Usage



```python

from privyfilter.privyfilter import Privyfilter as pf



faces2, img = pf.faceScrub("../TestPics/1Person-Close.jpg")



```



## Scrub Meta Data

```python

from privyfilter.privyfilter import Privyfilter as pf

img = pf.RemoveMetaData("../TestPics/1Person-Close.jpg")

```



## Face Swap - Inputs imgPath

```python

swapimg = pf.swapFaces("../TestPics/1Person-Close.jpg", "../TestPics/2Person-Close.jpg")

cv2_imshow(swapimg)

```



## EndGoal API:

   - privyfilter.processDir(srcPath, dstPath) - analyze all photos for demographics in srcdirect, if dstdirect - generate the balanced dataset)

   - privyfilter.processOne(srcPath, dstPath) - analyze photo and suggest demographics of balanced photo dataset - if dstpath then generate

   - privyfilter.generatePhoto(srcPhoto, demographic, dstPath) - generate a photo from the original with the demographic params specified  

   - privyfilter.findFaces(imgpath, demographics=false) or privyfilter.findFaces(cv2 object)

    - returns array of faces and can include demographics.

   - privyfilter.scrubFaces(imgPath) or privyfilter.scrubFaces(cv2 object)

    - returns faces, and scrubbed img

   - privyfilter.swapFaces(img1Path, img2Path) or privyfilter.swapFaces(img1 cv2 object, img2 cv2 Object)

    - replaces all faces in 2nd image with first. Returns new image as cv2 Object.

   - privyfilter.getFakeFace()

    - returns fakeface image object

   - privyfilter.replaceFaces(img1)

    - returns img of all faces randomly replaced with fake ones.



## Deploying Module



```

python setup.py bdist_wheel --universal

twine upload dist/*

```



## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.



Please make sure to update the tests as appropriate.



## License

[MIT](https://choosealicense.com/licenses/mit/)