Photo Filter for racial/ethical bias

Remove or change attributes of photos the could cause bias or issue.

Create a balanced dataset for machine learning without compliance or ethical violations.

Built with ❤️ by Matthew Davis - linkedin - github - Medium - Youtube - Facebook

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

Use Cases

• GDPR Protection of Photo Sharing Compliance
• Privacy for Machine Learning utilizing Photos
• Ethical Machine Learning - Remove/Generalize Race/Gender/Skincolor/Generalize

Privy filter is made to remove private data from photos and to give the method for removing or accomodating for unethical bias in data.

Latest Release:

v0.16 - RemoveMeta - Remove the Meta Data from photos

Next Release - alpha - bugs in loading the model

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

• 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 - 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

v0.3 - Direct Aging

• Test Face Aging Algo
• Test Gender Changing Algo
• Testing Race Direct Synthetic Style Transfer
• Fixes for DetectSkin and AdjustSkin

v0.4 - Full Balanced Dataset Creator

• Update Accuracy of getDemographics
• CreateBalancedPhotos(img)

v0.5 - Improve Accuracy and Speed

• Baseline of Full Pipeline per Photo

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 Faces
3. Create alternatives synthetic photos for balanced training

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:

• Face Detect - Colab Testing - Included in the Notebook folder in the repo.
• Facescrub - Colab Testing - Simplest De-identification - Simple blur - Commonly available
• Face Location Extraction
• Basic Library Object Implementation
• v0.1 Release for basic Direct Identifiers
• Pose Extraction Notebook
• Basic Skin Extraction
• Synthetic Face Generation
• Face Swap Testing
• Face Swap Integration into main library
• Fixes for model automated download
• Improved SkinExtraction Protocol
• Skin Adjustment Methodology
• Fixes for face detection to
• v0.2 Release for George Floyd Implementation

Installation

Use the package manager pip to install Privy - Privacy Photo Filter library.

pip install privyfilter

Basic Usage

from privyfilter.privyfilter import Privyfilter as pf

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

Scrub Meta Data

from privyfilter.privyfilter import Privyfilter as pf
img = pf.RemoveMetaData("../TestPics/1Person-Close.jpg")

Face Swap - Inputs imgPath

swapimg = pf.swapFaces("../TestPics/1Person-Close.jpg", "../TestPics/2Person-Close.jpg")
cv2_imshow(swapimg)

EndGoal API:

• 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