Perceptual hashing tools for detecting child sexual abuse material
source link: https://github.com/thorn-oss/perception
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perception
perception
provides flexible, well-documented, and comprehensively tested tooling for perceptual hashing research, development, and production use. See the documentation
for details.
Background
perception
was initially developed at Thorn
as part of our work to eliminate child sexual abuse material from the internet. For more information on the issue, check out our CEO's TED talk
.
Getting Started
Installation
pip install opencv-python perception
Hashing
Hashing with different functions is simple with perception
.
from perception import hashers file1, file2 = 'test1.jpg', 'test2.jpg' hasher = hashers.PHash() hash1, hash2 = hasher.compute(file1), hasher.compute(file2) distance = hasher.compute_distance(hash1, hash2)
Examples
See below for end-to-end examples for common use cases for perceptual hashes.
Supported Hashing Algorithms
perception
currently ships with:
perception.hashers.PHash perception.hashers.PDQ perception.hashers.DHash perception.hashers.AverageHash perception.hashers.MarrHildreth perception.hashers.ColorMoment perception.hashers.BlockMean
Contributing
To work on the project, start by doing the following.
# Install local dependencies for # code completion, etc. make init # Build the Docker container to run # tests and such. make build
make lab-server make precommit make documentation-server
To implement new features, please first file an issue proposing your change for discussion.
To report problems, please file an issue with sample code, expected results, actual results, and a complete traceback.
Alternatives
There are other packages worth checking out to see if they meet your needs for perceptual hashing. Here are some examples.
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