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facehash: Verify data integrity with hyper-realistic faces

What is this?

Cryptographic hash functions are widely used for verifying data integrity, but hexadecimal (or worse, binary) representations are hard for humans to remember. In contrast, humans have evolved to parse other human faces instantly and to store thousands of them in memory.

This makes faces the ideal representation for cryptographic hashes, at least for quickly checking if some data has changed, even after a long time has passed.

Usage

You need a Linux environment with Python >=3.7 and PyTorch >=1.8 installed. Then install via:

pip install git+https://github.com/alebeck/facehash

facehash should have been added to your PATH, just call it like

facehash file
# orecho -n "hello world!"| facehash
# you can also specify an output file (instead of displaying)echo -n "hello world!"| facehash -o out.png

The first run can take a bit longer as the StyleGAN2 model has to be downloaded and relevant PyTorch extensions are built.

How does it work?

We calculate an extended (512 byte) SHA hash by appending eight nonce values to the input and concatenating the respective SHA-512 hashes. Each byte is expected to be uniformly distributed within its value range, due to the chaotic nature of the hash. We use a Box-Muller transform to deterministically map the uniformly distributed bytes to a 512-dimensional Gaussian latent vector, which is expected by the Generator function of NVIDIA's StyleGAN2-ADA model.

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Verify data integrity with hyper-realistic generative faces

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33 stars

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1 watching

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2 forks
Report repository

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GitHub - alebeck/facehash: Verify data integrity with hyper-realistic generative faces · GitHub
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facehash: Verify data integrity with hyper-realistic faces
What is this?
Cryptographic hash functions are widely used for verifying data integrity, but hexadecimal (or worse, binary) representations are hard for humans to remember. In contrast, humans have evolved to parse other human faces instantly and to store thousands of them in memory.
This makes faces the ideal representation for cryptographic hashes, at least for quickly checking if some data has changed, even after a long time has passed.
Usage
You need a Linux environment with Python >=3.7 and PyTorch >=1.8 installed. Then install via:
pip install git+https://github.com/alebeck/facehash
facehash should have been added to your PATH, just call it like
facehash file
# orecho -n "hello world!"| facehash
# you can also specify an output file (instead of displaying)echo -n "hello world!"| facehash -o out.png
The first run can take a bit longer as the StyleGAN2 model has to be downloaded and relevant PyTorch extensions are built.
How does it work?
We calculate an extended (512 byte) SHA hash by appending eight nonce values to the input and concatenating the respective SHA-512 hashes. Each byte is expected to be uniformly distributed within its value range, due to the chaotic nature of the hash. We use a Box-Muller transform to deterministically map the uniformly distributed bytes to a 512-dimensional Gaussian latent vector, which is expected by the Generator function of NVIDIA's StyleGAN2-ADA model.
About
Verify data integrity with hyper-realistic generative faces
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33 stars
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1 watching
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GitHub - alebeck/facehash: Verify data integrity with hyper-realistic generative faces · GitHub
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facehash: Verify data integrity with hyper-realistic faces
What is this?
Cryptographic hash functions are widely used for verifying data integrity, but hexadecimal (or worse, binary) representations are hard for humans to remember. In contrast, humans have evolved to parse other human faces instantly and to store thousands of them in memory.
This makes faces the ideal representation for cryptographic hashes, at least for quickly checking if some data has changed, even after a long time has passed.
Usage
You need a Linux environment with Python >=3.7 and PyTorch >=1.8 installed. Then install via:
pip install git+https://github.com/alebeck/facehash
facehash should have been added to your PATH, just call it like
facehash file
# orecho -n "hello world!"| facehash
# you can also specify an output file (instead of displaying)echo -n "hello world!"| facehash -o out.png
The first run can take a bit longer as the StyleGAN2 model has to be downloaded and relevant PyTorch extensions are built.
How does it work?
We calculate an extended (512 byte) SHA hash by appending eight nonce values to the input and concatenating the respective SHA-512 hashes. Each byte is expected to be uniformly distributed within its value range, due to the chaotic nature of the hash. We use a Box-Muller transform to deterministically map the uniformly distributed bytes to a 512-dimensional Gaussian latent vector, which is expected by the Generator function of NVIDIA's StyleGAN2-ADA model.
About
Verify data integrity with hyper-realistic generative faces
Resources
Readme
License
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Stars
33 stars
Watchers
1 watching
Forks
2 forks
Report repository
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var __m = "*";
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GitHub - alebeck/facehash: Verify data integrity with hyper-realistic generative faces · GitHub
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facehash: Verify data integrity with hyper-realistic faces
What is this?
Cryptographic hash functions are widely used for verifying data integrity, but hexadecimal (or worse, binary) representations are hard for humans to remember. In contrast, humans have evolved to parse other human faces instantly and to store thousands of them in memory.
This makes faces the ideal representation for cryptographic hashes, at least for quickly checking if some data has changed, even after a long time has passed.
Usage
You need a Linux environment with Python >=3.7 and PyTorch >=1.8 installed. Then install via:
pip install git+https://github.com/alebeck/facehash
facehash should have been added to your PATH, just call it like
facehash file
# orecho -n "hello world!"| facehash
# you can also specify an output file (instead of displaying)echo -n "hello world!"| facehash -o out.png
The first run can take a bit longer as the StyleGAN2 model has to be downloaded and relevant PyTorch extensions are built.
How does it work?
We calculate an extended (512 byte) SHA hash by appending eight nonce values to the input and concatenating the respective SHA-512 hashes. Each byte is expected to be uniformly distributed within its value range, due to the chaotic nature of the hash. We use a Box-Muller transform to deterministically map the uniformly distributed bytes to a 512-dimensional Gaussian latent vector, which is expected by the Generator function of NVIDIA's StyleGAN2-ADA model.
About
Verify data integrity with hyper-realistic generative faces
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Readme
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Stars
33 stars
Watchers
1 watching
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2 forks
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var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - alebeck/facehash: Verify data integrity with hyper-realistic generative faces · GitHub
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facehash: Verify data integrity with hyper-realistic faces
What is this?
Cryptographic hash functions are widely used for verifying data integrity, but hexadecimal (or worse, binary) representations are hard for humans to remember. In contrast, humans have evolved to parse other human faces instantly and to store thousands of them in memory.
This makes faces the ideal representation for cryptographic hashes, at least for quickly checking if some data has changed, even after a long time has passed.
Usage
You need a Linux environment with Python >=3.7 and PyTorch >=1.8 installed. Then install via:
pip install git+https://github.com/alebeck/facehash
facehash should have been added to your PATH, just call it like
facehash file
# orecho -n "hello world!"| facehash
# you can also specify an output file (instead of displaying)echo -n "hello world!"| facehash -o out.png
The first run can take a bit longer as the StyleGAN2 model has to be downloaded and relevant PyTorch extensions are built.
How does it work?
We calculate an extended (512 byte) SHA hash by appending eight nonce values to the input and concatenating the respective SHA-512 hashes. Each byte is expected to be uniformly distributed within its value range, due to the chaotic nature of the hash. We use a Box-Muller transform to deterministically map the uniformly distributed bytes to a 512-dimensional Gaussian latent vector, which is expected by the Generator function of NVIDIA's StyleGAN2-ADA model.
About
Verify data integrity with hyper-realistic generative faces
Resources
Readme
License
Activity
Stars
33 stars
Watchers
1 watching
Forks
2 forks
Report repository
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GitHub - alebeck/facehash: Verify data integrity with hyper-realistic generative faces · GitHub
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facehash: Verify data integrity with hyper-realistic faces
What is this?
Cryptographic hash functions are widely used for verifying data integrity, but hexadecimal (or worse, binary) representations are hard for humans to remember. In contrast, humans have evolved to parse other human faces instantly and to store thousands of them in memory.
This makes faces the ideal representation for cryptographic hashes, at least for quickly checking if some data has changed, even after a long time has passed.
Usage
You need a Linux environment with Python >=3.7 and PyTorch >=1.8 installed. Then install via:
pip install git+https://github.com/alebeck/facehash
facehash should have been added to your PATH, just call it like
facehash file
# orecho -n "hello world!"| facehash
# you can also specify an output file (instead of displaying)echo -n "hello world!"| facehash -o out.png
The first run can take a bit longer as the StyleGAN2 model has to be downloaded and relevant PyTorch extensions are built.
How does it work?
We calculate an extended (512 byte) SHA hash by appending eight nonce values to the input and concatenating the respective SHA-512 hashes. Each byte is expected to be uniformly distributed within its value range, due to the chaotic nature of the hash. We use a Box-Muller transform to deterministically map the uniformly distributed bytes to a 512-dimensional Gaussian latent vector, which is expected by the Generator function of NVIDIA's StyleGAN2-ADA model.
About
Verify data integrity with hyper-realistic generative faces
Resources
Readme
License
Activity
Stars
33 stars
Watchers
1 watching
Forks
2 forks
Report repository
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GitHub - alebeck/facehash: Verify data integrity with hyper-realistic generative faces · GitHub
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facehash: Verify data integrity with hyper-realistic faces
What is this?
Cryptographic hash functions are widely used for verifying data integrity, but hexadecimal (or worse, binary) representations are hard for humans to remember. In contrast, humans have evolved to parse other human faces instantly and to store thousands of them in memory.
This makes faces the ideal representation for cryptographic hashes, at least for quickly checking if some data has changed, even after a long time has passed.
Usage
You need a Linux environment with Python >=3.7 and PyTorch >=1.8 installed. Then install via:
pip install git+https://github.com/alebeck/facehash
facehash should have been added to your PATH, just call it like
facehash file
# orecho -n "hello world!"| facehash
# you can also specify an output file (instead of displaying)echo -n "hello world!"| facehash -o out.png
The first run can take a bit longer as the StyleGAN2 model has to be downloaded and relevant PyTorch extensions are built.
How does it work?
We calculate an extended (512 byte) SHA hash by appending eight nonce values to the input and concatenating the respective SHA-512 hashes. Each byte is expected to be uniformly distributed within its value range, due to the chaotic nature of the hash. We use a Box-Muller transform to deterministically map the uniformly distributed bytes to a 512-dimensional Gaussian latent vector, which is expected by the Generator function of NVIDIA's StyleGAN2-ADA model.
About
Verify data integrity with hyper-realistic generative faces
Resources
Readme
License
Activity
Stars
33 stars
Watchers
1 watching
Forks
2 forks
Report repository
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GitHub - alebeck/facehash: Verify data integrity with hyper-realistic generative faces · GitHub
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facehash: Verify data integrity with hyper-realistic faces
What is this?
Cryptographic hash functions are widely used for verifying data integrity, but hexadecimal (or worse, binary) representations are hard for humans to remember. In contrast, humans have evolved to parse other human faces instantly and to store thousands of them in memory.
This makes faces the ideal representation for cryptographic hashes, at least for quickly checking if some data has changed, even after a long time has passed.
Usage
You need a Linux environment with Python >=3.7 and PyTorch >=1.8 installed. Then install via:
pip install git+https://github.com/alebeck/facehash
facehash should have been added to your PATH, just call it like
facehash file
# orecho -n "hello world!"| facehash
# you can also specify an output file (instead of displaying)echo -n "hello world!"| facehash -o out.png
The first run can take a bit longer as the StyleGAN2 model has to be downloaded and relevant PyTorch extensions are built.
How does it work?
We calculate an extended (512 byte) SHA hash by appending eight nonce values to the input and concatenating the respective SHA-512 hashes. Each byte is expected to be uniformly distributed within its value range, due to the chaotic nature of the hash. We use a Box-Muller transform to deterministically map the uniformly distributed bytes to a 512-dimensional Gaussian latent vector, which is expected by the Generator function of NVIDIA's StyleGAN2-ADA model.
About
Verify data integrity with hyper-realistic generative faces
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Readme
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Stars
33 stars
Watchers
1 watching
Forks
2 forks
Report repository
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