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---
tags:
- generated_from_keras_callback
model-index:
- name: nathanReitinger/mlcb
results: []
widget:
- text: "window._wpemojiSettings = {'baseUrl':'http:\/\/s.w.org\/images\/core\/emoji\/72x72\/','ext':'.png','source':{'concatemoji':'http:\/\/basho.com\/wp-includes\/js\/wp-emoji-release.min.js?ver=4.2.2'}}; !function(a,b,c){function d(a){var c=b.createElement('canvas'),d=c.getContext&&c.getContext('2d');return d&&d.fillText?(d.textBaseline='top',d.font='600 32px Arial','flag'===a?(d.fillText(String.fromCharCode(55356,56812,55356,56807),0,0),c.toDataURL().length>3e3):(d.fillText(String.fromCharCode(55357,56835),0,0),0!==d.getImageData(16,16,1,1).data[0])):!1}function e(a){var c=b.createElement('script');c.src=a,c.type='text/javascript',b.getElementsByTagName('head')[0].appendChild(c)}var f,g;c.supports={simple:d('simple'),flag:d('flag')},c.DOMReady=!1,c.readyCallback=function(){c.DOMReady=!0},c.supports.simple&&c.supports.flag||(g=function(){c.readyCallback()},b.addEventListener?(b.addEventListener('DOMContentLoaded',g,!1),a.addEventListener('load',g,!1)):(a.attachEvent('onload',g),b.attachEvent('onreadystatechange',function(){'complete'===b.readyState&&c.readyCallback()})),f=c.source||{},f.concatemoji?e(f.concatemoji):f.wpemoji&&f.twemoji&&(e(f.twemoji),e(f.wpemoji)))}(window,document,window._wpemojiSettings);"
example_title: "Word Press Emoji False Positive"
- text: "var canvas = document.createElement('canvas');
var ctx = canvas.getContext('2d');
var txt = 'i9asdm..$#po((^@KbXrww!~cz';
ctx.textBaseline = 'top';
ctx.font = '16px 'Arial'';
ctx.textBaseline = 'alphabetic';
ctx.rotate(.05);
ctx.fillStyle = '#f60';
ctx.fillRect(125,1,62,20);
ctx.fillStyle = '#069';
ctx.fillText(txt, 2, 15);
ctx.fillStyle = 'rgba(102, 200, 0, 0.7)';
ctx.fillText(txt, 4, 17);
ctx.shadowBlur=10;
ctx.shadowColor='blue';
ctx.fillRect(-20,10,234,5);
var strng=canvas.toDataURL();"
example_title: "Canvas Fingerprinting Canonical Example"
inference:
parameters:
wait_for_model: true
use_cache: false
temperature: 0
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# nathanReitinger/mlcb
This model is a fine-tuned version of [dbernsohn/roberta-javascript](https://huggingface.co/dbernsohn/roberta-javascript) on the [mlcb dataset](https://huggingface.co/datasets/nathanReitinger/mlcb).
It achieves the following results on the evaluation set:
- Train Loss: 0.0463
- Validation Loss: 0.0930
- Train Accuracy: 0.9708
- Epoch: 4
## Intended uses & limitations
The model can be used to identify whether a JavaScript program is engaging in canvas fingerprinting.
## Training and evaluation data
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 910, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1291 | 0.1235 | 0.9693 | 0 |
| 0.0874 | 0.1073 | 0.9662 | 1 |
| 0.0720 | 0.1026 | 0.9677 | 2 |
| 0.0588 | 0.0950 | 0.9708 | 3 |
| 0.0463 | 0.0930 | 0.9708 | 4 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.11.0
- Datasets 2.13.2
- Tokenizers 0.13.3
# Citation
```
@inproceedings{reitinger2021ml,
title={ML-CB: Machine Learning Canvas Block.},
author={Nathan Reitinger and Michelle L Mazurek},
journal={Proc.\ PETS},
volume={2021},
number={3},
pages={453--473},
year={2021}
}
```
- [OSF](https://osf.io/shbe7/)
- [GitHub](https://github.com/SP2-MC2/ML-CB)
- [Data](https://dataverse.harvard.edu/dataverse/ml-cb)
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