Instructions to use hacnho/keras-averagepooling1d-poolsize-trigger-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use hacnho/keras-averagepooling1d-poolsize-trigger-poc with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://hacnho/keras-averagepooling1d-poolsize-trigger-poc") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Keras AveragePooling1D Pool Size Trigger MFV PoC
This repository contains a benign Huntr MFV proof of concept.
Files:
control_averagepooling1d_pool_size.keras- control Keras Native artifact withAveragePooling1D(pool_size=2, strides=1).malicious_averagepooling1d_pool_size.keras- malicious Keras Native artifact withAveragePooling1D(pool_size=1, strides=1).reproduce.py- verifies the trigger-specific output flip and scanner output.
Run:
python3 -m venv /tmp/mfv-keras-averagepooling1d
. /tmp/mfv-keras-averagepooling1d/bin/activate
pip install -r requirements.txt
python reproduce.py
Expected result:
- Benign sequences
[0,0,0,0],[1,0,1,0], and[2,0,0,2]keep class0in both artifacts. - Trigger sequence
[0,0,0,4]is class0in the control artifact and class1in the malicious artifact. modelscan==0.8.8reportsNo issues found!.
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