Instructions to use hacnho/keras-globalmaxpooling1d-dataformat-trigger-poc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use hacnho/keras-globalmaxpooling1d-dataformat-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-globalmaxpooling1d-dataformat-trigger-poc") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Keras GlobalMaxPooling1D Data Format Trigger MFV PoC
This repository contains a benign Huntr MFV proof of concept.
Files:
control_globalmaxpooling1d_data_format.keras- control Keras Native artifact withGlobalMaxPooling1D(data_format="channels_last").malicious_globalmaxpooling1d_data_format.keras- malicious Keras Native artifact withGlobalMaxPooling1D(data_format="channels_first").reproduce.py- verifies the trigger-specific output flip and scanner output.
Run:
python3 -m venv /tmp/mfv-keras-globalmaxpool
. /tmp/mfv-keras-globalmaxpool/bin/activate
pip install -r requirements.txt
python reproduce.py
Expected result:
- Benign sequences keep classes
[0, 1, 0]in both artifacts. - The trigger puts
2.0only at sequence coordinate[0,1]; it is class0in the control artifact and class1in the malicious artifact. modelscan==0.8.8reportsNo issues found!.
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