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* [TF FE] Add user guide about TF FE Capabilities and Limitations Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> * Update docs/resources/tensorflow_frontend.md * Update docs/OV_Runtime_UG/protecting_model_guide.md Co-authored-by: Maxim Vafin <maxim.vafin@intel.com> * Update docs/OV_Runtime_UG/deployment/local-distribution.md Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com> Co-authored-by: Maxim Vafin <maxim.vafin@intel.com>
61 lines
3.1 KiB
Markdown
61 lines
3.1 KiB
Markdown
# Using Encrypted Models with OpenVINO {#openvino_docs_OV_UG_protecting_model_guide}
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Deploying deep-learning capabilities to edge devices can present security
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challenges like ensuring inference integrity, or providing copyright
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protection of your deep-learning models.
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One possible solution is to use cryptography to protect models as they are
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deployed and stored on edge devices. Model encryption, decryption and
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authentication are not provided by OpenVINO but can be implemented with
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third-party tools (i.e., OpenSSL). While implementing encryption, ensure that
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the latest versions of tools are used and follow cryptography best practices.
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This guide presents how to use OpenVINO securely with protected models.
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## Secure Model Deployment
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After a model is optimized by the OpenVINO Model Optimizer, it's deployed
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to target devices in the OpenVINO Intermediate Representation (OpenVINO IR) format. An optimized
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model is stored on edge device and is executed by the OpenVINO Runtime.
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TensorFlow (check [TensorFlow Frontend Capabilities and Limitations](../resources/tensorflow_frontend.md)), ONNX
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and PaddlePaddle models can be read natively by OpenVINO Runtime as well.
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Encrypting and optimizing model before deploying it to the edge device can be
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used to protect deep-learning models. The edge device should keep the stored model
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protected all the time and have the model decrypted **in runtime only** for use
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by the OpenVINO Runtime.
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## Loading Encrypted Models
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The OpenVINO Runtime requires model decryption before loading. Allocate
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a temporary memory block for model decryption and use the
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`ov::Core::read_model` method to load the model from a memory buffer.
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For more information, see the `ov::Core` Class Reference Documentation.
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@snippet snippets/protecting_model_guide.cpp part0
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Hardware-based protection such as Intel Software Guard Extensions
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(Intel SGX) can be used to protect decryption operation secrets and
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bind them to a device. For more information, see the [Intel Software Guard
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Extensions](https://software.intel.com/en-us/sgx).
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Use the `ov::Core::read_model` to set model representations and
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weights respectively.
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Currently there is no way to read external weights from memory for ONNX models.
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The `ov::Core::read_model(const std::string& model, const Tensor& weights)` method
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should be called with `weights` passed as an empty `ov::Tensor`.
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@snippet snippets/protecting_model_guide.cpp part1
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## Additional Resources
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- Intel® Distribution of OpenVINO™ toolkit [home page](https://software.intel.com/en-us/openvino-toolkit).
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- Model Optimizer [Developer Guide](../MO_DG/Deep_Learning_Model_Optimizer_DevGuide.md).
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- [OpenVINO™ Runtime User Guide](openvino_intro.md).
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- For more information on Sample Applications, see the [OpenVINO Samples Overview](Samples_Overview.md)
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- For information on a set of pre-trained models, see the [Overview of OpenVINO™ Toolkit Pre-Trained Models](@ref omz_models_group_intel).
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- For IoT Libraries and Code Samples, see the [Intel® IoT Developer Kit](https://github.com/intel-iot-devkit).
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