OpenVINO Security#

Deploying deep learning models for OpenVINO may raise security and privacy issues. Trained models are often valuable intellectual property and you may choose to protect them with encryption or other security tools.

Actual security and privacy requirements depend on your unique deployment scenario. This section provides general guidance on using OpenVINO tools and libraries securely. The main security measure for OpenVINO is its Security Add-on. You can find its description in the Ecosystem section.

Using Encrypted Models with OpenVINO#

Deploying deep-learning capabilities to edge devices can present security challenges like ensuring inference integrity, or providing copyright protection of your deep-learning models.

One possible solution is to use cryptography to protect models as they are deployed and stored on edge devices. Model encryption, decryption and authentication are not provided by OpenVINO but can be implemented with third-party tools (i.e., OpenSSL). While implementing encryption, ensure that the latest versions of tools are used and follow cryptography best practices.

This guide presents how to use OpenVINO securely with protected models.

Secure Model Deployment#

After a model is optimized by model conversion API, it’s deployed to target devices in the OpenVINO Intermediate Representation (OpenVINO IR) format. An optimized model is stored on edge device and is executed by the OpenVINO Runtime. TensorFlow, TensorFlow Lite, ONNX and PaddlePaddle models can be read natively by OpenVINO Runtime as well.

Encrypting and optimizing model before deploying it to the edge device can be used to protect deep-learning models. The edge device should keep the stored model protected all the time and have the model decrypted in runtime only for use by the OpenVINO Runtime.

../_images/deploy_encrypted_model.svg

Loading Encrypted Models#

The OpenVINO Runtime requires model decryption before loading. Allocate a temporary memory block for model decryption and use the ov::Core::read_model method to load the model from a memory buffer. For more information, see the ov::Core Class Reference Documentation.

std::vector<uint8_t> model_data, weights_data;

std::string password; // taken from an user
std::ifstream model_file("model.xml"), weights_file("model.bin");

// Read model files and decrypt them into temporary memory block
decrypt_file(model_file, password, model_data);
decrypt_file(weights_file, password, weights_data);

Hardware-based protection such as Intel Software Guard Extensions (Intel SGX) can be used to protect decryption operation secrets and bind them to a device. For more information, see the Intel Software Guard Extensions.

Use the ov::Core::read_model to set model representations and weights respectively.

Currently there is no way to read external weights from memory for ONNX models. The ov::Core::read_model(const std::string& model, const Tensor& weights) method should be called with weights passed as an empty ov::Tensor.

ov::Core core;
// Load model from temporary memory block
std::string str_model(model_data.begin(), model_data.end());
auto model = core.read_model(str_model,
    ov::Tensor(ov::element::u8, {weights_data.size()}, weights_data.data()));

Encrypted models that have already been compiled, in the form of blob files, can be loaded using the ov::Core::import_model method, as shown in the code sample below:

ov::Core core;
// Import a model from a blob.
std::ifstream compiled_blob(blob, std::ios_base::in | std::ios_base::binary);
auto compiled_model = core.import_model(compiled_blob, "CPU");

Additional Resources#