Deploying deep-learning capabilities to edge devices can present security challenges like ensuring inference integrity, or providing copyright protection of your deep-learning models. OpenVINO provide a simple method with crypto algorithm to protect model in disk. Model encryption, decryption and authentication are not provided by OpenVINO but can be implemented with third-party tools (i.e., OpenSSL). In this example, we use AES-128-cbc algorithm in OpenSSL to demonstrate the model cryptography.
As you can see the mechanism in below image, there are two part to process:
First is to encrypt your plain IR model into encrypted model.
The second part is to use the same password key and IV which used for encryption before to decrypt model at model loading runtime.
Step 1: Encrypt model
Make sure you install the OpenSSL and boost, for example in Ubuntu:
$ sudo apt install openssl libboost-dev
Then use command line to do model encryption by OpenSSL AES-128-CBC algorithm. In this simply example, I use same password for Key and IV, it is hexadecimal of string "openvino encrypt". You can use some online str2hex tool to generate hex representation of your string password.
This blog just provide an example of model encryption by OpenSSL. This method can only protect you model in disk, for total memory crypto, you can refer technologies like OpenVINO™ Security Add-on in virtual machine to provide an isolated environment for security sensitive operations, and use Intel® SGX (Software Guard Extensions) which allows developers to split a computer's memory into private, predefined, highly secure areas called enclaves, which better protect sensitive information.