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With the Increasing use of Machine Learning in Android applications, moreresearch and efforts are being put into developing better-performing machinelearning algorithms with a vast amount of data. Along with machine learning formobile phones, the threat of extraction of trained machine learning models fromapplication packages (APK) through reverse engineering exists. Currently, thereare ways to protect models in mobile applications such as name obfuscation,cloud deployment, last layer isolation. Still, they offer less security, andtheir implementation requires more effort. This paper gives an algorithm toprotect trained machine learning models inside android applications with highsecurity and low efforts to implement it. The algorithm ensures security byencrypting the model and real-time decrypting it with 256-bit AdvancedEncryption Standard (AES) inside the running application. It works efficientlywith big model files without interrupting the User interface (UI) Thread. Ascompared to other methods, it is fast, more secure, and involves fewer efforts.This algorithm provides the developers and researchers a way to secure theiractions and making the results available to all without any concern.

Encryption and Real Time Decryption for protecting Machine Learning models in Android Applications