Transformers -- Messages in Disguise

TL;DR

This paper introduces a novel neural network-based cryptography scheme called RANDOM ANC, which is efficient, unique, and communication overhead-free, potentially revolutionizing adaptive encryption methods.

Contribution

The paper presents the RANDOM ANC model with three new neural network layers, enabling efficient, key-specific encryption without additional communication overhead.

Findings

RANDOM ANC achieves up to 2.5 MB/sec encryption speed.

The model requires only 100 KB storage.

It ensures message uniqueness tied to encryption keys.

Abstract

Modern cryptography, such as Rivest Shamir Adleman (RSA) and Secure Hash Algorithm (SHA), has been designed by humans based on our understanding of cryptographic methods. Neural Network (NN) based cryptography is being investigated due to its ability to learn and implement random cryptographic schemes that may be harder to decipher than human-designed algorithms. NN based cryptography may create a new cryptographic scheme that is NN specific and that changes every time the NN is (re)trained. This is attractive since it would require an adversary to restart its process(es) to learn or break the cryptographic scheme every time the NN is (re)trained. Current challenges facing NN-based encryption include additional communication overhead due to encoding to correct bit errors, quantizing the continuous-valued output of the NN, and enabling One-Time-Pad encryption. With this in mind, the Random Adversarial Data Obfuscation Model (RANDOM) Adversarial Neural Cryptography (ANC) network is introduced. RANDOM is comprised of three new NN layers: the (i) projection layer, (ii) inverse projection layer, and (iii) dot-product layer. This results in an ANC network that (i) is computationally efficient, (ii) ensures the encrypted message is unique to the encryption key, and (iii) does not induce any communication overhead. RANDOM only requires around 100 KB to store and can provide up to 2.5 megabytes per second of end-to-end encrypted communication.