Transformers—Messages in disguise
Year: 2025
Type: article
Abstract: Compared to human-designed algorithms, Neural Network (NN)-based cryptography can create a new NN-specific cryptographic scheme that changes every time the NN is (re)trained. NN retraining is advantageous because it requires an adversary to restart its process(es) to learn or break the cryptographic scheme every time the NN is (re)trained. However, NN-based encryption faces challenges, including communication overhead due to encoding for bit errors, quantizing the NN’s continuous-valued output, and enabling One-Time Pad encryption. With this in mind, this work introduces the Random Adversarial Data Obfuscation Model (RANDOM), an Adversarial Neural Cryptography (ANC) scheme. RANDOM is computationally efficient, can generate a new cryptographic mapping in 16 seconds, ensures the encrypted message is unique to the encryption key, does not induce any communication overhead, requires around 100 Kb of memory, and provides up to 2.5 Mb/s of end-to-end encrypted communication.
Institution: University of Tennessee at Chattanooga
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,340
OpenAlex ID: W4410487231