2003/11/30 by Andreas Ruttor, Wolfgang Kinzel, Lanir Shacham +2 · 3 citations
Computer Science · Physics and Astronomy · #Chaos-based Image/Signal Encryption #Neural Networks and Applications #cond-mat.dis-nn #stochastic dynamics and bifurcation
paper · pdf · doi:10.1103/physreve.69.046110
published as Phys. Rev. E 69, 046110 (2004) · 8 pages, 10 figures; abstract changed, references updated
openalex publication_date 2004/04/27 · arxiv created 2004/07/15 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Neural cryptography is based on a competition between attractive and repulsive stochastic forces. A feedback mechanism is added to neural cryptography which increases the repulsive forces. Using numerical simulations and an analytic approach, the probability of a successful attack is calculated for different model parameters. Scaling laws are derived which show that feedback improves the security of the system. In addition, a network with feedback generates a pseudorandom bit sequence which can be used to encrypt and decrypt a secret message.