On the security of a clipped hopfield neural network-based cryptosystem

Authors:
Daniel Socek;Dubravko Culibrk
Affiliations:
Florida Atlantic University, Boca Raton, FL;Florida Atlantic University, Boca Raton, FL
Venue:
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
Year:
2005

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Abstract

A cryptosystem based on a clipped Hopfield neural network (CHNN) was recently proposed primarily for encryption of digital images and videos. The system is fast and suitable for hardware implementation. The present paper investigates the security aspects of the CHNN-based cryptosystem, and the following weaknesses are pointed out: 1) the cryptosystem is not sufficiently secure against the ciphertext-only attacks due to the weak randomness properties of the generated keystream, and 2) the cryptosystem is insecure against known/chosen-plaintext attacks and only one known plaintext-ciphertext pair is enough to completely break all ciphertexts of the same or smaller size obtained using the same encryption keys. The security of CHNN-based cryptosystem cannot be improved unless the basic model is fundamentally changed.