The Reincarnation of Grille Cipher: A Generative Approach

TL;DR

This paper introduces a novel generative framework for grille cipher encryption using GANs, combining cryptography and steganography to enhance secure communication with promising experimental results.

Contribution

It proposes a new generative approach inspired by Cardan grille, integrating GANs for message encryption and decryption, and introduces new criteria for channel security.

Findings

Effective message encryption and decryption demonstrated

Channel security improved using Jensen-Shannon Divergence

Promising experimental results with semantic image inpainting

Abstract

In order to keep the data secret, various techniques have been implemented to encrypt and decrypt the secret data. Cryptography is committed to the security of content, i.e. it cannot be restored with a given ciphertext. Steganography is to hiding the existence of a communication channel within a stego. However, it has been difficult to construct a cipher (cypher) that simultaneously satisfy both channel and content security for secure communication. Inspired by the Cardan grille, this paper presents a new generative framework for grille cipher. A digital cardan grille is used for message encryption and decryption. The ciphertext is directly sampled by a powerful generator without an explicit cover. Message loss and prior loss are proposed for penalizing message extraction error and unrealistic ciphertext. Jensen-Shannon Divergence is introduced as new criteria for channel security. A simple practical data-driven grille cipher is proposed using semantic image inpainting and generative adversarial network. Experimental results demonstrate the promising of the proposed method.