Abstract
FakeSafe: Human Level Steganography Techniques by Disinformation Mapping Using Cycle-Consistent Adversarial Network
Highlights
S TEGANOGRAPHY is the art of covered writing
GENERATIVE ADVERSARIAL NETWORKS(GAN) WITH CYCLE CONSISTENCY LOSS generative adversarial networks (GANs) was adopted as the steganography function F to map private information to fake message in this study based on its marvellous performance in generating fake data sets visually realistic to humans
We explored the feasibility of multi-step FakeSafe encoding of information
Summary
S TEGANOGRAPHY is the art of covered writing. The term dates back to at least 440 B.C. when the Early Greek sovereigns would shave off the hair of slaves, tattoo the furtive messages onto their scalps, and wait for their hair to regrow to shroud the messages to be delivered [1]. Inspired by previous steganographic research, we combined GANs and consistency loss to develop a novel steganography method named FakeSafe, which maps the private information onto a fake message visually indistinguishable from the real messages. MOTIVATION AND FORMULATION The FakeSafe method aims to map the original private information onto a fake but realistically looking message.
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