FakeSafe: Human Level Steganography Techniques by Disinformation Mapping Using Cycle-Consistent Adversarial Network

Abstract

Steganography is the task of concealing a message within an overt medium such that the presence of the hidden message is barely detectable. Recently, myriads of works have introduced the inchoate techniques of deep learning to the field of steganography. Nevertheless, existing issues like small payload capacity and image distortion have exceedingly suffocated the steganographic research. In this paper, we propose FakeSafe, a novel cycle-consistent adversarial network proffering human-level steganography. Mapping the confidential information into fake messages, FakeSafe efficaciously precludes the detection of steganalysis algorithms and human eyes. There are three contributions in our work: (i) we construct a multi-step FakeSafe mapping, which significantly impedes the steganalysis models to identify and recover the hidden message; (ii) our steganographic models are robust enough since they are applicable to multifarious data domains, including image and text information; (iii) we introduce a coverless solution to embed the clandestine message within a medium of a specific type in lieu of a dedicated cover. We have conducted experiments using both benchmark and real-world data sets to demonstrate potential applications of FakeSafe, whose open source library is available online at: https://github.com/mikemikezhu/fake-safe.

Keywords

• Generative adversarial networks
• machine learning
• privacy