Double-Flow-based Steganography without Embedding for Image-to-Image Hiding
Not on the map. Why?
Abstract (arXiv)
As an emerging concept, steganography without embedding (SWE) hides a secret message without directly embedding it into a cover. Thus, SWE has the unique advantage of being immune to typical steganalysis methods and can better protect the secret message from being exposed. However, existing SWE methods are generally criticized for their poor payload capacity and low fidelity of recovered secret messages. In this paper, we propose a novel steganography-without-embedding technique, named DF-SWE, which addresses the aforementioned drawbacks and produces diverse and natural stego images. Specifically, DF-SWE employs a reversible circulation of double flow to build a reversible bijective transformation between the secret image and the generated stego image. Hence, it provides a way to directly generate stego images from secret images without a cover image. Besides leveraging the invertible property, DF-SWE can invert a secret image from a generated stego image in a nearly lossless manner and increases the fidelity of extracted secret images. To the best of our knowledge, DF-SWE is the first SWE method that can hide large images and multiple images into one image with the same size, significantly enhancing the payload capacity. According to the experimental results, the payload capacity of DF-SWE achieves 24-72 BPP is 8000-16000 times compared to its competitors while producing diverse images to minimize the exposure risk. Importantly, DF-SWE can be applied in the steganography of secret images in various domains without requiring training data from the corresponding domains. This domain-agnostic property suggests that DF-SWE can 1) be applied to hiding private data and 2) be deployed in resource-limited systems.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 43 references that are in the map. First posted on arXiv 25 Nov 2023.
Builds on
7 of 43 references.
References · 7
- 2014Deep Learning Face Attributes in the Wildcited by 3,693
- 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
- 2015LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loopcited by 1,355
- 2018HiDDeN: Hiding Data With Deep Networkscited by 397
- 2018Invisible Steganography via Generative Adversarial Networkscited by 21
- 2022Secret-to-Image Reversible Transformation for Generative Steganographycited by 13
- 2023Generative Steganographic Flowcited by 7
Built on by
Not cited yet.