387 research outputs found
Diffusion-Stego: Training-free Diffusion Generative Steganography via Message Projection
Generative steganography is the process of hiding secret messages in
generated images instead of cover images. Existing studies on generative
steganography use GAN or Flow models to obtain high hiding message capacity and
anti-detection ability over cover images. However, they create relatively
unrealistic stego images because of the inherent limitations of generative
models. We propose Diffusion-Stego, a generative steganography approach based
on diffusion models which outperform other generative models in image
generation. Diffusion-Stego projects secret messages into latent noise of
diffusion models and generates stego images with an iterative denoising
process. Since the naive hiding of secret messages into noise boosts visual
degradation and decreases extracted message accuracy, we introduce message
projection, which hides messages into noise space while addressing these
issues. We suggest three options for message projection to adjust the trade-off
between extracted message accuracy, anti-detection ability, and image quality.
Diffusion-Stego is a training-free approach, so we can apply it to pre-trained
diffusion models which generate high-quality images, or even large-scale
text-to-image models, such as Stable diffusion. Diffusion-Stego achieved a high
capacity of messages (3.0 bpp of binary messages with 98% accuracy, and 6.0 bpp
with 90% accuracy) as well as high quality (with a FID score of 2.77 for 1.0
bpp on the FFHQ 6464 dataset) that makes it challenging to distinguish
from real images in the PNG format
Generative Steganography Diffusion
Generative steganography (GS) is an emerging technique that generates stego
images directly from secret data. Various GS methods based on GANs or Flow have
been developed recently. However, existing GAN-based GS methods cannot
completely recover the hidden secret data due to the lack of network
invertibility, while Flow-based methods produce poor image quality due to the
stringent reversibility restriction in each module. To address this issue, we
propose a novel GS scheme called "Generative Steganography Diffusion" (GSD) by
devising an invertible diffusion model named "StegoDiffusion". It not only
generates realistic stego images but also allows for 100\% recovery of the
hidden secret data. The proposed StegoDiffusion model leverages a non-Markov
chain with a fast sampling technique to achieve efficient stego image
generation. By constructing an ordinary differential equation (ODE) based on
the transition probability of the generation process in StegoDiffusion, secret
data and stego images can be converted to each other through the approximate
solver of ODE -- Euler iteration formula, enabling the use of irreversible but
more expressive network structures to achieve model invertibility. Our proposed
GSD has the advantages of both reversibility and high performance,
significantly outperforming existing GS methods in all metrics.Comment: Draft for ACM-mm 2023.Shall not be reproduced without permission,
rights reserved
Perfectly Secure Steganography Using Minimum Entropy Coupling
Steganography is the practice of encoding secret information into innocuous
content in such a manner that an adversarial third party would not realize that
there is hidden meaning. While this problem has classically been studied in
security literature, recent advances in generative models have led to a shared
interest among security and machine learning researchers in developing scalable
steganography techniques. In this work, we show that a steganography procedure
is perfectly secure under Cachin (1998)'s information-theoretic model of
steganography if and only if it is induced by a coupling. Furthermore, we show
that, among perfectly secure procedures, a procedure maximizes information
throughput if and only if it is induced by a minimum entropy coupling. These
insights yield what are, to the best of our knowledge, the first steganography
algorithms to achieve perfect security guarantees for arbitrary covertext
distributions. To provide empirical validation, we compare a minimum entropy
coupling-based approach to three modern baselines -- arithmetic coding, Meteor,
and adaptive dynamic grouping -- using GPT-2, WaveRNN, and Image Transformer as
communication channels. We find that the minimum entropy coupling-based
approach achieves superior encoding efficiency, despite its stronger security
constraints. In aggregate, these results suggest that it may be natural to view
information-theoretic steganography through the lens of minimum entropy
coupling
Computing Dependencies between DCT Coefficients for Natural Steganography in JPEG Domain
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