Towards Imperceptible JPEG Image Hiding: Multi-range Representations-driven Adversarial Stego Generation
Journal:
arXiv
Published Date:
Jul 11, 2025
Abstract
Deep hiding has been exploring the hiding capability of deep learning-based
models, aiming to conceal image-level messages into cover images and reveal
them from generated stego images. Existing schemes are easily detected by
steganalyzers due to their large payloads and their limitation to feature
extraction based solely on either pure convolution or pure transformer
operators within a single range, as well as pixel-level loss constraints. To
address the issue, in this paper, we introduce generation-based adversarial
attacks into color JPEG image deep hiding and propose a multi-range
representations-driven adversarial stego generation framework called MRAG from
a steganalysis perspective. Specifically, we integrate the local-range neighbor
reception characteristic of the convolution and the global-range dependency
modeling of the transformer to construct MRAG. Meanwhile, we use the
transformed images obtained through coarse-grained and fine-grained frequency
decomposition as inputs, introducing multi-grained information. Furthermore, a
features angle-norm disentanglement loss is designed to constrain the generated
stegos closer to covers in the angle and norm space of the steganalyzer's
classified features. Consequently, small yet effective adversarial
perturbations can be injected into the process of generating stegos, ensuring
that stegos maintain favorable secret restorability and imperceptibility.
Extensive experiments demonstrate that MRAG can achieve state-of-the-art
performance.
