SMILENet: Unleashing Extra-Large Capacity Image Steganography via a Synergistic Mosaic InvertibLE Hiding Network

• URL: http://arxiv.org/abs/2503.05118v1
• Date: Fri, 07 Mar 2025 03:31:47 GMT
• Title: SMILENet: Unleashing Extra-Large Capacity Image Steganography via a Synergistic Mosaic InvertibLE Hiding Network
• Authors: Jun-Jie Huang, Zihan Chen, Tianrui Liu, Wentao Zhao, Xin Deng, Xinwang Liu, Meng Wang, Pier Luigi Dragotti,
• Abstract summary: We propose a novel synergistic framework that achieves 25 image hiding through three key innovations.<n>A network architecture coordinates reversible and non-reversible operations to efficiently exploit information redundancy in both secret and cover images.<n>A unified training strategy that coordinates complementary modules to achieve 3.0x higher capacity than existing methods with superior visual quality.
• Score: 71.11351750072936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing image steganography methods face fundamental limitations in hiding capacity (typically $1\sim7$ images) due to severe information interference and uncoordinated capacity-distortion trade-off. We propose SMILENet, a novel synergistic framework that achieves 25 image hiding through three key innovations: (i) A synergistic network architecture coordinates reversible and non-reversible operations to efficiently exploit information redundancy in both secret and cover images. The reversible Invertible Cover-Driven Mosaic (ICDM) module and Invertible Mosaic Secret Embedding (IMSE) module establish cover-guided mosaic transformations and representation embedding with mathematically guaranteed invertibility for distortion-free embedding. The non-reversible Secret Information Selection (SIS) module and Secret Detail Enhancement (SDE) module implement learnable feature modulation for critical information selection and enhancement. (ii) A unified training strategy that coordinates complementary modules to achieve 3.0x higher capacity than existing methods with superior visual quality. (iii) Last but not least, we introduce a new metric to model Capacity-Distortion Trade-off for evaluating the image steganography algorithms that jointly considers hiding capacity and distortion, and provides a unified evaluation approach for accessing results with different number of secret image. Extensive experiments on DIV2K, Paris StreetView and ImageNet1K show that SMILENet outperforms state-of-the-art methods in terms of hiding capacity, recovery quality as well as security against steganalysis methods.

Related papers

• Boosting Semi-Supervised Medical Image Segmentation via Masked Image Consistency and Discrepancy Learning [2.5355185243767986]
  We propose the Masked Image Consistency and Discrepancy Learning (MICD) framework with three key modules. The Cross Feature Consistency (CFC) module fortifies information exchange and model robustness. The Cross Model Discrepancy (CMD) module utilizes EMA teacher networks to oversee outputs and preserve branch diversity.
  arXiv  Detail & Related papers  (2025-03-18T08:20:35Z)
• Multi-Head Attention Driven Dynamic Visual-Semantic Embedding for Enhanced Image-Text Matching [0.8611782340880084]
  This study proposes an innovative visual semantic embedding model, Multi-Headed Consensus-Aware Visual-Semantic Embedding (MH-CVSE)<n>This model introduces a multi-head self-attention mechanism based on the consensus-aware visual semantic embedding model (CVSE) to capture information in multiple subspaces in parallel.<n>In terms of loss function design, the MH-CVSE model adopts a dynamic weight adjustment strategy to dynamically adjust the weight according to the loss value itself.
  arXiv  Detail & Related papers  (2024-12-26T11:46:22Z)
• MonoMM: A Multi-scale Mamba-Enhanced Network for Real-time Monocular 3D Object Detection [9.780498146964097]
  We propose an innovative network architecture, MonoMM, for real-time monocular 3D object detection. MonoMM consists of Focused Multi-Scale Fusion (FMF) and Depth-Aware Feature Enhancement Mamba (DMB) modules. Our method outperforms previous monocular methods and achieves real-time detection.
  arXiv  Detail & Related papers  (2024-08-01T10:16:58Z)
• Diffusion-Based Hierarchical Image Steganography [60.69791384893602]
  Hierarchical Image Steganography is a novel method that enhances the security and capacity of embedding multiple images into a single container. It exploits the robustness of the Diffusion Model alongside the reversibility of the Flow Model. The innovative structure can autonomously generate a container image, thereby securely and efficiently concealing multiple images and text.
  arXiv  Detail & Related papers  (2024-05-19T11:29:52Z)
• Unleashing Network Potentials for Semantic Scene Completion [50.95486458217653]
  This paper proposes a novel SSC framework - Adrial Modality Modulation Network (AMMNet) AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities, and a customized adversarial training scheme leveraging dynamic gradient competition. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin.
  arXiv  Detail & Related papers  (2024-03-12T11:48:49Z)
• MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection [54.52102265418295]
  We propose a novel and effective Multi-Level Fusion network, named as MLF-DET, for high-performance cross-modal 3D object DETection. For the feature-level fusion, we present the Multi-scale Voxel Image fusion (MVI) module, which densely aligns multi-scale voxel features with image features. For the decision-level fusion, we propose the lightweight Feature-cued Confidence Rectification (FCR) module, which exploits image semantics to rectify the confidence of detection candidates.
  arXiv  Detail & Related papers  (2023-07-18T11:26:02Z)
• A Generic Shared Attention Mechanism for Various Backbone Neural Networks [53.36677373145012]
  Self-attention modules (SAMs) produce strongly correlated attention maps across different layers. Dense-and-Implicit Attention (DIA) shares SAMs across layers and employs a long short-term memory module. Our simple yet effective DIA can consistently enhance various network backbones.
  arXiv  Detail & Related papers  (2022-10-27T13:24:08Z)
• Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network [63.69237156340457]
  We present and advocate an explainable approach toward SISR named model-guided deep unfolding network (MoG-DUN) MoG-DUN is accurate (producing fewer aliasing artifacts), computationally efficient (with reduced model parameters), and versatile (capable of handling multiple degradations) The superiority of the proposed MoG-DUN method to existing state-of-theart image methods including RCAN, SRDNF, and SRFBN is substantiated by extensive experiments on several popular datasets and various degradation scenarios.
  arXiv  Detail & Related papers  (2020-09-14T08:23:37Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.