Exploiting Fine-Grained DCT Representations for Hiding Image-Level Messages within JPEG Images
Not on the map. Why?
Abstract (arXiv)
Unlike hiding bit-level messages, hiding image-level messages is more challenging, which requires large capacity, high imperceptibility, and high security. Although recent advances in hiding image-level messages have been remarkable, existing schemes are limited to lossless spatial images as covers and cannot be directly applied to JPEG images, the ubiquitous lossy format images in daily life. The difficulties of migration are caused by the lack of targeted design and the loss of details due to lossy decompression and re-compression. Considering that taking DCT densely on 8×8 image patches is the core of the JPEG compression standard, we design a novel model called textsfEFDR, which can comprehensively underlineExploit underlineFine-grained underlineDCT underlineRepresentations and embed the secret image into quantized DCT coefficients to avoid the lossy process. Specifically, we transform the JPEG cover image and hidden secret image into fine-grained DCT representations that compact the frequency and are associated with the inter-block and intra-block correlations. Subsequently, the fine-grained DCT representations are further enhanced by a sub-band features enhancement module. Afterward, a transformer-based invertibility module is designed to fuse enhanced sub-band features. Such a design enables a fine-grained self-attention on each sub-band and captures long-range dependencies while maintaining excellent reversibility for hiding and recovery. To our best knowledge, this is the first attempt to embed a color image of equal size in a color JPEG image. Extensive experiments demonstrate the effectiveness of our textsfEFDR with superior performance.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 44 references that are in the map. First posted on arXiv 11 May 2023.
Builds on
17 of 44 references.
References · 17
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2014Microsoft COCO: Common Objects in Contextcited by 17,608
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,177
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2016Density estimation using Real NVPcited by 2,042
- 2018Glow: Generative Flow with Invertible 1x1 Convolutionscited by 1,545
- 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,369
- 2020On Layer Normalization in the Transformer Architecturecited by 922
- 2020Invertible Image Rescalingcited by 92
- 2019SteganoGAN: High Capacity Image Steganography with GANscited by 72
5 more
- 2017Generating Steganographic Images via Adversarial Trainingcited by 58
- 2018CNN Based Adversarial Embedding with Minimum Alteration for Image Steganographycited by 22
- 2017Steganographic Generative Adversarial Networkscited by 21
- 2019Reversible GANs for Memory-efficient Image-to-Image Translationcited by 19
- 2017SSGAN: Secure Steganography Based on Generative Adversarial Networkscited by 18
Built on by
Citing papers per year: 2025: 1.
1 paper.
Similar, not cited
Close in wording, no citation either way
- 2025Seeing Delta Parameters as JPEG Images: Data-Free Delta Compression with Discrete Cosine Transform
- 2023Differentiable JPEG: The Devil is in the Details
- 2023JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer
- 2025DCT-Shield: A Robust Frequency Domain Defense against Malicious Image Editing
- 2023Image Data Hiding in Neural Compressed Latent Representations
- 2023Stable Messenger: Steganography for Message-Concealed Image Generation
- 2025Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations
- 2025Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal