Invertible Mosaic Image Hiding Network for Very Large Capacity Image Steganography
September 16, 2023 Β· Declared Dead Β· π IEEE International Conference on Acoustics, Speech, and Signal Processing
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Zihan Chen, Tianrui Liu, Jun-Jie Huang, Wentao Zhao, Xing Bi, Meng Wang
arXiv ID
2309.08987
Category
cs.MM: Multimedia
Citations
7
Venue
IEEE International Conference on Acoustics, Speech, and Signal Processing
Last Checked
3 months ago
Abstract
The existing image steganography methods either sequentially conceal secret images or conceal a concatenation of multiple images. In such ways, the interference of information among multiple images will become increasingly severe when the number of secret images becomes larger, thus restrict the development of very large capacity image steganography. In this paper, we propose an Invertible Mosaic Image Hiding Network (InvMIHNet) which realizes very large capacity image steganography with high quality by concealing a single mosaic secret image. InvMIHNet consists of an Invertible Image Rescaling (IIR) module and an Invertible Image Hiding (IIH) module. The IIR module works for downscaling the single mosaic secret image form by spatially splicing the multiple secret images, and the IIH module then conceal this mosaic image under the cover image. The proposed InvMIHNet successfully conceal and reveal up to 16 secret images with a small number of parameters and memory consumption. Extensive experiments on ImageNet-1K, COCO and DIV2K show InvMIHNet outperforms state-of-the-art methods in terms of both the imperceptibility of stego image and recover accuracy of secret image.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Multimedia
π
π
Old Age
R.I.P.
π»
Ghosted
Viewport-Adaptive Navigable 360-Degree Video Delivery
π
π
The Cartographer
A Comprehensive Survey on Cross-modal Retrieval
π
π
The Cartographer
An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
R.I.P.
π»
Ghosted
A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding
R.I.P.
π»
Ghosted
Video Generation From Text
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted