DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion Models
- URL: http://arxiv.org/abs/2407.10459v1
- Date: Mon, 15 Jul 2024 06:15:49 GMT
- Title: DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion Models
- Authors: Yiwei Yang, Zheyuan Liu, Jun Jia, Zhongpai Gao, Yunhao Li, Wei Sun, Xiaohong Liu, Guangtao Zhai,
- Abstract summary: Coverless image steganography (CIS) enhances imperceptibility by not using any cover image.
Recent works have utilized text prompts as keys in CIS through diffusion models.
We propose DiffStega, an innovative training-free diffusion-based CIS strategy for universal application.
- Score: 38.17146643777956
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Traditional image steganography focuses on concealing one image within another, aiming to avoid steganalysis by unauthorized entities. Coverless image steganography (CIS) enhances imperceptibility by not using any cover image. Recent works have utilized text prompts as keys in CIS through diffusion models. However, this approach faces three challenges: invalidated when private prompt is guessed, crafting public prompts for semantic diversity, and the risk of prompt leakage during frequent transmission. To address these issues, we propose DiffStega, an innovative training-free diffusion-based CIS strategy for universal application. DiffStega uses a password-dependent reference image as an image prompt alongside the text, ensuring that only authorized parties can retrieve the hidden information. Furthermore, we develop Noise Flip technique to further secure the steganography against unauthorized decryption. To comprehensively assess our method across general CIS tasks, we create a dataset comprising various image steganography instances. Experiments indicate substantial improvements in our method over existing ones, particularly in aspects of versatility, password sensitivity, and recovery quality. Codes are available at \url{https://github.com/evtricks/DiffStega}.
Related papers
- MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection [64.29452783056253]
The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia.
Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored.
We propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities.
arXiv Detail & Related papers (2024-09-15T13:08:59Z) - Robust Message Embedding via Attention Flow-Based Steganography [34.35209322360329]
Image steganography can hide information in a host image and obtain a stego image that is perceptually indistinguishable from the original one.
We propose a novel message embedding framework, called Robust Message Steganography (RMSteg), which is competent to hide message via QR Code in a host image.
arXiv Detail & Related papers (2024-05-26T03:16:40Z) - Coarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis [65.7968515029306]
We propose a novel Coarse-to-Fine Latent Diffusion (CFLD) method for Pose-Guided Person Image Synthesis (PGPIS)
A perception-refined decoder is designed to progressively refine a set of learnable queries and extract semantic understanding of person images as a coarse-grained prompt.
arXiv Detail & Related papers (2024-02-28T06:07:07Z) - DKiS: Decay weight invertible image steganography with private key [11.41125892113752]
A novel private key-based image steganography technique has been introduced.
Access requires a corresponding private key, regardless of the public knowledge of the steganography method.
A critical challenge in the invertible image steganography process has been identified.
arXiv Detail & Related papers (2023-11-30T04:21:10Z) - Catch You Everything Everywhere: Guarding Textual Inversion via Concept Watermarking [67.60174799881597]
We propose the novel concept watermarking, where watermark information is embedded into the target concept and then extracted from generated images based on the watermarked concept.
In practice, the concept owner can upload his concept with different watermarks (ie, serial numbers) to the platform, and the platform allocates different users with different serial numbers for subsequent tracing and forensics.
arXiv Detail & Related papers (2023-09-12T03:33:13Z) - PRO-Face S: Privacy-preserving Reversible Obfuscation of Face Images via
Secure Flow [69.78820726573935]
We name it PRO-Face S, short for Privacy-preserving Reversible Obfuscation of Face images via Secure flow-based model.
In the framework, an Invertible Neural Network (INN) is utilized to process the input image along with its pre-obfuscated form, and generate the privacy protected image that visually approximates to the pre-obfuscated one.
arXiv Detail & Related papers (2023-07-18T10:55:54Z) - RoSteALS: Robust Steganography using Autoencoder Latent Space [19.16770504267037]
RoSteALS is a practical steganography technique leveraging frozen pretrained autoencoders to free the payload embedding from learning the distribution of cover images.
RoSteALS has a light-weight secret encoder of just 300k parameters, is easy to train, has perfect secret recovery performance and comparable image quality on three benchmarks.
arXiv Detail & Related papers (2023-04-06T22:14:26Z) - Hiding Images in Deep Probabilistic Models [58.23127414572098]
We describe a different computational framework to hide images in deep probabilistic models.
Specifically, we use a DNN to model the probability density of cover images, and hide a secret image in one particular location of the learned distribution.
We demonstrate the feasibility of our SinGAN approach in terms of extraction accuracy and model security.
arXiv Detail & Related papers (2022-10-05T13:33:25Z) - Image Steganography based on Style Transfer [12.756859984638961]
We propose image steganography network based on style transfer.
We embed secret information while transforming the content image style.
In latent space, the secret information is integrated into the latent representation of the cover image to generate the stego images.
arXiv Detail & Related papers (2022-03-09T02:58:29Z) - Multitask Identity-Aware Image Steganography via Minimax Optimization [9.062839197237807]
We propose a framework, called Multitask Identity-Aware Image Steganography (MIAIS), to achieve direct recognition on container images without restoring secret images.
The key issue of the direct recognition is to preserve identity information of secret images into container images and make container images look similar to cover images at the same time.
In order to be flexible for the secret image restoration in some cases, we incorporate an optional restoration network into our method.
arXiv Detail & Related papers (2021-07-13T02:53:38Z)
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.