DKiS: Decay weight invertible image steganography with private key

• URL: http://arxiv.org/abs/2311.18243v2
• Date: Thu, 18 Jan 2024 07:47:24 GMT
• Title: DKiS: Decay weight invertible image steganography with private key
• Authors: Hang Yang, Yitian Xu, Xuhua Liu
• Abstract summary: 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.
• Score: 11.41125892113752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image steganography, defined as the practice of concealing information within another image, traditionally encounters security challenges when its methods become publicly known or are under attack. To address this, a novel private key-based image steganography technique has been introduced. This approach ensures the security of the hidden information, as access requires a corresponding private key, regardless of the public knowledge of the steganography method. Experimental evidence has been presented, demonstrating the effectiveness of our method and showcasing its real-world applicability. Furthermore, a critical challenge in the invertible image steganography process has been identified by us: the transfer of non-essential, or `garbage', information from the secret to the host pipeline. To tackle this issue, the decay weight has been introduced to control the information transfer, effectively filtering out irrelevant data and enhancing the performance of image steganography. The code for this technique is publicly accessible at https://github.com/yanghangAI/DKiS, and a practical demonstration can be found at http://yanghang.site/hidekey.

Related papers

• DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion Models [38.17146643777956]
  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.
  arXiv  Detail & Related papers  (2024-07-15T06:15:49Z)
• Privacy-preserving Optics for Enhancing Protection in Face De-identification [60.110274007388135]
  We propose a hardware-level face de-identification method to solve this vulnerability. We also propose an anonymization framework that generates a new face using the privacy-preserving image, face heatmap, and a reference face image from a public dataset as input.
  arXiv  Detail & Related papers  (2024-03-31T19:28:04Z)
• PRIS: Practical robust invertible network for image steganography [10.153270845070676]
  Image steganography is a technique of hiding secret information inside another image, so that the secret is not visible to human eyes. Most of the existing image steganography methods have low hiding robustness when the container images affected by distortion. This paper proposed PRIS to improve the robustness of image steganography, it based on invertible neural networks.
  arXiv  Detail & Related papers  (2023-09-24T12:29:13Z)
• Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks [64.67735676127208]
  Text-to-image diffusion models have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. We introduce customized solutions by fully exploiting the aforementioned free attention masks.
  arXiv  Detail & Related papers  (2023-08-13T10:07:46Z)
• Attribute-preserving Face Dataset Anonymization via Latent Code Optimization [64.4569739006591]
  We present a task-agnostic anonymization procedure that directly optimize the images' latent representation in the latent space of a pre-trained GAN. We demonstrate through a series of experiments that our method is capable of anonymizing the identity of the images whilst -- crucially -- better-preserving the facial attributes.
  arXiv  Detail & Related papers  (2023-03-20T17:34:05Z)
• ConfounderGAN: Protecting Image Data Privacy with Causal Confounder [85.6757153033139]
  We propose ConfounderGAN, a generative adversarial network (GAN) that can make personal image data unlearnable to protect the data privacy of its owners. Experiments are conducted in six image classification datasets, consisting of three natural object datasets and three medical datasets.
  arXiv  Detail & Related papers  (2022-12-04T08:49:14Z)
• 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)
• Robust Face-Swap Detection Based on 3D Facial Shape Information [59.32489266682952]
  Face-swap images and videos have attracted more and more malicious attackers to discredit some key figures. Previous pixel-level artifacts based detection techniques always focus on some unclear patterns but ignore some available semantic clues. We propose a biometric information based method to fully exploit the appearance and shape feature for face-swap detection of key figures.
  arXiv  Detail & Related papers  (2021-04-28T09:35:48Z)
• A Novel Local Binary Pattern Based Blind Feature Image Steganography [12.970738540611855]
  A novel feature based blind image steganography technique is proposed, which preserves the LBP (Local binary pattern) feature of the cover with comparable embedding rates. The proposed scheme computes the local binary pattern to hide the bits of the secret image in such a way that the local relationship that exists in the cover are preserved in the resulting stego image.
  arXiv  Detail & Related papers  (2021-01-16T06:37:00Z)
• Towards Face Encryption by Generating Adversarial Identity Masks [53.82211571716117]
  We propose a targeted identity-protection iterative method (TIP-IM) to generate adversarial identity masks. TIP-IM provides 95%+ protection success rate against various state-of-the-art face recognition models.
  arXiv  Detail & Related papers  (2020-03-15T12:45:10Z)

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.