Carrier selection method capable of avoiding pretreatment

A technology of preprocessing and carrier, applied in the field of secret data, it can solve the problem of ignoring preprocessing distortion in embedded distortion, and achieve the effect of improving steganographic security.

Active Publication Date: 2020-12-25
东南数字经济发展研究院
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, existing vector selection methods only focus on embedding distortions and ignore preprocessing distortions

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Carrier selection method capable of avoiding pretreatment
  • Carrier selection method capable of avoiding pretreatment
  • Carrier selection method capable of avoiding pretreatment

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0026] combined with figure 1 , a preprocessing-avoiding carrier selection method, including a preprocessing distortion calculation stage, an embedding distortion calculation stage, and a distortion fusion and carrier selection stage. In this example, the BOSSbass ver.1.01 image library is used to construct the steganographic candidate image set, including 10,000 grayscale images with a size of 512×512. All images are randomly divided into 10 subsets, each with 1000 images. Then select 7 subsets to perform 7 image processing operations: histogram equalization, sharpening, dilation, erosion, Wiener filtering, median filtering, and mean filtering. The remaining 3 subsets are not modified. After processing, use all 10000 images as candidate images {X 1 ,X 2 ,...,X 10000}. Then 70% of the candidate images are preprocessed. In addition, 1338 images from th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a carrier selection method capable of avoiding preprocessing, which comprises a preprocessing distortion calculation stage, an embedded distortion calculation stage and a distortion fusion and carrier selection stage, and is characterized in that a pre-classifier is trained to give the possibility that an image is preprocessed, the higher the possibility is, the larger thepreprocessing distortion is, and for the embedded distortion, the current distortion minimization framework is used for calculation. Finally, the two types of distortion are fused, and an image withminimum total distortion is selected as a carrier. According to the carrier selection method capable of avoiding preprocessing, the selection of the preprocessed image as the carrier is avoided by avoiding the preprocessed image, so that the multi-carrier steganography security is improved.

Description

technical field [0001] The invention relates to the technical field of secret data, in particular to a carrier selection method for avoiding preprocessing. Background technique [0002] Steganography is a technique for conveying secret information through an open channel. Due to its wide application, digital image has become the most commonly used carrier in steganography. To achieve steganography, secret information is usually embedded by slightly modifying the carrier content. At present, steganography mainly guarantees security by minimizing image distortion. Steganalysis, on the other hand, aims to reveal the existence of secret information by analyzing images transmitted on public channels. At present, steganalysis is mainly based on supervised learning, and steganalysis classifiers are trained by extracting image features, and then used to judge suspicious images. Recently, steganalysis based on deep learning integrates feature extraction and classification process...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06T1/00G06T5/00G06T5/20G06T5/30G06T5/40G06K9/62
CPCG06T1/0021G06T5/40G06T5/003G06T5/30G06T5/20G06T2201/0202G06T2207/20024G06F18/285G06F18/25Y02E30/30
Inventor 钱振兴张新鹏李晓龙秦川
Owner 东南数字经济发展研究院
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap