CN110796585A - Image hiding method based on deep learning - Google Patents

Image hiding method based on deep learning Download PDF

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Publication number
CN110796585A
CN110796585A CN201911033271.6A CN201911033271A CN110796585A CN 110796585 A CN110796585 A CN 110796585A CN 201911033271 A CN201911033271 A CN 201911033271A CN 110796585 A CN110796585 A CN 110796585A
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image
block
carrier
target hidden
parameters
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CN110796585B (en
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陈�峰
郭庆
张晗
宋志华
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Air Force Engineering University of PLA
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Air Force Engineering University of PLA
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G06T1/0021—Image watermarking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/50—Image enhancement or restoration using two or more images, e.g. averaging or subtraction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T9/00—Image coding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20021—Dividing image into blocks, subimages or windows
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20212—Image combination
    • G06T2207/20221—Image fusion; Image merging

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)

Abstract

The invention discloses an image hiding method based on deep learning, which specifically comprises the following operation steps: s1: importing a target hidden image a and a carrier image A as a carrier thereof; s2: respectively segmenting the target hidden image a and the carrier image A according to the same pixel size; s3: acquisition block amAnd block AmThe characteristic parameters of (1); s4: deep learning, namely respectively performing cross comparison on data in the image block a database and the image block A database; s5: using block amSegment A with closest replacement parametersmAnd all the recombined picture blocks AmAnd recombining to form an image for output. The invention replaces the picture blocks of the carrier image by the picture blocks of the target hidden imageThe image blocks of the marked hidden image have higher dispersibility, and finally the carrier image blocks containing the target hidden image blocks are recombined, so that the target hidden image is dispersed in the carrier image by the tiny image blocks, is difficult to detect and has stronger confidentiality.

Description

Image hiding method based on deep learning
Technical Field
The invention belongs to the technical field of image hiding, and particularly relates to an image hiding method based on deep learning.
Background
In the image hiding technology, images, videos, voices and text digital texts are used as carriers, and information to be hidden is distributed in the carriers and cannot be easily found. The image hiding technology has important significance for military, intelligence and national security. The existing image hiding method is that after data transformation, a target hidden image is hidden in a certain area of a carrier image as a whole in a centralized way, so that the boundary of the target hidden image is obviously different from the carrier image, and the defect that the target hidden image is easy to recognize exists.
Disclosure of Invention
The present invention is directed to an image hiding method based on deep learning to solve the above problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme: an image hiding method based on deep learning specifically comprises the following operation steps:
s1: importing a target hidden image a and a carrier image A serving as a carrier of the target hidden image a, comparing the overall parameters of the target hidden image a and the carrier image A, mapping and transforming the parameters of the target hidden image a to approximate the parameters of the carrier image A, and encrypting the mapping and transforming parameters;
s2: respectively segmenting the target hidden image a and the carrier image A according to the same pixel size to obtain a plurality of image blocks a of the target hidden image amAnd a plurality of blocks A of the carrier image Am；
S3: acquisition block amAnd block AmRespectively and correspondingly storing the characteristic parameters in an image block a database and an image block A database;
s4: deep learning, cross-comparing the data in the image block a database and the image block A database respectively, and determining the data to be compared with the image block amSegment A with the closest parametersm；
S5: using block amSegment A with closest replacement parametersmAnd all the recombined picture blocks AmAnd recombining to form an image for output.
Preferably, the global parameters in step S1 include Tamura texture feature, HOG feature and LBP feature.
Preferably, the division size of the image in step S2 is 5-20px × 5-20 px.
Preferably, in the step S2, when the image is divided, the divided block a is subjected to key pairmAnd block AmThe location parameter of (2) is encrypted.
Preferably, block a of step S3mAnd block AmThe characteristic parameters include gray values and color moments.
The invention has the technical effects and advantages that:
the invention can obtain the processing image which is close to the parameters of the carrier image by processing the target hidden image in advance, finely dividing the processed target hidden image and the carrier image, obtaining the image blocks of the carrier image which are similar to the image blocks of the target hidden image by comparison, replacing the image blocks of the carrier image by the image blocks of the target hidden image, and finally recombining the image blocks of the carrier image which contain the image blocks of the target hidden image to ensure that the target hidden image is dispersed in the carrier image by the tiny image blocks, thereby realizing the hiding of the target hidden image in the carrier image, being difficult to find, and the image blocks are encrypted to have stronger confidentiality.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a diagram of a deep learning structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1-2, an image hiding method based on deep learning specifically includes the following operation steps:
s1: introducing a target hidden image a and a carrier image A serving as a carrier of the target hidden image a, comparing the target hidden image a with the overall parameters of the carrier image A, and mapping and transforming the parameters of the target hidden image a to approximate the parameters of the carrier image A, wherein in order to improve the hiding effect, the area of the carrier image A occupied by the target hidden image a is not more than 20%, and after transforming Tamura texture characteristics, HOG characteristics and LBP characteristic parameters of the target hidden image a, the target hidden image a is more similar to the carrier image A, which is beneficial to hiding the target hidden image a in the carrier image A, wherein the transformation rule of the target hidden image a is encrypted, so that a lawless person is prevented from cracking the target hidden image a in the carrier image A;
s2: respectively segmenting the target hidden image a and the carrier image A according to the same pixel size to obtain a plurality of image blocks a of the target hidden image amAnd a plurality of blocks A of the carrier image AmIn the image segmentation, theoretically, the smaller the image segmentation is, the easier the image segmentation is to hide in the carrier image, and considering the actual technical conditions, the segmentation size of the image is selected from the segmentation size of 5-20px × 5-20px, and the segmented image block a is subjected to key pair segmentationmAnd block AmThe position parameter of the image block a is encrypted, so that the image block a is convenient to decryptmAnd block AmCarrying out reduction;
s3: acquisition block amAnd block AmCharacteristic parameter of (1), block amAnd block AmThe characteristic parameters comprise gray values and color moments, and are respectively and correspondingly stored in the image block a database and the image block A database, and the image block a in a small rangemAnd block AmDue to the small span, the segment amAnd block AmCan be processed similar to a monochromatic pattern in a small range, and the smaller the segmentation image is, the more unitary the chroma is, thereby being more convenient to collectGrey scale value and color moment parameters;
s4: deep learning, cross-comparing the data in the image block a database and the image block A database respectively, and determining the data to be compared with the image block amSegment A with the closest parametersm；
S5: using block amSegment A with closest replacement parametersmAfter determination, use block amReplacement of Picture Block AmUntil all the blocks amEntering the image block A database through replacement, recording the corresponding relationship of the replacement as a secret key for storage, and recording all the reconstructed image blocks A in the image block A databasemAnd recombining to form an image for output.
The invention can obtain the processing image which is close to the parameters of the carrier image by processing the target hidden image in advance, obtains the image blocks of the carrier image which are similar to the image blocks of the target hidden image by performing fine segmentation on the processed target hidden image and the carrier image, replaces the image blocks of the carrier image by the image blocks of the target hidden image, has higher dispersity when replacing the image blocks of the carrier image, and finally recombines the image blocks of the carrier image which contain the image blocks of the target hidden image to ensure that the target hidden image is dispersed in the carrier image by the small image blocks, thereby realizing the purpose that the target hidden image is hidden in the carrier image, difficult to find and has stronger confidentiality.
When decryption is needed, the image blocks are divided according to the same specification, the carrier image blocks with the replacement relation are made into target hidden image blocks correspondingly, then the position parameters of the target hidden image blocks are decrypted according to the secret keys during the division, so that the target hidden images are combined into a complete image, and Tamura texture characteristics, HOG characteristics and LBP characteristic parameters of the image are restored to obtain the target hidden image.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.

Claims (5)

1. An image hiding method based on deep learning is characterized in that: the method specifically comprises the following operation steps:
s1: importing a target hidden image a and a carrier image A serving as a carrier of the target hidden image a, comparing the overall parameters of the target hidden image a and the carrier image A, mapping and transforming the parameters of the target hidden image a to approximate the parameters of the carrier image A, and encrypting the mapping and transforming parameters;
s2: respectively segmenting the target hidden image a and the carrier image A according to the same pixel size to obtain a plurality of image blocks a of the target hidden image amAnd a plurality of blocks A of the carrier image Am；
S3: acquisition block amAnd block AmRespectively and correspondingly storing the characteristic parameters in an image block a database and an image block A database;
s4: deep learning, cross-comparing the data in the image block a database and the image block A database respectively, and determining the data to be compared with the image block amSegment A with the closest parametersm；
S5: using block amSegment A with closest replacement parametersmAnd all the recombined picture blocks AmAnd recombining to form an image for output.
2. The image hiding method based on deep learning of claim 1, wherein: the overall parameters in step S1 include Tamura texture features, HOG features, LBP features.
3. The image hiding method based on deep learning of claim 1, wherein: the division size of the image in step S2 is 5-20px × 5-20 px.
4. The image hiding method based on deep learning of claim 1, wherein: in the step S2, when the image is divided, the divided block a is subjected to key pairmAnd block AmThe location parameter of (2) is encrypted.
5. The image hiding method based on deep learning of claim 1, wherein: step S3, block amAnd block AmThe characteristic parameters include gray values and color moments.
CN201911033271.6A 2019-10-28 2019-10-28 Image hiding method based on deep learning Expired - Fee Related CN110796585B (en)

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US20110194727A1 (en) * 2010-02-11 2011-08-11 National Taiwan University Of Science & Technology Image data processig systems for hiding secret information and data hiding methods using the same
JP2011216070A (en) * 2010-03-31 2011-10-27 Fujitsu Ltd Method for restoring hidden character and image processor
CN109547663A (en) * 2018-09-13 2019-03-29 华南理工大学 A kind of improvement LSB image latent writing method of combining cipher
CN110276753A (en) * 2019-06-20 2019-09-24 兰州理工大学 Objective self-adapting hidden method based on the mapping of feature space statistical information
US20200184592A1 (en) * 2017-05-19 2020-06-11 Google Llc Hiding Information and Images via Deep Learning

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Publication number Priority date Publication date Assignee Title
US20110194727A1 (en) * 2010-02-11 2011-08-11 National Taiwan University Of Science & Technology Image data processig systems for hiding secret information and data hiding methods using the same
JP2011216070A (en) * 2010-03-31 2011-10-27 Fujitsu Ltd Method for restoring hidden character and image processor
US20200184592A1 (en) * 2017-05-19 2020-06-11 Google Llc Hiding Information and Images via Deep Learning
CN109547663A (en) * 2018-09-13 2019-03-29 华南理工大学 A kind of improvement LSB image latent writing method of combining cipher
CN110276753A (en) * 2019-06-20 2019-09-24 兰州理工大学 Objective self-adapting hidden method based on the mapping of feature space statistical information

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