CN107979711A - Based on the method for optimizing distortion modification prevention hided transmission - Google Patents

Based on the method for optimizing distortion modification prevention hided transmission Download PDF

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CN107979711A
CN107979711A CN201711212279.XA CN201711212279A CN107979711A CN 107979711 A CN107979711 A CN 107979711A CN 201711212279 A CN201711212279 A CN 201711212279A CN 107979711 A CN107979711 A CN 107979711A
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distortion
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CN107979711B (en
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王子驰
钱振兴
张新鹏
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University of Shanghai for Science and Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
    • H04N1/32—Circuits or arrangements for control or supervision between transmitter and receiver or between image input and image output device, e.g. between a still-image camera and its memory or between a still-image camera and a printer device
    • H04N1/32101—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title
    • H04N1/32144—Display, printing, storage or transmission of additional information, e.g. ID code, date and time or title embedded in the image data, i.e. enclosed or integrated in the image, e.g. watermark, super-imposed logo or stamp
    • H04N1/32149—Methods relating to embedding, encoding, decoding, detection or retrieval operations
    • H04N1/32267—Methods relating to embedding, encoding, decoding, detection or retrieval operations combined with processing of the image

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Image Processing (AREA)
  • Facsimile Transmission Control (AREA)

Abstract

The present invention proposes a kind of based on the method for optimizing distortion modification prevention hided transmission.The extraction of secret data is destroyed by slight modifications view data, while location revision is concentrated on into the region of image fault minimum and selects optimal modification direction to minimize image fault so that amended image still keeps higher quality.The secret data in image is being destroyed under conditions of optimizing image fault completely, to achieve the purpose that to prevent secret data from transmitting.The present invention is suitable for uncompressed image and jpeg image at the same time.

Description

Method for blocking concealed transmission based on optimized distortion modification
Technical Field
The invention relates to the field of secret data transmission, in particular to a method for preventing hidden transmission based on optimized distortion modification.
Background
Steganography, i.e., the transmission of information in a concealed manner, conveys secret information through normal behavior or carriers with the aim of not causing third party perception. Digital image steganography embeds secret information by slightly modifying a carrier image, and transmits a secret image to realize covert communication. In the initial digital image steganography method, the anti-detection performance of steganography is ensured mainly by maintaining the statistical characteristics of images unchanged, or the embedding efficiency of steganography is improved by utilizing channel coding. However, the above method does not achieve satisfactory performance due to the lack of accurate statistical models of digital images and the difference in modification costs at different locations of the images. The situation is broken by STC (synchronous Trellis Coding) proposed by Fridrich team in 2011, and the occurrence of STC enables the emphasis of steganography to be changed from a construction Coding method to a design distortion cost function. In this coding framework, a distortion cost function needs to be designed for the carrier image. The distortion cost function assigns a cost value for measuring the modification risk to each carrier element, and the overall distortion of the dense object is expressed as the sum of the cost values of all modified elements. For a given embedding rate, STC coding can minimize additive distortion between a carrier and a dense object under a custom distortion cost function, and the embedding efficiency of the STC coding is close to the theoretical limit under the condition of the additive distortion.
Digital images which are massively spread on a network in recent years provide great convenience for steganography, and how to prevent the transmission of steganographic secret data becomes a crucial problem in network space security. Steganalysis is a technique for detecting the presence of secret data in an image. At present, steganalysis mainly detects the existence of secret data through a supervised machine learning mode of high-dimensional features and integration classification. But because the current steganography analysis tool detects steganography at low embedding volumes (less than 0.05 bits per pixel) with very high error rates (greater than 40%), it approximates random guessing. Therefore, the image that the steganalysis tool decides is normal still highly likely to contain secret data. To completely prevent the transmission of the secret data, the image should be slightly altered to destroy the secret data. On the other hand, since normal image processing operations such as noise reduction, recompression, beautification, etc., which are often used in social networks, may cause the steganalysis tool to misinterpret the image as a dense image, the image determined to be dense may not be intercepted in its entirety. The portion of the image may also be subjected to the same modification operation to break the possibility of secret data transmission.
Disclosure of Invention
In view of the drawbacks of the prior art, it is an object of the present invention to provide a method for blocking concealed transmissions based on optimized distortion modification. The extraction of the secret data is destroyed by slightly modifying the image data, while the modification positions are concentrated in the region with the least image distortion and the best modification direction is selected to minimize the image distortion, so that the modified image still maintains a high quality and the normal use of the image is not affected.
In order to achieve the purpose, the invention adopts the following technical scheme:
a method for modifying the blocking of concealed transmissions based on optimized distortion includes a modification priority value calculation stage, a modification location determination stage, and a modification direction determination stage. Let the image to be modified contain n elements (for uncompressed images, "element" refers to image pixel; for JPEG images, "element" refers to image DCT coefficient), the ith element is denoted as x i I belongs to {1,2, \8230;, n }. W distortion cost functions are provided for use, and the jth distortion cost function is allocated to x i Has a cost value of I.e. w cost values per image element. The method comprises the following specific steps:
(1) Assigning each distortion cost function to n cost values of n picture elementsThe ranges of (A) and (B) are respectively limited to 0-1, as shown in formula (1). So that the range of the cost value allocated by each distortion cost function is the same.
(2) For all picture elements { x 1 ,x 2 ,…,x n W cost values assigned by w distortion cost functions are calculated respectivelyAverage value of (2)As shown in formula (2).
(3) Calculating the respective average cost values according to the formula (3)Inverse of (a) { theta } 1 ,θ 2 ,…,θ n }。
(4) Will { theta } 1 ,θ 2 ,…,θ n The symbols are sorted from large to small, and the sorted value is set as { theta' 1 ,θ′ 2 ,…,θ′ n H and { theta' 1 ,θ′ 2 ,…,θ′ n Respective picture elements are { x' 1 ,x′ 2 ,…,x′ n }. Let the modification ratio of the image element be r, (0)<r&lt, 1), the image element needing to be modified is { x' 1 ,x′ 2 ,…,x′ k K = round (r × n), round (·) denotes rounding. It is known from experience that: for an uncompressed image, the modification ratio r of the elements is set to 0.01; for a JPEG image, the modification ratio r of an element is calculated according to equation (4), wherein QF is the quality factor of JPEG compression.
(5) To { x' 1 ,x′ 2 ,…,x′ k Calculating their predicted values, respectivelyThe calculation method is as follows:
let us say that the image is represented in two-dimensional form, element x i Corresponds to x u,v U ∈ {1,2, \8230;, M }, v ∈ {1,2, \8230;, N }, where M and N are image sizes. For uncompressed images, x is calculated by equation (5) u,v Predicted value of (2)For JPEG image, x is calculated by formula (6) u,v Predicted value of (2)The elements beyond the edges of the image are obtained by means of symmetric filling. For example, if the element x u+1,v Beyond the image edge, use x u-1,v Instead of this.
(6) Calculating { x' 1 ,x′ 2 ,…,x′ k The predicted value ofAfter that, { x' 1 ,x′ 2 ,…,x′ k The modified element is { x ″) 1 ,x″ 2 ,…,x″ k And finishing the modification of the image data.
Compared with the prior art, the invention has the following outstanding advantages:
the method can completely destroy the secret data in the image, thereby preventing the transmission of the secret data; the modification operation has minimal impact on the image, i.e. the distortion of the image is optimized, and the modified image does not affect normal use.
Drawings
FIG. 1 is a block diagram of the process of the present invention.
Fig. 2 is an uncompressed Lena grayscale image for testing.
Fig. 3 is an uncompressed Lena grayscale image modified by the method of the present invention.
Detailed Description
The following description will further describe a specific embodiment of the present invention with reference to the accompanying drawings.
As shown in FIG. 1, a method for preventing blind transmission based on optimized distortion modification includes a modification priority value calculation stage, a modification position determination stage, and a modification direction determination stage. This example takes the uncompressed Lena grayscale image of size 512 x 512 shown in fig. 2 as an example, which contains 262144 pixels. Let the ith pixel be denoted x i I ∈ {1,2, \ 8230;, 262144}. In the embodiment, 3 distortion cost functions of HILL, WOW and SUNIWARD are adopted to measure the distortion cost of each pixel, and the jth distortion cost function is assigned to x i Has a cost value ofI.e. 3 cost values per image element. The method comprises the following specific steps:
(1) 262144 cost values for assigning each distortion cost function to 262144 image pixels Are respectively limited between 0 and 1, as shown in formula (1). So that the assigned cost value ranges of the distortion cost functions are the same.
(2) For all image pixels { x } 1 ,x 2 ,…,x 262144 Calculate 3 costs for 3 distortion cost function assignments eachValue ofAverage value of (2)As shown in formula (2).
(3) Calculating the respective average cost values according to the formula (3)Reciprocal of (a) { theta } 1 ,θ 2 ,…,θ 262144 }。
(4) Will { theta } 1 ,θ 2 ,…,θ 262144 Are sorted from large to small, and the sorted value is { theta' 1 ,θ′ 2 ,…,θ′ 262144 H and { theta' 1 ,θ′ 2 ,…,θ′ 262144 The corresponding image pixels are { x' 1 ,x′ 2 ,…,x′ 262144 }. Since the modification ratio r to the uncompressed image pixel is set to 0.01, the modification ratio of the pixel in this example is 0.01, and the image pixel to be modified is { x' 1 ,x′ 2 ,…,x′ 2621 }。
(5) To { x' 1 ,x′ 2 ,…,x′ 2621 Calculating and calculating the predicted values according to the formula (5) respectively
(6) Calculating { x' 1 ,x′ 2 ,…,x′ 2621 The predicted value ofAfter that, { x' 1 ,x′ 2 ,…,x′ 2621 All the modified pixels are { x ″) 1 ,x″ 2 ,…,x″ 262144 And } this time, the modification of the image data is completed.
The existing steganographic method HILL and SUNIWARD are used for embedding secret data with embedding rate of 0.05bpp in the original uncompressed Lena image respectively, the image with the embedded data is modified by the method, and the error rate of extracting data in the modified image is close to 50 percent, namely random guess. It is shown that the invention can completely prevent the transmission of secret data.
In addition, the peak signal-to-noise ratio (PSNR) of the Lena image modified by the method of the present invention is 68.1dB, which shows that the distortion of the image caused by the method of the present invention is very small, and fig. 3 is the modified Lena image.

Claims (2)

1. A method for preventing hidden transmission based on optimized distortion modification is characterized in that extraction of secret data is damaged by slightly modifying image data, modification positions are concentrated in an area with minimum image distortion, and an optimal modification direction is selected to minimize the image distortion, so that the modified image still maintains high quality;
let the image to be modified contain n elements, the ith element being denoted x i Where i ∈ {1,2, \8230;, n }; w distortion cost functions can be used, and the jth distortion cost function is distributed to x i Has a cost value ofWhere j ∈ {1,2, \8230;, w }, i.e., each image element corresponds to w cost values;
the method comprises a priority value modification calculation stage, a position modification determination stage and a direction modification determination stage, and comprises the following specific steps:
(1) Assigning each distortion cost function to n cost values of n picture elementsThe ranges of the distortion cost functions are respectively limited between 0 and 1, as shown in formula (1), so that the ranges of the cost values distributed by the distortion cost functions are the same;
(2) For all picture elements { x 1 ,x 2 ,…,x n W cost values assigned by w distortion cost functions are calculated respectivelyAverage value of (2)As shown in formula (2):
(3) Calculating the respective average cost values according to the formula (3)Reciprocal of (a) { theta } 1 ,θ 2 ,…,θ n }；
(4) Will { theta } 1 ,θ 2 ,…,θ n Are sorted from large to small, and the sorted value is { theta' 1 ,θ′ 2 ,…,θ′ n H, and { theta' 1 ,θ′ 2 ,…,θ′ n Respective picture elements are { x' 1 ,x′ 2 ,…,x′ n }; let the modification ratio of the picture elements be r, where 0<r&1, the image element to be modified is { x' 1 ,x′ 2 ,…,x′ k Where k = round (r × n), round (·) denotes rounding; it is known from experience that: for an uncompressed image, the modification ratio r of the elements is set to 0.01; for a JPEG image, the modification ratio r of an element is calculated according to the formula (4), wherein QF is the quality factor of JPEG compression:
(5) To { x' 1 ,x′ 2 ,…,x′ k Calculating their predicted values, respectivelyThe calculation method is as follows:
let us say that the image is represented in two dimensions, the element x i Corresponds to x u,v U ∈ {1,2, \8230;, M }, v ∈ {1,2, \8230;, N }, where M and N are image sizes; calculating x for uncompressed images using equation (5) u,v Predicted value of (2)For JPEG image, x is calculated by equation (6) u,v Predicted value of (2)Elements beyond the edges of the image are obtained by means of symmetric filling; for example, if the element x u+1,v Beyond the image edge, use x u-1,v Instead of:
(6) Calculating { x' 1 ,x′ 2 ,…,x′ k The predicted value ofThereafter, { x' 1 ,x′ 2 ,…,x′ k }，
The modified element is { x ″) 1 ,x″ 2 ,…,x″ k And } this time, the modification of the image data is completed.
2. The method for modifying blocking concealed transmissions based on optimized distortion according to claim 1, wherein said image to be modified comprises elements in pixels for uncompressed images and DCT coefficients for JPEG images.
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