CN110517182B - Medical image zero watermark embedding method based on NSCT combined transformation - Google Patents

Medical image zero watermark embedding method based on NSCT combined transformation Download PDF

Info

Publication number
CN110517182B
CN110517182B CN201910809474.3A CN201910809474A CN110517182B CN 110517182 B CN110517182 B CN 110517182B CN 201910809474 A CN201910809474 A CN 201910809474A CN 110517182 B CN110517182 B CN 110517182B
Authority
CN
China
Prior art keywords
watermark
matrix
medical image
image
transform
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201910809474.3A
Other languages
Chinese (zh)
Other versions
CN110517182A (en
Inventor
李京兵
周经俊
黄梦醒
涂蓉
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hainan University
Original Assignee
Hainan University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hainan University filed Critical Hainan University
Priority to CN201910809474.3A priority Critical patent/CN110517182B/en
Publication of CN110517182A publication Critical patent/CN110517182A/en
Application granted granted Critical
Publication of CN110517182B publication Critical patent/CN110517182B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • G06T1/005—Robust watermarking, e.g. average attack or collusion attack resistant
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2201/00—General purpose image data processing
    • G06T2201/005—Image watermarking
    • G06T2201/0052—Embedding of the watermark in the frequency domain
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2201/00—General purpose image data processing
    • G06T2201/005—Image watermarking
    • G06T2201/0065—Extraction of an embedded watermark; Reliable detection

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Epidemiology (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Radiology & Medical Imaging (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Image Processing (AREA)
  • Editing Of Facsimile Originals (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

The scheme is realized based on mixed transformation and Arnold transformation of NSCT, RDWT and DCT, and in the watermark embedding process, firstly, more robust medical image feature vectors are extracted by combining NSCT, RDWT and DCT to resist geometric attack. And secondly, encrypting the watermark by utilizing Arnold transformation to enhance the safety of watermark information. Finally, the zero watermark technology is used for realizing watermark embedding, the integrity, the watermark capacity and the stealthiness of the medical image are ensured, the defects caused by the modification of the original image data by the traditional watermark embedding technology are avoided, and the quality of the medical image is ensured. Therefore, even if the patient information is attacked intentionally or unintentionally, the medical image and the watermark cannot be cracked as long as the unauthorized user does not know the secret key, so that the personal information of the patient is really protected.

Description

Medical image zero watermark embedding method based on NSCT combined transformation
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method, an apparatus, a device, and a readable storage medium for embedding a zero watermark in a medical image based on NSCT combinatorial transformation.
Background
In the biomedical field, a large amount of digital data including ultrasound, radiographs, CT, MRI, and the like are generated every day. Most of the data are stored and remotely communicated by using a PACS system, but the data are easy to steal or tamper in transmission and storage in a cloud environment, so that researchers have conducted intensive research on confidentiality, integrity and authenticity of medical images. Meanwhile, medical images contain a large amount of important information of patients, and face security threats when stored and transmitted in the internet. Therefore, there is an urgent need to take protective measures for medical images of patients.
In order to avoid data privacy during remote access, the data needs to be encrypted, and data analysis in a ciphertext domain is expected to be completed. The method can prevent unauthorized users from accessing related information by adopting a proper information hiding means, and currently, a plurality of technologies and methods for protecting medical images, such as digital watermarking, image steganography, cryptography and the like, exist.
For digital image watermarking, we generally need to be robust to geometric and conventional attacks. In the biomedical field, medical imaging data of a patient have extremely strict quality specifications, not allowing to store or transmit resulting in any visual quality variation. Furthermore, the extraction and embedding of the watermark should be fast and real-time so that it can be applied in telemedicine. There are also threats such as tampering, and it is necessary to consider security issues of storing these data in the cloud and transmitting them through the internet while ensuring unique features of medical images. Digital watermarking of medical images is therefore of particular importance.
Currently, diagnostic information of a patient may be hidden in a medical image according to the patient's particular disease state to protect the patient's privacy from theft. For example, personal information is embedded in a medical image as a digital watermark in the medical image, so that the problem of information hiding of a patient can be solved well. The traditional method is to directly embed the scrambled watermark into the original image, so that watermark information can be protected, but the potential safety hazard of carrier information leakage when the medical image is subjected to unauthorized access cannot be eliminated.
Therefore, how to avoid leakage of medical image information and watermark information and improve robustness is a problem to be solved urgently by technical personnel in the field.
Disclosure of Invention
The application aims to provide a medical image zero-watermark embedding method, device and equipment based on NSCT combined transformation and a readable storage medium, which are used for solving the problems that the traditional watermark embedding scheme cannot avoid leakage of medical image information and watermark information and is poor in robustness.
The specific scheme is as follows:
in a first aspect, the present application provides a medical image zero watermark embedding method based on NSCT combined transform, including:
acquiring an original medical image and a binary watermark image generated according to patient information;
respectively carrying out NSCT (non-subsampled transform), RDWT (weighted round-robin) transform and DCT (discrete cosine transform) transform on the pixel gray value matrix of the original medical image to obtain a coefficient matrix; extracting binary symbols of a preset number of middle and low frequency coefficients in the coefficient matrix to serve as a characteristic vector matrix of the original medical image;
performing Arnold transformation on the pixel gray value matrix of the binary watermark image to obtain an encrypted watermark information matrix;
and carrying out XOR operation on the characteristic vector matrix and the encrypted watermark information matrix to obtain a logic key matrix so as to realize zero-watermark embedding.
Preferably, after the performing an exclusive or operation on the feature vector matrix and the encrypted watermark information matrix to obtain a logical key matrix, the method further includes:
determining a medical image to be tested with a zero watermark according to the zero watermark extraction request;
respectively carrying out NSCT (non-subsampled transform), RDWT (weighted round-robin) transform and DCT (discrete cosine transform) transform on the medical image to be tested to obtain a coefficient matrix to be tested; extracting the binary symbols of the preset number of middle and low frequency coefficients in the coefficient matrix to be detected to serve as a characteristic vector matrix of the medical image to be detected;
and carrying out exclusive OR operation on the characteristic vector matrix of the medical image to be detected and the logic key to obtain an encrypted watermark information matrix in the medical image to be detected.
Preferably, the performing Arnold transformation on the pixel gray value matrix of the binary watermark image to obtain an encrypted watermark information matrix includes:
initializing an encryption coefficient matrix of Arnold transformation;
and performing encryption scrambling operation on the pixel gray value matrix of the binary watermark image for preset times according to the encryption coefficient matrix and the size of the binary watermark image to obtain an encrypted watermark information matrix.
Preferably, after the performing an exclusive or operation on the feature vector matrix of the medical image to be detected and the logic key to obtain an encrypted watermark information matrix in the medical image to be detected, the method further includes:
acquiring the encryption coefficient matrix in the initialization process according to the zero watermark restoration request;
and performing Arnold inverse transformation on the encrypted watermark information matrix according to the encryption coefficient matrix, the size of the binary watermark image and the preset times to obtain the original binary watermark image.
Preferably, the extracting a preset number of binary symbols of the medium and low frequency coefficients in the coefficient matrix to serve as a feature vector matrix of the original medical image includes:
extracting 4 x 8 medium and low frequency coefficients in the coefficient matrix to obtain a 4 x 8 coefficient matrix; converting the 4 x 8 coefficient matrix into a 1 x 32 coefficient matrix, and performing row dimension ascending operation to obtain a 32 x 32 coefficient matrix;
and assigning the middle and low frequency coefficient which is greater than or equal to 0 in the 32 x 32 coefficient matrix as 1, and assigning the middle and low frequency coefficient which is less than 0 as 0 to obtain the feature vector matrix of the original medical image.
In a second aspect, the present application provides a medical image zero watermark embedding apparatus based on NSCT combinatorial transformation, including:
an original image acquisition module: the system comprises a processor, a memory, a processor, a memory and a display, wherein the processor is used for acquiring an original medical image and a binary watermark image generated according to patient information;
an original feature extraction module: the pixel gray value matrix of the original medical image is subjected to NSCT (non-subsampled Contourlet transform), RDWT (weighted dispersive weight transfer) transform and DCT (discrete cosine transform) transform respectively to obtain a coefficient matrix; extracting binary symbols of a preset number of middle and low frequency coefficients in the coefficient matrix to serve as a characteristic vector matrix of the original medical image;
a watermark encryption module: the pixel gray value matrix of the binary watermark image is subjected to Arnold transformation to obtain an encrypted watermark information matrix;
a watermark embedding module: and the logic key matrix is used for carrying out XOR operation on the characteristic vector matrix and the encrypted watermark information matrix to obtain a logic key matrix so as to realize zero watermark embedding.
Preferably, the method further comprises the following steps:
the image to be detected determining module: the system comprises a zero watermark extraction request module, a zero watermark detection module and a zero watermark detection module, wherein the zero watermark extraction request module is used for determining a medical image to be detected with a zero watermark according to the zero watermark extraction request;
the to-be-detected feature extraction module: the system is used for respectively carrying out NSCT (non-subsampled Contourlet transform), RDWT (RDWT transform) and DCT (discrete cosine transform) on the medical image to be detected to obtain a coefficient matrix to be detected; extracting the binary symbols of the preset number of middle and low frequency coefficients in the coefficient matrix to be detected to serve as a characteristic vector matrix of the medical image to be detected;
a watermark extraction module: and the logic key is used for carrying out XOR operation on the characteristic vector matrix of the medical image to be detected and the logic key to obtain the encrypted watermark information matrix in the medical image to be detected.
Preferably, the watermark encryption module is specifically configured to:
initializing an encryption coefficient matrix of Arnold transformation; and performing encryption scrambling operation on the pixel gray value matrix of the binary watermark image for preset times according to the encryption coefficient matrix and the size of the binary watermark image to obtain an encrypted watermark information matrix.
In a third aspect, the present application provides a medical image zero watermark embedding device based on NSCT combined transform, including:
a memory: for storing a computer program;
a processor: for executing the computer program to implement the steps of a NSCT combinatorial transformation-based medical image zero watermark embedding method as described above.
In a fourth aspect, the present application provides a readable storage medium, on which a computer program is stored, which, when being executed by a processor, is configured to implement the steps of a NSCT combinatorial transformation-based medical image zero watermark embedding method as described above.
The application provides a medical image zero watermark embedding method, device, equipment and readable storage medium based on NSCT combined transformation, and the scheme comprises the following steps: acquiring an original medical image and a binary watermark image generated according to patient information; respectively carrying out NSCT (non-subsampled transform), RDWT (weighted round-robin) transform and DCT (discrete cosine transform) transform on a pixel gray value matrix of an original medical image to obtain a coefficient matrix; extracting binary symbols of a preset number of middle and low frequency coefficients in the coefficient matrix to serve as a characteristic vector matrix of the original medical image; performing Arnold transformation on a pixel gray value matrix of the binary watermark image to obtain an encrypted watermark information matrix; and carrying out XOR operation on the characteristic vector matrix and the encrypted watermark information matrix to obtain a logic key matrix so as to realize zero watermark embedding.
Therefore, the scheme is realized based on mixed transformation and Arnold transformation of NSCT, RDWT and DCT, and in the watermark embedding process, firstly, more robust medical image feature vectors are extracted by combining NSCT, RDWT and DCT to resist geometric attack. And secondly, encrypting the watermark by utilizing Arnold transformation to enhance the safety of watermark information. Finally, the zero watermark technology is used for realizing watermark embedding, the integrity, the watermark capacity and the stealthiness of the medical image are ensured, the defects caused by the modification of the original image data by the traditional watermark embedding technology are avoided, and the quality of the medical image is ensured. Therefore, even if the patient information is attacked intentionally or unintentionally, the medical image and the watermark cannot be cracked as long as the unauthorized user does not know the secret key, so that the personal information of the patient is really protected.
Drawings
For a clearer explanation of the embodiments or technical solutions of the prior art of the present application, the drawings needed for the description of the embodiments or prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a flowchart of a first implementation of a medical image zero-watermark embedding method based on NSCT combinatorial transformation according to an embodiment of the present application;
fig. 2 is a schematic diagram of an NSP principle of a first embodiment of a medical image zero-watermark embedding method based on NSCT combinatorial transformation according to the present application;
fig. 3 is a schematic diagram of an NSDFB principle of a first embodiment of a medical image zero-watermark embedding method based on NSCT combinatorial transformation according to the present application;
fig. 4 is a schematic diagram of RDWT transform of a first embodiment of a medical image zero-watermark embedding method based on NSCT combinatorial transform according to the present application;
fig. 5 is a flowchart illustrating an implementation of a second embodiment of a medical image zero-watermark embedding method based on NSCT combinatorial transformation according to the present application;
fig. 6 is a functional block diagram of an embodiment of a medical image zero-watermark embedding apparatus based on NSCT combinatorial transformation according to the present application.
Detailed Description
The core of the application is to provide a medical image zero-watermark embedding method, a device, equipment and a readable storage medium based on NSCT combined transformation, and the method combines the characteristic vector of the encrypted medical image, cryptography and zero-watermark technology, so that the defect that the traditional digital watermark method cannot protect the medical image per se is overcome, the method has strong robustness and invisibility, and can protect the privacy information of a patient and the data security of the medical image at the same time.
In order that those skilled in the art will better understand the disclosure, the following detailed description will be given with reference to the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present application and not all 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 application.
Referring to fig. 1, a first embodiment of a medical image zero-watermark embedding method based on NSCT combinatorial transformation provided by the present application is described below, where the first embodiment includes:
s101, acquiring an original medical image and a binary watermark image generated according to patient information;
s102, respectively carrying out NSCT (non-subsampled Contourlet transform), RDWT (RDWT transform) and DCT (discrete cosine transform) on the pixel gray value matrix of the original medical image to obtain a coefficient matrix; extracting binary symbols of a preset number of middle and low frequency coefficients in the coefficient matrix to serve as a characteristic vector matrix of the original medical image;
s103, performing Arnold transformation on the pixel gray value matrix of the binary watermark image to obtain an encrypted watermark information matrix;
s104, carrying out XOR operation on the characteristic vector matrix and the encrypted watermark information matrix to obtain a logic key matrix so as to realize zero watermark embedding.
The original medical image may be an ultrasound, an X-ray, a CT, an MRI, or the like.
As described above, the present embodiment is implemented based on NSCT transform, RDWT transform, DCT transform, and Arnold transform, and four kinds of transforms are described below:
the NSCT transform, i.e., the non-downsampled contour transform, is a transform based on a non-downsampled pyramid (NSP) and a non-downsampled directional filter (NSDFB). NSP reduces the sampling distortion of the filter by multi-scale decomposition of the image and eliminates up-sampling and down-sampling, ensuring translational invariance, e.g., fig. 2 is divided into two levels. The NSDFB decomposes the signal into two circular band sub-band filters at a time, and the size of each sub-image after proportional decomposition is constant, so that more details can be retained and the method is effective to reduce distortion of the filter and obtain translation invariance, for example, fig. 3 is a two-scale division diagram. That is, NSP decomposes an image into high and low frequencies to ensure multi-scale, NSDFB decomposes a high frequency subband into multiple subbands, subbands in different directions ensure multi-directivity, and a low frequency part continues to be decomposed according to a pyramid model, so that NSCT has good translational invariance and can well resist geometric attack.
RDWT transform, i.e. redundant discrete wavelet transform, which ensures translational invariance by eliminating downsampling, but since the output image size remains the same, it creates a lot of storage space and stores more features for the next stage, the following is the forward transform of RDWT:
Figure BDA0002184605540000071
wherein, h [ -k ]]And g [ -k ]]Low-pass and high-pass filters representing forward transformation by RDWT; c. CjAnd djIs the j-th coefficient after forward transform. As shown in fig. 4, the LL portion signal features extracted after the RDWT forward transform, the other three portions give details or differences of the signal, and the LL subband is passed to the RDWT transform.
DCT transform, i.e., discrete cosine transform, is one of various digital transform methods for transforming a spatial domain image into a frequency domain for analysis, and is widely used for data or image compression. The operating principle of the DCT is to reduce medium-high frequency components in the image, which correspond to detail information in the image, such as texture edges, and the like, and which are not sensitive to the human visual system, only the low frequency part needs to be retained, and the low frequency part focuses on a large amount of energy of the image to improve the robustness and the compressive resistance of the watermarking algorithm, and the DCT formula is as follows:
Figure BDA0002184605540000081
where i and j are the sampling values of the image space domain, u and v are the sampling values of the image transform domain, and N is the length and width of the image. In digital image processing, the image is usually square, and if not, the image is usually padded to perform a discrete cosine transform.
Arnold transform, Arnold Cat Map. Chaos is a seemingly random motion, which refers to a stochastic-like process that occurs in a deterministic system. Therefore, with its initial values and parameters, the chaotic system can be generated. The most well-known chaotic system is Arnold transformation, and the formulas of Arnold forward transformation and inverse transformation are respectively as follows:
Figure BDA0002184605540000082
Figure BDA0002184605540000083
wherein (x)n,yn) Is the pixel coordinate of the original watermark image, (x)n+1,yn+1) Is the pixel coordinate of the watermark image after N iterations, a, b, c, d are positive integers to ensure that the mapping is a one-to-one mapping, and N is the width or height of the watermark image. Since the Arnold transform has periodicity, i.e., if position (x)n+1,yn+1) By T iterative transformations, it will return to the original position. T is the conversion period, i.e. the number of iterations, and depends on parameters a, b, c, d which can be used as keys, since the correlation of the pixel changes each time these values change, i.e. produce a completely different effect. From the analysis of the periodicity of the discrete Arnold transform by the relevant scholars, the conclusion is that for any N>2, the period of the Arnold transform is T ≦ N2/2, which is the best result so far.
As described above, when extracting the feature vector of the original medical image, the present embodiment extracts a preset number of binary symbols of the middle and low frequency coefficients in the coefficient matrix obtained through NSCT transform, RDWT transform, and DCT transform as the feature vector matrix of the original medical image, and the extraction method is as follows:
at present, the main reasons of poor geometric attack resistance of most medical image watermarking algorithms are as follows: the slight geometric transformation of medical images often results in large changes in pixel or transform coefficient values, which makes the embedded watermark easily attacked. If a feature vector reflecting the geometric characteristics of the medical image can be found, the feature value of the image basically does not have obvious mutation when the image has small geometric transformation. Through the observation of the coefficient matrix of a large number of medical images, the embodiment finds that when the medical image feature extraction is performed in the above manner, when a common geometric transformation is performed on a medical image to extract features, the magnitude of the low-medium frequency coefficient may change slightly, but the sign of the coefficient remains substantially unchanged. According to the visual characteristics of human, the low-intermediate frequency signals have a large influence on human vision and represent the main features of the image, so the low-intermediate frequency coefficient symbol sequence of the medical image selected in this embodiment is used as the feature vector matrix of the original medical image.
In the medical image zero watermark embedding method based on NSCT combined transformation provided by the embodiment, a coefficient matrix obtained by extracting an original medical image through NSCT, RDWT and DCT transformation is analyzed, and a binary symbol sequence of a low-frequency coefficient using the coefficient matrix can be used as a feature vector of a medical image, so that the geometric attack resistance is improved; the Arnold is used for encrypting the watermark image, so that the security of watermark information is improved; meanwhile, the zero watermark concept is combined, the embedding of watermark information is realized, the integrity, the watermark capacity and the invisibility of the medical image are ensured, an interested area does not need to be selected in advance, and the method can be used in a remote environment. Therefore, even if the patient information is attacked intentionally or unintentionally, the medical image and the watermark cannot be cracked as long as the unauthorized user does not know the secret key, so that the personal information of the patient is really protected.
An embodiment two of a medical image zero watermark embedding method based on NSCT combinatorial transformation provided by the present application is described in detail below, and referring to fig. 5, the embodiment two specifically includes:
s501, acquiring an original medical image and a binary watermark image generated according to patient information;
specifically, a meaningful binary text image is selected as a binary watermark image embedded in the medical image and is marked as WnW (i, j) | w (i, j) ═ 0, 1; }. Meanwhile, the tenth piece of medical volume data is selected as an original medical image, and is marked as I (I, j), wherein WnAnd (I, j) and I (I, j) respectively represent pixel gray values of the binary watermark image and the original medical image.
S502, respectively carrying out NSCT (non-subsampled Contourlet transform), RDWT (RDWT transform) and DCT (discrete cosine transform) on the pixel gray value matrix of the original medical image to obtain a coefficient matrix; extracting binary symbols of 4 x 8 medium and low frequency coefficients in the coefficient matrix to serve as a feature vector matrix of the original medical image;
specifically, the original medical image EI (i, j) is subjected to full-map NSCT-RDWT-DCT hybrid transform to obtain a coefficient matrix D (i, j), which is denoted as D (i, j) ═ HT (EI (i, j)).
Specifically, extracting 4 × 8 medium and low frequency coefficients in the coefficient matrix to obtain a 4 × 8 coefficient matrix; converting the 4 x 8 coefficient matrix into a 1 x 32 coefficient matrix, and performing row dimension ascending operation to obtain a 32 x 32 two-dimensional coefficient matrix; and assigning the middle and low frequency coefficients which are more than or equal to 0 in the 32 x 32 two-dimensional coefficient matrix as '1', and assigning the middle and low frequency coefficients which are less than 0 as '0', so as to obtain the two-dimensional characteristic vector matrix of the original medical image. That is, 4 × 8 feature coefficients are taken for D (i, j), and then converted into a 1 × 32 coefficient matrix, and then a 32 × 32 two-dimensional matrix is obtained by row-wise upscaling, and numbers greater than or equal to 0 are assigned as "1", and the rest are assigned as "0", so as to obtain a binary matrix C (i, j) as a feature vector matrix of the original medical image.
S503, initializing an encryption coefficient matrix of Arnold transformation; according to the encryption coefficient matrix and the size of the binary watermark image, carrying out encryption scrambling operation on the pixel gray value matrix of the binary watermark image for preset times to obtain an encrypted watermark information matrix;
firstly, scrambling binary encryption matrix coefficients a, b, c and d according to an initialized Arnold Cat Map; the encryption matrix is then combined with the digital watermark W as described in the formulation of the Arnold forward transformn(i, j) performing inner product operation, and combining mod (N) to obtain the encrypted watermark information matrix BWn(i, j). Where N is the length or width of the binary watermark image, but the digital watermark image is usually square, and thus the length and width are equal, thus completing the encryption of the watermark.
S504, carrying out XOR operation on the characteristic vector matrix and the encrypted watermark information matrix to obtain a logic key matrix so as to realize zero watermark embedding;
encrypted watermark information matrix BWn(i, j) aiming at embedding the watermark and obtaining the logical key. Specifically, a feature vector matrix C(i, j) and encrypted watermark information matrix BWn(i, j) performing exclusive or operation bit by bit, so as to embed the watermark into the encrypted image, and obtaining a 32 × 32 logical Key matrix Key (i, j), that is:
Key(i,j)＝C(i,j)⊕BWn(i，j)；
and storing Key (i, j), which is used for later watermark extraction. The Key (i, j) is used as a Key to apply to a third party, so that ownership and use right of the original medical image can be obtained, and the purpose of protecting the medical image is achieved.
S505, determining a medical image to be tested with a zero watermark according to the zero watermark extraction request;
s506, respectively carrying out NSCT (non-subsampled Contourlet transform), RDWT (RDWT transform) and DCT (discrete cosine transform) on the medical image to be tested to obtain a coefficient matrix to be tested; extracting the binary symbols of the preset number of middle and low frequency coefficients in the coefficient matrix to be detected to serve as a characteristic vector matrix of the medical image to be detected;
firstly, the feature vector of the medical image EI' (i, j) to be measured is extracted, and is a coefficient matrix different from the coefficient matrix of the original image. Performing NSCT-RDWT-DCT mixed transformation on the medical image EI' (i, j) to be detected to obtain a coefficient matrix to be detected; 4 × 8 feature coefficients are extracted from D '(i, j), then the feature coefficients are converted into a coefficient matrix of 1 × 32, then a two-dimensional matrix of 32 × 32 is obtained through row dimension increasing operation, perceptual hash operation is carried out, the number larger than or equal to 0 is assigned as' 1 ', and the rest are assigned as' 0 ', so that a binary matrix C' (i, j), namely a feature vector matrix of the medical image to be detected, is obtained. The procedure is as follows:
D'(i,j)＝HT(EI'(i,j))
C'(i,j)＝pHash(D'(i,j))
s507, carrying out XOR operation on the characteristic vector matrix of the medical image to be detected and the logic key to obtain an encrypted watermark information matrix in the medical image to be detected;
when the watermark is extracted, only the logic Key Key (i, j) is needed, and the original image is not needed, so that the zero watermark extraction algorithm is realized. Specifically, an exclusive or operation is performed on a feature vector matrix C '(i, j) of the encrypted image to be detected and a logic Key (i, j), so as to extract an encrypted watermark information matrix BW' n (i, j):
BW'n(i,j)＝Key(i,j)⊕C'(i,j)
s508, acquiring the encryption coefficient matrix in the initialization process according to the zero watermark restoration request;
s509, performing Arnold inverse transformation on the encrypted watermark information matrix according to the encryption coefficient matrix, the size of the binary watermark image and the preset times to obtain an original binary watermark image.
Acquiring a two-dimensional encryption matrix of initialized Arnold Cat Map scrambled binary encryption matrix coefficients a, b, c and d; and then, performing operation inner product on the two-dimensional encryption matrix and the extracted encryption watermark BW 'N (i, j), and then performing mod (N) to restore the watermark image W' N (i, j), wherein N is the length or the width of the digital watermark image, but the digital watermark image is usually square, so the length and the width are equal, and the watermark restoration is completed.
In order to prove the conventional attack resistance and the geometric attack resistance of the watermark of the embodiment, simulation experiments are performed to realize verification, which specifically comprises the following steps:
1. conventional attack resistance capability of watermark
(1) Adding Gaussian noise
The watermark image with 1% Gaussian noise intensity is blurred visually; for an extracted watermark whose average NC value is equal to 0.89, the presence of the watermark can be clearly detected. Table 1 shows the detection data of the watermark against gaussian interference. From experimental data, it can be seen that when the gaussian noise strength is as high as 25%, the PSNR of the image is reduced to 11.4095dB, and the extracted watermark correlation coefficient NC is 0.80, the presence of the watermark can still be detected. This shows that the embodiment has good gaussian noise resistance.
TABLE 1
Figure BDA0002184605540000121
(2) JPEG compression processing
JPEG compression is carried out on the watermark image by adopting the image compression strength percentage as a parameter; for an image with 30% compression strength, the image has a blocking effect; for the extracted watermark, the average NC value is equal to 1.00. Table 2 shows experimental data of the watermark image against JPEG compression. When the compression quality is 4%, the existence of the watermark can still be detected, and NC is 0.89, which shows that the embodiment has strong capability of resisting JPEG compression.
TABLE 2
Figure BDA0002184605540000131
(3) Median filtering process
For a medical image with a median filtering parameter of [3x3] and filtering times of 20 times, the image has blurs; for the extracted watermark, the average NC value is equal to 0.80, and the detection effect is obvious. Table 3 shows the median filtered data of the watermark image, and it can be seen that when the median filter parameter is [7x7] and the number of filtering repetitions is 20, the presence of the watermark can still be detected, and the average NC value is equal to 0.64.
TABLE 3
Figure BDA0002184605540000132
2. The watermark has geometric attack resistance:
(1) rotational transformation
For a medical image with a watermark image rotated by 10 degrees, the PSNR is 22.7324dB, and the signal-to-noise ratio is low; for an extracted watermark, the presence of the watermark is clearly detected, with an average NC value equal to 0.80. Table 4 shows experimental data of watermark anti-rotation attack. It can be seen from the table that when the watermark image is rotated by 40 deg., the average NC value is equal to 0.57, and the presence of the watermark can still be detected.
TABLE 4
Figure BDA0002184605540000133
Figure BDA0002184605540000141
(2) Scaling transform
For the watermark image with the scaling factor of 0.3, the central image is smaller than the original image; for extracted watermarks, the average NC value is equal to 0.80, the presence of the watermark can be clearly detected. Table 5 shows experimental data of the watermark resisting the scaling attack, and it can be seen from table 5 that when the scaling factor is as large as 4, the average NC value is equal to 1.00, and the watermark can still be measured, which shows that the embodiment has stronger scaling resistance.
TABLE 5
Figure BDA0002184605540000142
(3) Translation transformation
For the watermark image vertically shifted down by 4%, when the PSNR is 21.8269dB, the signal-to-noise ratio is very low; for extracted watermarks, the average NC value is equal to 0.80, the presence of the watermark can be clearly detected. Table 6 shows experimental data of watermark anti-translation transformation. It is known from the table that when the vertical shift is shifted down by 15%, the presence of the watermark can still be detected, so that the embodiment has strong anti-translation capability.
TABLE 6
Figure BDA0002184605540000143
(4) Shear attack
For the condition that the watermark image is cut by 3% in the Y-axis direction, a part of the bottom part is cut off relative to the original medical image; the watermark extracted at this time has an average NC value equal to 1.00, and the presence of the watermark can be detected significantly. Table 7 shows experimental data of the watermark against the shearing attack, and as can be seen from the experimental data in the table, this embodiment has a strong shearing resistance.
TABLE 7
Figure BDA0002184605540000151
Through the above experiments, the medical image zero-watermark embedding method based on NSCT combined transformation provided by the embodiment has strong capability of resisting conventional attacks and geometric attacks, and the embedding of the watermark does not affect the original medical image.
In the following, a medical image zero-watermark embedding apparatus based on NSCT combined transform provided by an embodiment of the present application is introduced, and a medical image zero-watermark embedding apparatus based on NSCT combined transform described below and a medical image zero-watermark embedding method based on NSCT combined transform described above may be referred to correspondingly.
As shown in fig. 6, the apparatus includes:
the original image acquisition module 601: the system comprises a processor, a memory, a processor, a memory and a display, wherein the processor is used for acquiring an original medical image and a binary watermark image generated according to patient information;
raw feature extraction module 602: the pixel gray value matrix of the original medical image is subjected to NSCT (non-subsampled Contourlet transform), RDWT (weighted dispersive weight transfer) transform and DCT (discrete cosine transform) transform respectively to obtain a coefficient matrix; extracting binary symbols of a preset number of middle and low frequency coefficients in the coefficient matrix to serve as a characteristic vector matrix of the original medical image;
the watermark encryption module 603: the pixel gray value matrix of the binary watermark image is subjected to Arnold transformation to obtain an encrypted watermark information matrix;
the watermark embedding module 604: and the logic key matrix is used for carrying out XOR operation on the characteristic vector matrix and the encrypted watermark information matrix to obtain a logic key matrix so as to realize zero watermark embedding.
In some specific embodiments, the method further comprises:
the image to be detected determining module: the system comprises a zero watermark extraction request module, a zero watermark detection module and a zero watermark detection module, wherein the zero watermark extraction request module is used for determining a medical image to be detected with a zero watermark according to the zero watermark extraction request;
the to-be-detected feature extraction module: the system is used for respectively carrying out NSCT (non-subsampled Contourlet transform), RDWT (RDWT transform) and DCT (discrete cosine transform) on the medical image to be detected to obtain a coefficient matrix to be detected; extracting the binary symbols of the preset number of middle and low frequency coefficients in the coefficient matrix to be detected to serve as a characteristic vector matrix of the medical image to be detected;
a watermark extraction module: and the logic key is used for carrying out XOR operation on the characteristic vector matrix of the medical image to be detected and the logic key to obtain the encrypted watermark information matrix in the medical image to be detected.
In some specific embodiments, the watermark encryption module is specifically configured to:
initializing an encryption coefficient matrix of Arnold transformation; and performing encryption scrambling operation on the pixel gray value matrix of the binary watermark image for preset times according to the encryption coefficient matrix and the size of the binary watermark image to obtain an encrypted watermark information matrix.
The medical image zero watermark embedding device based on NSCT combined transformation of the present embodiment is used for implementing the aforementioned medical image zero watermark embedding method based on NSCT combined transformation, and therefore, the specific implementation of the device can be found in the foregoing embodiment of a medical image zero watermark embedding method based on NSCT combined transformation, and therefore, the specific implementation thereof can refer to the description of the corresponding embodiments of the respective portions, and will not be further described herein.
In addition, since the medical image zero-watermark embedding apparatus based on NSCT combined transform of this embodiment is used to implement the aforementioned medical image zero-watermark embedding method based on NSCT combined transform, its role corresponds to that of the above method, and is not described herein again.
In addition, the application also provides a medical image zero-watermark embedding device based on NSCT combined transformation, which comprises:
a memory: for storing a computer program;
a processor: for executing the computer program to implement the steps of a NSCT combinatorial transformation-based medical image zero watermark embedding method as described above.
Finally, the present application provides a readable storage medium having stored thereon a computer program for implementing the steps of a method for zero watermark embedding of a medical image based on NSCT combinatorial transformation as described above when the computer program is executed by a processor.
The embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above detailed descriptions of the solutions provided in the present application, and the specific examples applied herein are set forth to explain the principles and implementations of the present application, and the above descriptions of the examples are only used to help understand the method and its core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.

Claims (9)

1. A medical image zero watermark embedding method based on NSCT combined transformation is characterized by comprising the following steps:
acquiring an original medical image and a binary watermark image generated according to patient information;
respectively carrying out NSCT (non-subsampled transform), RDWT (weighted round-robin) transform and DCT (discrete cosine transform) transform on the pixel gray value matrix of the original medical image to obtain a coefficient matrix; extracting binary symbols of a preset number of middle and low frequency coefficients in the coefficient matrix to serve as a characteristic vector matrix of the original medical image; the extracting of the binary symbols of the preset number of middle and low frequency coefficients in the coefficient matrix as the feature vector matrix of the original medical image includes:
extracting 4 x 8 medium and low frequency coefficients in the coefficient matrix to obtain a 4 x 8 coefficient matrix; converting the 4 x 8 coefficient matrix into a 1 x 32 coefficient matrix, and performing row dimension ascending operation to obtain a 32 x 32 coefficient matrix;
assigning the middle and low frequency coefficients which are greater than or equal to 0 in the 32 x 32 coefficient matrix to be 1, and assigning the middle and low frequency coefficients which are less than 0 to be 0 to obtain a feature vector matrix of the original medical image;
performing Arnold transformation on the pixel gray value matrix of the binary watermark image to obtain an encrypted watermark information matrix;
and carrying out XOR operation on the characteristic vector matrix and the encrypted watermark information matrix to obtain a logic key matrix so as to realize zero-watermark embedding.
2. The method of claim 1, wherein after the exclusive-or operation is performed on the eigenvector matrix and the encrypted watermark information matrix to obtain a logical key matrix, the method further comprises:
determining a medical image to be tested with a zero watermark according to the zero watermark extraction request;
respectively carrying out NSCT (non-subsampled transform), RDWT (weighted round-robin) transform and DCT (discrete cosine transform) transform on the medical image to be tested to obtain a coefficient matrix to be tested; extracting the binary symbols of the preset number of middle and low frequency coefficients in the coefficient matrix to be detected to serve as a characteristic vector matrix of the medical image to be detected;
and carrying out exclusive OR operation on the characteristic vector matrix of the medical image to be detected and the logic key to obtain an encrypted watermark information matrix in the medical image to be detected.
3. The method as claimed in claim 2, wherein performing Arnold transformation on the pixel gray value matrix of the binary watermark image to obtain an encrypted watermark information matrix comprises:
initializing an encryption coefficient matrix of Arnold transformation;
and performing encryption scrambling operation on the pixel gray value matrix of the binary watermark image for preset times according to the encryption coefficient matrix and the size of the binary watermark image to obtain an encrypted watermark information matrix.
4. The method as claimed in claim 3, wherein after the exclusive-or operation is performed on the feature vector matrix of the medical image to be tested and the logical key to obtain the encrypted watermark information matrix in the medical image to be tested, the method further comprises:
acquiring the encryption coefficient matrix in the initialization process according to the zero watermark restoration request;
and performing Arnold inverse transformation on the encrypted watermark information matrix according to the encryption coefficient matrix, the size of the binary watermark image and the preset times to obtain the original binary watermark image.
5. A medical image zero watermark embedding device based on NSCT combined transformation is characterized by comprising:
an original image acquisition module: the system comprises a processor, a memory, a processor, a memory and a display, wherein the processor is used for acquiring an original medical image and a binary watermark image generated according to patient information;
an original feature extraction module: the pixel gray value matrix of the original medical image is subjected to NSCT (non-subsampled Contourlet transform), RDWT (weighted dispersive weight transfer) transform and DCT (discrete cosine transform) transform respectively to obtain a coefficient matrix; extracting binary symbols of a preset number of middle and low frequency coefficients in the coefficient matrix to serve as a characteristic vector matrix of the original medical image; the extracting of the binary symbols of the preset number of middle and low frequency coefficients in the coefficient matrix as the feature vector matrix of the original medical image includes:
extracting 4 x 8 medium and low frequency coefficients in the coefficient matrix to obtain a 4 x 8 coefficient matrix; converting the 4 x 8 coefficient matrix into a 1 x 32 coefficient matrix, and performing row dimension ascending operation to obtain a 32 x 32 coefficient matrix;
assigning the middle and low frequency coefficients which are greater than or equal to 0 in the 32 x 32 coefficient matrix to be 1, and assigning the middle and low frequency coefficients which are less than 0 to be 0 to obtain a feature vector matrix of the original medical image;
a watermark encryption module: the pixel gray value matrix of the binary watermark image is subjected to Arnold transformation to obtain an encrypted watermark information matrix;
a watermark embedding module: and the logic key matrix is used for carrying out XOR operation on the characteristic vector matrix and the encrypted watermark information matrix to obtain a logic key matrix so as to realize zero watermark embedding.
6. The apparatus of claim 5, further comprising:
the image to be detected determining module: the system comprises a zero watermark extraction request module, a zero watermark detection module and a zero watermark detection module, wherein the zero watermark extraction request module is used for determining a medical image to be detected with a zero watermark according to the zero watermark extraction request;
the to-be-detected feature extraction module: the system is used for respectively carrying out NSCT (non-subsampled Contourlet transform), RDWT (RDWT transform) and DCT (discrete cosine transform) on the medical image to be detected to obtain a coefficient matrix to be detected; extracting the binary symbols of the preset number of middle and low frequency coefficients in the coefficient matrix to be detected to serve as a characteristic vector matrix of the medical image to be detected;
a watermark extraction module: and the logic key is used for carrying out XOR operation on the characteristic vector matrix of the medical image to be detected and the logic key to obtain the encrypted watermark information matrix in the medical image to be detected.
7. The apparatus as recited in claim 6, wherein said watermark encryption module is specifically configured to:
initializing an encryption coefficient matrix of Arnold transformation; and performing encryption scrambling operation on the pixel gray value matrix of the binary watermark image for preset times according to the encryption coefficient matrix and the size of the binary watermark image to obtain an encrypted watermark information matrix.
8. A medical image zero watermark embedding device based on NSCT combined transform, comprising:
a memory: for storing a computer program;
a processor: for executing the computer program for implementing the steps of a NSCT combinatorial transform-based medical image zero watermark embedding method according to any of claims 1 to 4.
9. A readable storage medium, having stored thereon a computer program for implementing the steps of a NSCT combinatorial transformation-based medical image zero watermark embedding method according to any of claims 1-4 when being executed by a processor.
CN201910809474.3A 2019-08-29 2019-08-29 Medical image zero watermark embedding method based on NSCT combined transformation Active CN110517182B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910809474.3A CN110517182B (en) 2019-08-29 2019-08-29 Medical image zero watermark embedding method based on NSCT combined transformation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910809474.3A CN110517182B (en) 2019-08-29 2019-08-29 Medical image zero watermark embedding method based on NSCT combined transformation

Publications (2)

Publication Number Publication Date
CN110517182A CN110517182A (en) 2019-11-29
CN110517182B true CN110517182B (en) 2021-06-25

Family

ID=68629071

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910809474.3A Active CN110517182B (en) 2019-08-29 2019-08-29 Medical image zero watermark embedding method based on NSCT combined transformation

Country Status (1)

Country Link
CN (1) CN110517182B (en)

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111224771B (en) * 2020-01-13 2021-09-21 燕山大学 Management coding encryption and decryption method based on principal component analysis and Henon mapping
CN111738899B (en) * 2020-06-23 2023-12-22 抖音视界有限公司 Method, apparatus, device and computer readable medium for generating watermark
CN113160029B (en) * 2021-03-31 2022-07-05 海南大学 Medical image digital watermarking method based on perceptual hashing and data enhancement
CN113744111B (en) * 2021-08-13 2024-03-19 三维通信股份有限公司 Watermark embedding method and device, watermark extracting method and device
CN115695988A (en) * 2022-09-09 2023-02-03 天翼数字生活科技有限公司 Intelligent equipment true random key generation method, device, terminal and medium
CN115879126A (en) * 2022-12-13 2023-03-31 安徽大学 Medical information sharing method based on secure cloud storage, electronic device and storage medium

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101662686A (en) * 2009-09-07 2010-03-03 江南大学 DCT domain video watermark treatment method based on texture features
CN102930500A (en) * 2012-11-19 2013-02-13 海南大学 Medical image robust watermarking method based on Arnold scrambling transformation and DCT (discrete cosine transformation)
KR101580987B1 (en) * 2014-03-05 2015-12-29 광운대학교 산학협력단 A watermarking method for 3D stereoscopic image based on depth and texture images

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR100539365B1 (en) * 2003-02-19 2005-12-28 (주)디지탈이노텍 Invariant image watermark using zernike moments
US9292893B2 (en) * 2009-12-10 2016-03-22 Empire Technology Development Llc Chaotic watermarking for a digital image
CN103886235B (en) * 2014-03-03 2017-02-22 杭州电子科技大学 Face image biological key generating method

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101662686A (en) * 2009-09-07 2010-03-03 江南大学 DCT domain video watermark treatment method based on texture features
CN102930500A (en) * 2012-11-19 2013-02-13 海南大学 Medical image robust watermarking method based on Arnold scrambling transformation and DCT (discrete cosine transformation)
KR101580987B1 (en) * 2014-03-05 2015-12-29 광운대학교 산학협력단 A watermarking method for 3D stereoscopic image based on depth and texture images

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
基于NSCT-DCT-DWT-SVD联合数字水印算法;展虎 等;《计算机应用与软件》;20180315;第35卷(第3期);第309-315页 *

Also Published As

Publication number Publication date
CN110517182A (en) 2019-11-29

Similar Documents

Publication Publication Date Title
CN110517182B (en) Medical image zero watermark embedding method based on NSCT combined transformation
Thanki et al. An efficient medical image watermarking scheme based on FDCuT–DCT
Al-Ataby et al. A modified high capacity image steganography technique based on wavelet transform
Singh et al. Multilevel encrypted text watermarking on medical images using spread-spectrum in DWT domain
Jayashree et al. A Robust Image Watermarking Scheme Using Z-Transform, Discrete Wavelet Transform and Bidiagonal Singular Value Decomposition.
CN111988492B (en) Gabor-DCT-based medical image robust watermarking method
Novamizanti et al. A Robust Medical Images Watermarking Using FDCuT-DCT-SVD.
Singh et al. Secure watermarking scheme for color DICOM images in telemedicine applications
CN104851072A (en) Robust watermarking method for medical image in cloud environment based on DFT encryption
Elbasi et al. Robust medical image watermarking using frequency domain and least significant bits algorithms
Thanki et al. Medical imaging and its security in telemedicine applications
Elbasi M-SVD based quality measurement in hybrid non-blind watermarked medical images
Ayad et al. Qam-dwt-svd based watermarking scheme for medical images
Kukreja et al. Copyright protection scheme for color images using extended visual cryptography
Qu et al. Optical color watermarking based on single-pixel imaging and singular value decomposition in invariant wavelet domain
Elbasi Enhancing the robustness of watermarked medical images using heuristic search algorithm
Novamizanti et al. Robust and imperceptible watermarking on medical images using coefficient pair modification
Shrestha et al. Color image steganography technique using Daubechies discrete wavelet transform
Parah et al. Secure and reversible data hiding scheme for healthcare system using magic rectangle and a new interpolation technique
CN115809954A (en) Medical image watermarking method, device and medium
Boujemaa et al. Fragile watermarking of medical image for content authentication and security
Ma et al. A novel blind grayscale watermark algorithm based on SVD
Mohammed et al. Digital image watermarking, analysis of current methods
CN112907426A (en) PHTs-DCT-based encrypted medical image digital watermark method
Kaya Robust medical image watermarking using frequency domain and least significant bits algorithms

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant