CN111047497B - JPEG image steganography information positioning method based on same-frequency sub-image filtering - Google Patents
JPEG image steganography information positioning method based on same-frequency sub-image filtering Download PDFInfo
- Publication number
- CN111047497B CN111047497B CN201911344997.1A CN201911344997A CN111047497B CN 111047497 B CN111047497 B CN 111047497B CN 201911344997 A CN201911344997 A CN 201911344997A CN 111047497 B CN111047497 B CN 111047497B
- Authority
- CN
- China
- Prior art keywords
- image
- same
- sub
- hidden
- frequency
- 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
Links
- 238000001914 filtration Methods 0.000 title claims abstract description 50
- 238000000034 method Methods 0.000 title claims abstract description 38
- 239000011159 matrix material Substances 0.000 claims description 22
- 238000001228 spectrum Methods 0.000 claims description 6
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000012935 Averaging Methods 0.000 claims description 2
- 230000006835 compression Effects 0.000 description 9
- 238000007906 compression Methods 0.000 description 9
- 238000001514 detection method Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000005070 sampling Methods 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 239000000969 carrier Substances 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000006837 decompression Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000004807 localization Effects 0.000 description 2
- 241000764238 Isis Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000000546 chi-square test Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000035772 mutation Effects 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000013139 quantization Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
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
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
- Editing Of Facsimile Originals (AREA)
- Compression, Expansion, Code Conversion, And Decoders (AREA)
Abstract
The invention discloses a JPEG image steganography information positioning method based on same-frequency subgraph filtering, which combines coefficients at the same position of 8 multiplied by 8 blocks in a JPEG image to obtain 64 steganography images with the same frequency, then carries out low-pass filtering on the same-frequency subgraphs of the steganography images to estimate the carrier image with the same frequency subgraph, thereby estimating a carrier JPEG image and obtaining the estimation of DCT coefficients of the carrier JPEG image; and then calculating a residual error mean value of each position in the multiple concealed images to be detected with the same embedding path and embedding rate, realizing concealed position estimation according to the residual error mean value, and considering the characteristic that information is not embedded in a specific coefficient when the JPEG image is concealed when calculating the residual error, thereby obviously improving the positioning accuracy of the concealed position of JSTEG concealed writing.
Description
Technical Field
The invention relates to the technical field of information security, in particular to a JPEG image steganography information positioning method based on same-frequency sub-image filtering.
Background
At present, digital steganography is a technology of embedding information in redundancy of multimedia data such as digital images, video, audio, and text to realize secret communication. Researchers have proposed many distinctive steganographic algorithms for different application scenarios. The steganographic algorithms can be used for normal secure communication, and can be easily used by lawless persons to steal privacy, commercial confidentiality and the like of the Internet of things so as to avoid security protection. Therefore, in order to protect the security and privacy of the internet of things, it is necessary to develop a reverse steganalysis technology research.
At present, a series of steganography detection algorithms with excellent performance have been proposed for steganography algorithms taking images as carriers. The steganography detection algorithms can accurately judge the steganographic images of the traditional steganography, can effectively distinguish novel self-adaptive steganographic images, and can even estimate the information ratio of steganography embedding or the change ratio of the steganographic embedded information to a carrier. Theoretically, for a steganographic system, the steganographic analyst can consider the steganographic system to have been breached as long as the steganographic analyst can correctly distinguish the carrier from the hidden object with a probability that exceeds random guessing. However, actually, the forensics are not always satisfied with being able to detect the confidential object, and it is more desirable to be able to accurately extract the embedded secret information. Compared with the hidden object detection, the extraction of the steganographic information is much more difficult, and the embedded information length and the steganographic position selection mechanism and the embedded position information are often required to be known.
Since the sequential steganography sequentially embeds the steganographic information in local areas of the carrier, the statistical properties of the local areas containing the steganographic information and other areas are obviously different. Therefore, early work on steganographic information localization focused primarily on sequential steganography. Researchers have proposed a variety of steganographic information positioning methods such as chi-square test method, local hue consistency method, continuous probability ratio test and optimization accumulation sum method, weighted steganographic image optimization method, JPEG blocking discontinuity sequence mutation point estimation method, and the like, successively for sequential space domain Least Significant Bit (LSB) replacement steganography, spread spectrum steganography, JSteg steganography, and the like.
Compared with sequential steganography, random steganography randomly distributes steganography information in the whole carrier, and local areas with obvious abnormal statistical characteristics cannot appear in the carrier, so that difficulty is brought to positioning of random steganography information. Therefore, early studies on the positioning of random steganographic information were rare and poor in performance. Such as: in 2004, davidson and Paul use the idea of anomaly point detection in data mining for reference, regard the hidden pixel positioning problem as image anomaly point detection based on energy, and provide an airspace hidden write information positioning algorithm based on anomaly point detection, wherein the algorithm has a large error for judging hidden positions and almost has no effect on the hidden pixel positioning result of a texture complex area; the Ambalavanan and Chandramouli model images by using a Markov random field, and provide a space domain steganography information positioning algorithm based on Bayesian estimation by using the similarity between the images and a statistical mechanics system, wherein the algorithm fails to apply to steganography with small changes to a carrier, such as LSB replacement, LSB matching steganography and the like.
In 2008, ker and the like provide an information positioning algorithm for replacing steganography by a spatial domain LSB based on a weighted steganography residual error under the condition of having a plurality of steganography images with the same embedding positions for the first time. Under the condition, researchers propose various steganographic information positioning algorithms with higher positioning accuracy. Such as: chiew and Pieprzyk are combined with the local entropy of the block where the pixel is located to improve the method proposed by Ker, and a binary image replacement steganography information positioning algorithm based on weighted steganography residual error and local entropy is proposed; ker and Lubenko provide an information positioning algorithm of airspace LSB matching steganography based on wavelet filtering by performing wavelet filtering on the hidden image and inversely transforming the obtained wavelet residual into airspace residual; quach utilizes a hidden Markov model to model a carrier image, then utilizes a Viterbi decoding algorithm to find the optimal estimation of the carrier image, and provides an information positioning algorithm based on LSB replacement and LSB matching steganography of maximum posterior probability estimation; then, the Quach uses a Markov random field model to model the carrier image, then uses a quadratic pseudo-binary optimization algorithm (QPBO) to find the optimal estimation of the carrier image, and provides an information positioning algorithm based on LSB replacement and LSB matching steganography of maximum a posteriori probability (MAP); gui and the like obtain 9 estimated carrier images through 4 neighborhood means and MAP estimation along 8 different directions, and improve the information positioning precision of LSB matching steganography by a steganography information positioning algorithm based on MAP by fusing corresponding residual errors; liu and the like can more accurately estimate the spatial domain carrier image subjected to JPEG compression by JPEG compression and decompression of the hidden image, and provide an LSB replacement and LSB matching steganography information positioning algorithm based on JPEG recompression to position steganography information with high precision. (ii) a Analysis by Yang and the like proves the property of the optimal steganography subset replaced by the minimum multiple bits, and the information positioning algorithm for replacing the steganography by the minimum multiple bits (MLSB) based on the optimal steganography subset is provided by utilizing wavelet filtering to estimate a carrier image. Compared with the early steganographic information positioning algorithm, the positioning accuracy of random steganographic information is greatly improved by the algorithm, and under certain specific conditions, partial algorithms are already used for estimating grouping of group parity steganographic and determining the steganographic pixel embedding sequence of random steganographic, even used for recovering steganographic keys, and extraction of steganographic information is realized. However, the above algorithms are only directed to LSB replacement, LSB matching, and MLSB replacement steganography with spatial domain images as carriers.
JPEG is the first digital image compression standard internationally, and the most widely used image compression standard that has been used to date. JPEG images generated by compression according to this standard are the most widely used image format at present.
Actually, JPEG images on the internet are more widely applied, and the steganography and steganography analysis of JPEG images have become a hotspot of research in the field of information hiding.
The main process of JPEG compression is as follows. The image is firstly converted into YCbCr color space, for example, the image before compression is R, G and B three-color space domain image, which is converted into three-color representation by Y, cb and Cr. Secondly, the chroma and the saturation are down-sampled according to the set sampling mode. When the set sampling mode is YUV411 or YUV422, the proportional sampling of three components of Y, cb and Cr is 4. Each component after sampling is then divided into 8 x 8 non-overlapping partitions. Next, 128 is subtracted from the data in each 8 × 8 block and a Discrete Cosine Transform (DCT) is performed thereon. And quantizing each 8 x 8 DCT coefficient block obtained by transformation by using a set quantization table. And finally, performing Huffman coding on the quantized DCT coefficient to obtain a JPEG image. JPEG decompression is the inverse of JPEG compression.
Random steganography taking a JPEG image as a carrier randomly selects quantized DCT coefficients from the JPEG image and then embeds information into the DCT coefficients. The method can be described as performing pseudo-random scrambling on quantized DCT coefficients in a JPEG image according to a given steganographic key, and then sequentially selecting a certain number of coefficients from a scrambled coefficient sequence to embed information. Because some JPEG image steganography algorithms consider some of the coefficients at specific positions or with specific values to be unavailable for embedding information, it is possible to scramble only the coefficients that can embed information during scrambling. Therefore, according to whether the non-embeddable coefficient is eliminated or not during the pseudo-random scrambling, the method for selecting the embedding position in the JPEG image random steganography can be divided into two types: the pseudo random scrambling method comprises a pseudo random scrambling method for eliminating the non-embeddable coefficients and a pseudo random scrambling method for not eliminating the non-embeddable coefficients.
When the JPEG image steganography adopts a pseudo-random scrambling method without eliminating non-embeddable coefficients, taking typical JSTEG steganography as an example, the embedding process is shown as follows.
(1) Performing Haffman decoding on the JPEG image or performing JPEG compression on the spatial domain image until the DCT coefficient is quantized to obtain a quantized DCT coefficient;
(2) Scrambling all quantized DCT coefficients in the whole JPEG image according to the steganographic key to obtain a scrambled DCT coefficient matrix;
(3) Sequentially selecting coefficients from the scrambled DCT coefficient matrix, and executing the following operations on the selected coefficients:
1) If the currently selected coefficient is a DCT coefficient or a JSTEG steganographic non-embeddable coefficient such as an AC coefficient with the value of 0 and 1, the coefficient is considered to be non-embeddable information, and the next coefficient is selected by skipping the coefficient;
2) And if the currently selected coefficient is the JSTEG steganographically embeddable coefficient, replacing the least significant bit of the selected coefficient by the currently to-be-embedded bit in the secret information, selecting the next coefficient, and reading the next to-be-embedded bit information.
3) And when the embedding of the secret information is finished, or the ratio of the selected coefficients to all the coefficients exceeds a certain threshold value, the embedding is finished.
(4) Inversely scrambling the coefficient matrix after embedding the information;
(5) And carrying out Huffman coding on the hidden coefficient matrix to generate a hidden JPEG image.
Researchers have proposed many steganography algorithms using JPEG images as carriers and designed many effective steganography detection algorithms. However, due to the weak correlation between the DCT coefficients in the JPEG image, accurate estimation of the carrier image is difficult. And it is difficult to align the hidden positions in multiple hidden images to obtain the positions where information is hidden in different hidden images or the positions where information is not hidden. These reasons make the implicit information localization algorithm of JPEG image not seen at present.
Disclosure of Invention
The invention aims to provide a JPEG image steganography information positioning method based on same-frequency sub-image filtering, which can estimate the steganography position of a JPEG image so as to realize the JPEG image steganography information positioning.
The technical scheme adopted by the invention is as follows:
a JPEG image steganography information positioning method based on same-frequency subgraph filtering comprises the following steps:
a: for given T pieces of hidden images to be detected with the same embedding position, estimating a quantized DCT coefficient in each position of the carrier JPEG image corresponding to each hidden image to be detected by adopting a carrier JPEG image estimation method based on same-frequency sub-image filtering to obtain an estimated DCT coefficient of each position of the carrier JPEG image;
b: calculating the residual error between the actual DCT coefficient of each position in each to-be-detected hidden image and the estimated DCT coefficient; if the actual DCT coefficient of a certain position in a certain to-be-detected hidden image is an embeddable coefficient, setting the residual error of the position in the hidden image to be 0;
c: calculating the mean value of the residual errors at the same position of all the hidden images to obtain the mean value of the residual errors at each position of the hidden images to be detected;
d: judging whether the residual error mean value of each position is a hidden position or not according to the residual error mean value of each position, thereby judging the positions of all hidden actual DCT coefficients; for a hidden image, if the position of an actual DCT coefficient is the estimated hidden position and the actual DCT coefficient is an embeddable coefficient, the actual DCT coefficient is judged to contain embedded secret information, and the position of the actual DCT coefficient is judged to be the hidden position.
Specifically, the step a specifically includes the following steps:
a1: performing Huffman decoding on the hidden image to be detected to obtain a matrix S consisting of quantized actual DCT coefficients;
wherein M represents the height of the to-be-detected hidden image, N represents the width of the to-be-detected hidden image, and M and N are integral multiples of 8; s is p,q The actual DCT coefficient of the p +1 row and the q +1 column in the matrix S is the quantized actual DCT coefficient of the (p, q) position of the to-be-detected hidden image, wherein p is more than or equal to 0 and less than or equal to M-1, and q is more than or equal to 0 and less than or equal to N-1;
a2: carrying out hidden image same-frequency subgraph division on the matrix S; combining actual DCT coefficients of the same position in the 8 x 8 blocks of the to-be-detected hidden image, namely the 8 x 8 blocks of the to-be-detected hidden image, and obtaining 64 same-frequency subgraphs of the hidden image for 64 different positions in the 8 x 8 blocks, S (i,j) Representing hidden image same-frequency subgraphs at the (i, j) positions in 8 multiplied by 8 blocks, i is more than or equal to 0 and less than or equal to 7, j is more than or equal to 0 and less than or equal to 7;
wherein
M is the height of the to-be-detected hidden image, and N is the width of the to-be-detected hidden image;
a3: low-pass filtering F for 64 hidden image same-frequency subgraphs by using low-pass filter low (S (i,j) ) Obtaining the estimated quantized carrier image same-frequency subgraph
Wherein
Same-frequency subgraph S for representing hidden image (i,j) Corresponding carrier image same-frequency subgraph C (i,j) I is more than or equal to 0 and less than or equal to 7, j is more than or equal to 0 and less than or equal to 7;
a4: combining the same-frequency subgraphs of the carrier image to obtain an estimated carrier JPEG image; placing the estimated DCT coefficient in the same-frequency subgraph of the carrier image at the position of the corresponding actual DCT coefficient to obtain the estimated DCT coefficient of each position in the JPEG image of the carrier:
wherein
A matrix representing the composition of the estimated DCT coefficients in the carrier image,
representation matrix
And the estimated DCT coefficient of the p +1 th line and the q +1 th column is the quantized DCT coefficient of the (p, q) position of the carrier image, wherein p is more than or equal to 0 and less than or equal to M-1, and q is more than or equal to 0 and less than or equal to N-1.
Specifically, the step A3 specifically includes the following steps:
a3.1: respectively carrying out wavelet decomposition on all same-frequency subgraphs in the hidden image to be detected: same-frequency subgraph S of hidden image by using 8-tap Daubechies filter (i,j) Decomposing to obtain hidden image same-frequency subgraph S (i,j) The four subbands include a low-frequency subband L, a horizontal subband H, a vertical subband V, and a diagonal subband D;
l (x, y) represents a hidden image same-frequency sub-image S (i,j) H (x, y) represents the same-frequency subgraph S of the hidden image (i,j) The wavelet coefficient value of (x, y) position in the horizontal sub-band H of (A), V (x, y) represents the same-frequency sub-image S of the hidden image (i,j) Of the vertical sub-band V (x, y) positionD (x, y) represents a hidden image same-frequency subgraph S (i,j) The wavelet coefficient value at the (x, y) position in the diagonal sub-band D of (1) x ≦ He,1 ≦ y ≦ Wi, he represents the height of any one of the four sub-bands, and Wi represents the width of any one of the four sub-bands;
a3.2: respectively in hidden image same frequency sub-image S (i,j) The minimum local variance solution is performed in the horizontal subband H, the vertical subband V, and the diagonal subband D: obtaining the local variance of each wavelet coefficient of a co-frequency subgraph of a carrier image by utilizing the maximum posterior estimation of 4 square dXd neighborhoods on a horizontal sub-band H, a vertical sub-band V and a diagonal sub-band D by utilizing a formula (5);
wherein
Representing the local variance of (x, y) position in the horizontal sub-band H of the same-frequency subgraph of the carrier image;
representing the local variance of the (x, y) position in the vertical sub-band V of the same-frequency subgraph of the carrier image,
representing the local variance of (x, y) positions in the diagonal sub-band D of the carrier image co-frequency sub-image;
representing the variance of the steganographic noise,
represents the mean of the squares of the wavelet coefficients in a square d x d neighborhood centered at the (x, y) position in the horizontal subband H,
representing the square inside a d x d neighborhood centered at the (x, y) position in the vertical subband VThe average of the squares of the wavelet coefficients,
represents the mean of the squares of the wavelet coefficients in a square D x D neighborhood centered at the (x, y) position in the diagonal subband D; d belongs to {3,5,7,9};
a3.3: wavelet coefficients of a horizontal sub-band H, a vertical sub-band V and a diagonal sub-band D are subjected to low-pass filtering by using a wiener filter:
H low (x, y) denotes the low-pass filtered horizontal sub-band H low Estimated coefficient values for the (x, y) position;
V low (x, y) denotes the low-pass filtered vertical sub-band V low Estimated coefficient values for the (x, y) position;
D low (x, y) denotes the low-pass filtered diagonal sub-band D low Estimated coefficient values for the (x, y) position;
a3.4: low frequency sub-band L and low pass filtered horizontal sub-band H low Low pass filtered vertical sub-band V low Low pass filtered diagonal sub-band D low And performing inverse wavelet transform to obtain estimated DCT coefficients in the carrier image.
Combining coefficients at the same position of each 8 multiplied by 8 block in a JPEG image to obtain 64 hidden image same-frequency subgraphs, then carrying out low-pass filtering on the hidden image same-frequency subgraphs, estimating a carrier image same-frequency subgraph, estimating a carrier JPEG image and obtaining estimation of DCT coefficients of the carrier JPEG image; and then calculating a residual error mean value of each position in the multiple concealed images to be detected with the same embedding path and embedding rate, realizing concealed position estimation according to the residual error mean value, and considering the characteristic that information is not embedded in a specific coefficient when the JPEG image is concealed when calculating the residual error, thereby obviously improving the positioning accuracy of the concealed position of JSTEG concealed writing.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a schematic diagram of the process of the present invention;
FIG. 3 is a schematic diagram of a carrier JPEG image estimation method of the invention;
FIG. 4 is a schematic diagram of a hidden image same-frequency subgraph dividing method according to the invention;
FIG. 5 is a schematic diagram of T hidden images with the same embedding path and embedding rate according to the present invention;
Detailed Description
As shown in fig. 1 and fig. 2, the JPEG image steganography information positioning method based on co-frequency subgraph filtering according to the present invention includes the following steps:
a: for given T pieces of hidden images to be detected with the same embedding position, a carrier based on same-frequency sub-image filtering is adopted
The JPEG image estimation method comprises the steps of estimating the DCT coefficient quantized in each position of the JPEG image of the carrier corresponding to each to-be-detected hidden image to obtain the estimated DCT coefficient of each position of the JPEG image of the carrier, namely obtaining the estimated carrier
A JPEG image.
Specifically, as shown in fig. 3, the step a specifically includes the following steps,
a1: performing Huffman decoding on the hidden image to be detected to obtain a matrix S consisting of quantized actual DCT coefficients;
wherein M represents the height of the to-be-detected hidden image, N represents the width of the to-be-detected hidden image, and both M and N are integral multiples of 8; s is p,q The actual DCT coefficient of the p +1 row and the q +1 column in the matrix S is the quantized actual DCT coefficient of the (p, q) position of the to-be-detected hidden image, wherein p is more than or equal to 0 and less than or equal to M-1, and q is more than or equal to 0 and less than or equal to N-1;
a2: carrying out hidden image same-frequency subgraph division on the matrix S; combining actual DCT coefficients of the same positions of all 8 x 8 blocks of the concealed image to be detected, namely the same frequency spectrum of all 8 x 8 blocks in the matrix S, and obtaining 64 different positions in the 8 x 8 blocksTo 64 hidden images with the same frequency subgraphs, S (i,j) Representing the same-frequency subgraph of the hidden image at the position of (i, j) in the 8 multiplied by 8 block, i is more than or equal to 0 and less than or equal to 7, j is more than or equal to 0 and less than or equal to 7;
wherein
M is the height of the to-be-detected hidden image, and N is the width of the to-be-detected hidden image;
a3: low-pass filtering F is carried out on 64 hidden image same-frequency subgraphs by utilizing a low-pass filter low (S (i,j) ) Obtaining the estimated quantized carrier image same-frequency subgraph
Setting S in step A2 (i,j) K +1 row and l +1 column of (d) has an actual DCT coefficient of s i+8k,j+8l In step A3
The estimated DCT coefficient of the k +1 th row and l +1 st column is s i+8k,j+8l Obtained by low-pass filtering and is represented as
Namely, it is
Is shown atEstimated carrier image co-frequency subgraph
Row k +1, column l + 1. K is more than or equal to 0 and less than or equal to m, and l is more than or equal to 0 and less than or equal to n.
A4: combining the same-frequency subgraphs of the carrier image to obtain an estimated carrier JPEG image; placing the estimated DCT coefficient in the same-frequency subgraph of the carrier image at the position of the corresponding actual DCT coefficient to obtain the estimated DCT coefficient of each position in the JPEG image of the carrier:
The different locations of each 8 x 8 block of actual DCT coefficients in a JPEG image represent different frequency spectra, and the coefficients at each location represent energy over different frequency spectra. The coefficient at the (0, 0) position is a direct current coefficient DC, the coefficients at other positions are alternating current coefficients AC, the coefficient at the upper left corner is a low frequency coefficient, and the coefficient at the lower right corner is a high frequency coefficient. Because there is usually strong correlation between the image contents of adjacent 8 × 8 blocks in a JPEG image, there is also strong correlation between the same spectral energy, i.e. the same position coefficient, in the adjacent 8 × 8 blocks in the quantized DCT coefficient matrix. Therefore, 64 hidden image co-frequency subgraphs can be obtained by combining all the 8 × 8 blocks in the JPEG image with the same frequency spectrum, namely, the coefficients at the same position, and then the estimation of the carrier JPEG image is realized by low-pass filtering the hidden image co-frequency subgraphs, thereby solving the problem that the carrier JPEG image is difficult to obtain.
B: calculating the residual error between the actual DCT coefficient of each position in each to-be-detected hidden image and the estimated DCT coefficient; if the actual DCT coefficient of a certain position in a certain to-be-detected hidden image is an embeddable coefficient, setting the residual error of the position in the hidden image to be 0;
c: averaging the residual errors of the same positions of all the hidden images to obtain the residual error average value of each position of the hidden image to be detected;
d: judging whether the residual error mean value of each position is a hidden position or not according to the residual error mean value of each position, so as to judge the positions of all hidden actual DCT coefficients; for a hidden image, if the position of an actual DCT coefficient is judged to be an estimated hidden position and the actual DCT coefficient is an embeddable coefficient, the actual DCT coefficient is judged to contain embedded secret information, and the position of the actual DCT coefficient is judged to be the hidden position.
When the embedding position is selected by adopting a pseudo-random scrambling method without eliminating the non-embeddable coefficient, the T pieces of the to-be-detected hidden image S with the same embedding path and embedding rate are subjected to 1 ,S 2 ,…,S T Actual DCT coefficients S of the same position (i, j) 1 (i,j),S 2 (i,j),…,S T (i, j) should belong to one of the following two cases as shown in FIG. 5. In fig. 5, black squares represent non-embeddable coefficients, white squares represent embeddable coefficients without embedded information, grid squares represent embeddable coefficients with embedded information, and numbers in circles represent the positions of coefficients in the pre-scrambling graph.
1) When position (i, j) is a steganographic position, the steganographer will determine whether to embed the information based on whether the coefficient is embeddable, at which time S 1 (i,j),S 2 (i,j),…,S T (i, j) belongs to the first case: s 1 (i,j),S 2 (i,j),…,S T (i, j) any coefficient S t (i, j) either non-embeddable coefficients or blind coefficients of embedded information;
2) When position (i, j)When the position is a non-hidden position, regardless of whether the coefficient can be embedded, the steganographer will not embed information in the position, and S is the time 1 (i,j),S 2 (i,j),…,S T (i, j) belongs to the second case: s. the 1 (i,j),S 2 (i,j),…,S T (i, j) any coefficient S t (i, j) are all non-secret coefficients with no information embedded.
Therefore, for each to-be-detected hidden image, if the position of an actual DCT coefficient is determined to be an estimated hidden position and the actual DCT coefficient is an embeddable coefficient, the actual DCT coefficient is determined to contain embedded secret information, and the position of the actual DCT coefficient is determined to be a hidden position.
In this embodiment, the low-pass filtering F described in step A3 low (S (i,j) ) Specifically, a wavelet filtering mode is adopted to obtain an estimated quantized DCT coefficient subgraph
The wavelet filtering has good multi-direction and multi-resolution analysis capability, can capture fine detail difference in an image, and the steganographic noise has the characteristic of small noise, so that the high-pass wavelet filtering can capture the steganographic noise well and shows excellent performance in JPEG image steganographic detection. Correspondingly, the low-pass wavelet filtering can well remove the steganographic noise in the steganographic image, and the carrier image can be estimated more accurately. Therefore, the low-pass wavelet filtering is used for estimating the carrier image co-frequency subgraph in the hidden image co-frequency subgraph.
The step A3 specifically comprises the following steps:
a3.1: carrying out wavelet decomposition on the actual DCT coefficient in each same-frequency subgraph of the hidden image to be detected: same-frequency subgraph S of hidden image by using 8-tap Daubechies filter (i,j) Decomposing to obtain hidden image same-frequency subgraph S (i,j) The four subbands include a low-frequency subband L, a horizontal subband H, a vertical subband V, and a diagonal subband D;
l (x, y) represents a hidden image same-frequency subgraph S (i,j) Low frequency subband L middle (x, y)Wavelet coefficient value of position, H (x, y) represents the same-frequency subgraph S of the hidden image (i,j) The coefficient value of (x, y) position in the horizontal sub-band H, V (x, y) represents the same-frequency sub-image S of the hidden image (i,j) The wavelet coefficient value of (x, y) position in the vertical sub-band V of (D, x, y) represents the same-frequency sub-picture S of the hidden image (i,j) The wavelet coefficient value at the (x, y) position in the diagonal sub-band D of (1) x ≦ He,1 ≦ y ≦ Wi, he represents the height of any one of the four sub-bands, and Wi represents the width of any one of the four sub-bands;
a3.2: respectively in the hidden image same frequency subgraph S (i,j) The minimum local variance solution is performed in the horizontal subband H, the vertical subband V, and the diagonal subband D: obtaining the local variance of each wavelet coefficient of a co-frequency subgraph of a carrier image by utilizing the maximum posterior estimation of 4 square dXd neighborhoods on a horizontal sub-band H, a vertical sub-band V and a diagonal sub-band D by utilizing a formula (5);
a3.3: wavelet coefficients of a horizontal sub-band H, a vertical sub-band V and a diagonal sub-band D are subjected to low-pass filtering by using a wiener filter:
H low (x, y) denotes the low-pass filtered horizontal sub-band H low The estimated coefficient value of the (x, y) position;
V low (x, y) denotes the low-pass filtered vertical sub-band V low Estimated coefficient values for the (x, y) position;
D low (x, y) denotes the low-pass filtered diagonal sub-band D low The estimated coefficient value of the (x, y) position;
a3.4: low frequency sub-band L and low pass filtered horizontal sub-band H low Low pass filtered vertical sub-band V low Low pass filtered diagonal sub-band D low And performing inverse wavelet transform to obtain an estimated DCT coefficient in the carrier image.
And D, according to the residual mean value of each position in the step D, judging whether the position is a hidden position or not, and demonstrating:
let C 1 ,C 2 ,…,C T Representing T number of to-be-detected hidden images S 1 ,S 2 ,…,S T Corresponding carrier image, S t Representing the t-th latent image to be detected, C t Representing the carrier image, p, corresponding to the t-th steganographic image to be detected 0 Denotes S 1 (i,j),S 2 (i,j),…,S T Ratio of non-embeddable coefficients in (i, j), p 1 Denotes S 1 (i,j),S 2 (i,j),…,S T (i, j) ratio of embeddable coefficients, S t (i, j) represents the actual DCT coefficient of the position of the t-th to-be-detected hidden image (i, j), C t (i, j) represents the DCT coefficient of the carrier image (i, j) position corresponding to the t-th hidden image to be detected, R (i, j) represents the mean value of the absolute difference values of the DCT coefficients of all the hidden images to be detected and the carrier image at the (i, j) position, namely the residual error between the actual DCT coefficient of the hidden image to be detected at the (i, j) position and the DCT coefficient of the corresponding carrier image
1≤t≤T；
When the steganographically embedded information is pseudo-random information and the average modification amplitude for each embeddable coefficient is α, if the position (i, j) is a hidden position, then the mean value R of the absolute difference of the position coefficients (i, j) 1 (i,j)≈p 1 α。
If the position (i, j) is a non-hidden position, then the mean value R of the absolute difference of the position coefficients of (i, j) 0 (i,j)＝0。
From R 1 (i, j) and R 0 (i, j) it can be seen that the mean values of the absolute difference values of the same position coefficients of the carrier image at the concealed position and the non-concealed position and the concealed image to be detected are obviously different, so that whether the position is the concealed position or not can be judged according to the residual mean value of each position.
Specifically, in the step B, after the carrier image corresponding to each to-be-detected hidden image is estimated, for the residual error between the actual DCT coefficient at each position in each to-be-detected hidden image and the estimated DCT coefficient, the calculation may be performed by using a method of calculating an absolute value in formula (7), or may be performed by using another suitable method according to a manner of changing the actual DCT coefficient by a steganography algorithm.
Taking a typical JSteg steganography as an example, the steganography embeds information by replacing the LSBs of embeddable coefficients. Research results in the aspect of positioning of spatial domain LSB replacement steganographic information show that WS (Weighted Stego-image) residual errors adjust difference signs according to asymmetry of sample change during LSB replacement (namely, only even numbers can be added with 1, and odd numbers can be subtracted with 1), and excellent performance is shown in LSB replacement steganographic information positioning. Therefore, the method can be applied to the calculation of the residual error in the step B of the invention when the JSTEG is steganographically, and can be adjusted according to whether the coefficient can be embedded or not. Since the JSteg steganography regards coefficients with values of 0 and 1 and the DC coefficient as non-embeddable coefficients, the WS residual error calculation formula of the JSteg steganography of each to-be-detected steganographic image is as follows:
wherein r is t (i, j) representing a concealed image subjected to JSTEG concealed writing, wherein all concealed images to be detected and estimated carrier images are aligned, and (i, j) the average value of absolute difference values of the actual DCT coefficient and the estimated DCT coefficient at the position, namely WS residual error between the actual DCT coefficient and the estimated DCT coefficient at the position (i, j);
S t representing the t-th hidden image;
representing an image obtained after LSB (least significant bit) of the embeddable coefficient of the T-th hidden image is turned, wherein T is more than or equal to 1 and less than or equal to T;
S t (i, j) represents the actual DCT coefficient of the position of the t-th hidden image (i, j) to be detected,
and (3) representing DCT coefficients of the position of the image (i, j) obtained after LSB inversion of the embeddable coefficients of the t-th hidden image.
mod (i, 8) represents the calculation of the remainder of i divided by 8.
Experiments are performed below on the JPEG image steganography information positioning method of the present invention.
In this embodiment, 10000 images with 512 × 512 PGM format from bossbase1.0 image library are JPEG-compressed by a quality factor of 75 in Matlab to obtain 10000 JPEG images. Then 1000 carrier images with the number of nonzero coefficients between 30000 and 50000 are randomly selected from 10000 carrier JPEG images. And scrambling the integers of 1-512 x512 by using a randderm function to obtain a pseudorandom path. Finally, selecting embeddable coefficients with the ratio q E {0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8 and 0.9} positioned in the front part of the pseudo-random path from 1000 carrier JPEG images, and embedding pseudo-random information in the selected coefficients by JSTEG steganography to respectively generate 10 test hidden image sets with different embedding ratios.
The algorithm presented herein will be tested for effectiveness and compared to other algorithms for performance, using the generated 10 sets of hidden images as samples.
In order to test the advantages of adopting same-frequency subgraph filtering and JSTEG steganography WS residual error given by the formula (8), the coefficient of the whole graph is subjected to low-pass wavelet filtering, and wavelet filtering is carried out on the same-frequency subgraph of the hidden image in the invention; then, respectively calculating the WS residual error of JSTEG steganography given by the formula (8) and the original WS residual error without setting the residual error of the non-embeddable position to 0, and realizing the JSTEG steganography information positioning algorithm under two different residual error calculation modes. Finally, comparing the performance difference of two JSTeg steganographic information positioning algorithms (SFSW-JSTeg and SFS 4-JSTeg) based on co-frequency sub-image filtering and the steganographic information positioning algorithms (WIW-JSTeg and WIW) based on full-image coefficient wavelet filtering.
SFSW-JSTEG (same-frequency subgraph wavelet filtering + JSTEG steganography non-embeddable coefficient residual setting of 0): estimating a carrier DCT coefficient by adopting wavelet filtering of a same-frequency subgraph, setting a residual error of a non-embeddable coefficient to be 0 when calculating a JSTEG steganography residual error between an actual DCT coefficient of a to-be-detected steganographic image and the estimated carrier image DCT coefficient, and finally realizing steganography information positioning according to an average value of residual errors at the same position of all to-be-detected images.
SFS4-JSTEG (same-frequency subgraph 4 neighborhood filtering + JSTEG steganography non-embeddable coefficient residual setting of 0): estimating the DCT coefficient of the carrier by using neighborhood filtering of a same-frequency sub-image 4, setting the residual error of the non-embeddable coefficient to be 0 when calculating the JSTEg steganographic residual error between the actual DCT coefficient of the steganographic image to be detected and the estimated DCT coefficient of the carrier image, and finally realizing positioning of steganographic information according to the average value of the residual errors at the same position of all the images to be detected.
WIW-JSTEG (full-map spatial wavelet filtering + JSTEG steganographic residual of non-embeddable coefficients set to 0): and performing wavelet filtering on the actual DCT coefficient of the full image to estimate the DCT coefficient of the carrier, setting the residual error of the non-embeddable coefficient to 0 when calculating the JSTEG steganography residual error between the actual DCT coefficient of the steganography image to be detected and the estimated DCT coefficient of the carrier image, and finally realizing steganography information positioning according to the average value of the residual errors at the same position of all the images to be detected.
WIW (whole-image coefficient wavelet filtering + left to set 0 the residual of JSteg stego un-embeddable coefficients): and performing wavelet filtering on the actual DCT coefficient of the full image to estimate the DCT coefficient of the carrier, then not setting the residual error of the non-embeddable coefficient to 0 when calculating the JSTEG steganography residual error between the actual DCT coefficient of the steganography image to be detected and the estimated DCT coefficient of the carrier image, and finally realizing steganography information positioning according to the average value of the residual errors at the same position of all the images to be detected.
Table 1 gives the positioning accuracy of four different steganographic information positioning algorithms SFSW-JSteg, SFS4-JSteg, WIW-JSteg and WIW for different numbers of stego positions (i.e. the first 50% positions of the embedding path) in the stego image with an embedding rate q = 0.5.
Table 2 shows the positioning accuracy of the steganographic position in 1000 steganographic images with the same embedding path and embedding rate by four different steganographic information positioning algorithms SFSW-JSTEg, SFS4-JSTEg, WIW-JSTEg and WIW under different embedding rates. It can be seen from tables 1 and 2 that the four algorithms can position the hidden positions with a correct rate exceeding the random guess, and the correct rate of positioning the hidden positions is obviously improved as the number of the hidden images and the embedding rate increase. The positioning accuracy of the two JSTEG steganographic information positioning algorithms based on the same-frequency sub-image filtering to the hidden position is obviously higher than that of the steganographic information positioning algorithm based on the full-image coefficient wavelet filtering. Particularly, the JSteg steganography information positioning algorithm SFSW-JSteg based on the same-frequency subgraph wavelet filtering is the most excellent in performance. The performance of the steganographic information positioning algorithm WIW based on the wavelet filtering of the full image coefficient is the worst, on one hand, because the correlation between adjacent position coefficients in the JPEG image is weak, the estimation precision of the coefficient of the carrier image through the wavelet filtering of the full image coefficient is poor; on the other hand, the WIW algorithm fails to consider the characteristics of JSTEG steganography, and the residual error of the JSTEG steganography non-embeddable coefficient is not set to be 0.
TABLE 1 accuracy of four steganographic information positioning algorithms at different steganographic image quantities
Table 2 accuracy of four steganographic information positioning algorithms for positioning hidden positions in 1000 images under different embedding rates
Claims (3)
1. A JPEG image steganography information positioning method based on same-frequency subgraph filtering is characterized in that: the method comprises the following steps:
a: for given T pieces of hidden images to be detected with the same embedding position, estimating a quantized DCT coefficient in each position of the carrier JPEG image corresponding to each hidden image to be detected by adopting a carrier JPEG image estimation method based on same-frequency sub-image filtering to obtain an estimated DCT coefficient of each position of the carrier JPEG image;
b: calculating the residual error between the actual DCT coefficient of each position in each to-be-detected hidden image and the estimated DCT coefficient; if the actual DCT coefficient of a certain position in a certain to-be-detected hidden image is an embeddable coefficient, setting the residual error of the position in the hidden image to be 0;
c: averaging the residual errors of the same positions of all the hidden images to obtain the residual error average value of each position of the hidden image to be detected;
d: judging whether the residual error mean value of each position is a hidden position or not according to the residual error mean value of each position, so as to judge the positions of all hidden actual DCT coefficients; for a hidden image, if the position of an actual DCT coefficient is the estimated hidden position and the actual DCT coefficient is an embeddable coefficient, the actual DCT coefficient is judged to contain embedded secret information, and the position of the actual DCT coefficient is judged to be the hidden position.
2. The JPEG image steganography information positioning method based on same-frequency subgraph filtering according to claim 1, characterized in that: the step A specifically comprises the following steps:
a1: performing Huffman decoding on the hidden image to be detected to obtain a matrix S consisting of quantized actual DCT coefficients;
wherein M represents the height of the to-be-detected hidden image, N represents the width of the to-be-detected hidden image, and both M and N are integral multiples of 8; s is p，q The actual DCT coefficients of the p +1 th row and the q +1 th column in the matrix S are the quantized actual DCT coefficients of the (p, q) position of the to-be-detected hidden image, wherein p is more than or equal to 0 and less than or equal to M-1, and q is more than or equal to 0 and less than or equal to N-1;
a2: carrying out hidden image same-frequency subgraph division on the matrix S; combining the actual DCT coefficients of the same frequency spectrum of all the 8 multiplied by 8 blocks in the matrix S, namely the same position in the 8 multiplied by 8 blocks of the hidden image to be detected, and obtaining 64 same-frequency sub-images of the hidden image for 64 different positions in the 8 multiplied by 8 blocks, S (i，j) Representing the same-frequency subgraph of the hidden image at the position of (i, j) in the 8 multiplied by 8 block, i is more than or equal to 0 and less than or equal to 7, j is more than or equal to 0 and less than or equal to 7;
wherein
M is the height of the to-be-detected hidden image, and N is the width of the to-be-detected hidden image;
a3: low-pass filtering 64 hidden image same-frequency subgraphs by using low-pass filterWave F low (S (i，j) ) Obtaining the estimated quantized carrier image same-frequency subgraph
Wherein
Same-frequency subgraph S for representing hidden image (i，j) Corresponding carrier image same-frequency subgraph C (i，j) I is more than or equal to 0 and less than or equal to 7, j is more than or equal to 0 and less than or equal to 7;
a4: combining the same-frequency subgraphs of the carrier image to obtain an estimated carrier JPEG image; placing the estimated DCT coefficient in the same-frequency subgraph of the carrier image at the position of the corresponding actual DCT coefficient to obtain the estimated DCT coefficient of each position in the JPEG image of the carrier:
wherein
A matrix representing the composition of the estimated DCT coefficients in the carrier image,
representation matrix
And the estimated DCT coefficient of the p +1 th line and the q +1 th column is the quantized DCT coefficient of the (p, q) position of the carrier image, wherein p is more than or equal to 0 and less than or equal to M-1, and q is more than or equal to 0 and less than or equal to N-1.
3. The JPEG image steganography information positioning method based on same-frequency subgraph filtering according to claim 2, characterized in that: the step A3 specifically comprises the following steps:
a3.1: respectively carrying out wavelet decomposition on all same-frequency subgraphs in the hidden image to be detected: same-frequency subgraph S of hidden image by using 8-tap Daubechies filter (i，j) Decomposing to obtain hidden image same-frequency subgraph S (i，j) The four sub-bands comprise a low-frequency sub-band L, a horizontal sub-band H, a vertical sub-band V and a diagonal sub-band D;
l (x, y) represents a hidden image same-frequency subgraph S (i，j) H (x, y) represents a hidden image co-frequency sub-pattern S (i，j) The wavelet coefficient value of (x, y) position in the horizontal sub-band H of (1), V (x, y) represents the hidden image same-frequency sub-pattern S (i，j) The wavelet coefficient value of (x, y) position in the vertical sub-band V of (D, x, y) represents the same-frequency sub-image S of the hidden image (i ，j) The wavelet coefficient value at the (x, y) position in the diagonal sub-band D of (1) x ≦ He,1 ≦ y ≦ Wi, he represents the height of any one of the four sub-bands, and Wi represents the width of any one of the four sub-bands;
a3.2: respectively in hidden image same frequency sub-image S (i，j) The minimum local variance solution is performed in the horizontal subband H, the vertical subband V, and the diagonal subband D: obtaining the local variance of each wavelet coefficient of the same-frequency subgraph of the carrier image by utilizing the maximum posterior estimation of 4 square dxd neighborhoods on a horizontal sub-band H, a vertical sub-band V and a diagonal sub-band D by utilizing a formula (5);
wherein
Representing the local variance of (x, y) position in the horizontal sub-band H of the same-frequency subgraph of the carrier image;
representing the local variance of the (x, y) position in the vertical sub-band V of the same-frequency sub-picture of the carrier image,
representing the local variance of (x, y) positions in the diagonal sub-band D of the same-frequency subgraph of the carrier image;
representing the variance of the steganographic noise,
represents the mean of the squares of the wavelet coefficients in a square d x d neighborhood centered at the (x, y) position in the horizontal subband H,
represents the mean of the squares of the wavelet coefficients in a square d x d neighborhood centered at the (x, y) position in the vertical subband V,
represents the mean of the squares of the wavelet coefficients in a square D x D neighborhood centered at the (x, y) position in the diagonal subband D; d belongs to {3,5,7,9};
a3.3: wavelet coefficients of a horizontal sub-band H, a vertical sub-band V and a diagonal sub-band D are subjected to low-pass filtering by a wiener filter:
H low (x, y) denotes the low-pass filtered horizontal sub-band H low Estimated coefficient values for the (x, y) position;
V low (x, y) denotes the low-pass filtered vertical sub-band V low The estimated coefficient value of the (x, y) position;
D low (x, y) denotes the low-pass filtered diagonal sub-band D low The estimated coefficient value of the (x, y) position;
a3.4: low frequency sub-band L and low pass filtered horizontal sub-band H low Low pass filtered vertical sub-band V low Low pass filtered diagonal sub-band D low And performing inverse wavelet transform to obtain an estimated DCT coefficient in the carrier image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911344997.1A CN111047497B (en) | 2019-12-24 | 2019-12-24 | JPEG image steganography information positioning method based on same-frequency sub-image filtering |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911344997.1A CN111047497B (en) | 2019-12-24 | 2019-12-24 | JPEG image steganography information positioning method based on same-frequency sub-image filtering |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111047497A CN111047497A (en) | 2020-04-21 |
| CN111047497B true CN111047497B (en) | 2022-10-04 |
Family
ID=70238791
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911344997.1A Active CN111047497B (en) | 2019-12-24 | 2019-12-24 | JPEG image steganography information positioning method based on same-frequency sub-image filtering |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111047497B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114170240B (en) * | 2021-11-12 | 2024-02-27 | 杭州电子科技大学 | JPEG image load positioning method based on weighted coefficient residual error |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105719225B (en) * | 2015-12-31 | 2018-12-11 | 杨春芳 | A kind of key recovery method of the LSB Matching steganography based on small echo absolute moment |
| CN110136074B (en) * | 2019-03-26 | 2020-12-08 | 中国人民解放军战略支援部队信息工程大学 | High dynamic range image hidden pixel ratio estimation method based on least square method |
-
2019
- 2019-12-24 CN CN201911344997.1A patent/CN111047497B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111047497A (en) | 2020-04-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Al-Qershi et al. | Two-dimensional difference expansion (2D-DE) scheme with a characteristics-based threshold | |
| Al-Mansoori et al. | Robust watermarking technique based on DCT to protect the ownership of DubaiSat-1 images against attacks | |
| Laishram et al. | A survey on digital image steganography: current trends and challenges | |
| Ahmad et al. | 3-level DWT Image watermarking against frequency and geometrical attacks | |
| Khalifa et al. | A robust non-blind algorithm for watermarking color images using multi-resolution wavelet decomposition | |
| Dejey et al. | Robust discrete wavelet–fan beam transforms-based colour image watermarking | |
| CN111047497B (en) | JPEG image steganography information positioning method based on same-frequency sub-image filtering | |
| Dalal et al. | A secure and robust video steganography scheme for covert communication in H. 264/AVC | |
| Hamad et al. | A blind high-capacity wavelet-based steganography technique for hiding images into other images | |
| Al-Qershi et al. | Reversible watermarking scheme based on two-dimensional difference expansion (2D-DE) | |
| Sharma et al. | Robust technique for steganography on Red component using 3-DWT-DCT transform | |
| Shanthakumari et al. | Video Steganography using LSB matching revisited algorithm | |
| Chang et al. | Reversible Image Hiding for High Image Quality Based on Histogram Shifting and Local Complexity. | |
| Tataru et al. | Improved blind DCT watermarking by using chaotic sequences | |
| Goswami et al. | Hybrid DCT-DWT Digital Image Steganography | |
| Maheshwari et al. | Robust multi-modal watermarking using visually encrypted watermark | |
| Yang et al. | High‐Quality and Robust Reversible Data Hiding by Coefficient Shifting Algorithm | |
| Khalili | A novel effective, secure and robust CDMA digital image watermarking in YUV color space using DWT2 | |
| CN114170240B (en) | JPEG image load positioning method based on weighted coefficient residual error | |
| El-Shahed et al. | Video Steganography using 3D Stationary Wavelet Transform | |
| Kumar et al. | Genetic Algorithm based Color Image Steganogaphy using Integer Wavelet Transform and Optimal Pixel Adjustment Process | |
| Ibrahim et al. | A robust image watermarking technique based on image interlacing | |
| Khalili et al. | Non Correlation DWT Based Watermarking Behavior in Different Color Spaces | |
| Wu et al. | Saturation Adjustment Scheme of Blind Color Watermarking for Secret Text Hiding. | |
| Liao | A multiple watermarking scheme for color images |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20201022 Address after: 450001 No. 62 science Avenue, hi tech Zone, Henan, Zhengzhou Applicant after: Information Engineering University of Strategic Support Force,PLA Address before: 405, 450001, 62, science Avenue, Zhengzhou hi tech Development Zone, Henan, China Applicant before: Yang Chunfang |
|
| TA01 | Transfer of patent application right | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |