CN105721875B - A kind of video motion vector Stego-detection method based on entropy - Google Patents
A kind of video motion vector Stego-detection method based on entropy Download PDFInfo
- Publication number
- CN105721875B CN105721875B CN201610066493.8A CN201610066493A CN105721875B CN 105721875 B CN105721875 B CN 105721875B CN 201610066493 A CN201610066493 A CN 201610066493A CN 105721875 B CN105721875 B CN 105721875B
- Authority
- CN
- China
- Prior art keywords
- motion vector
- entropy
- sliding window
- stego
- video
- 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
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
- H04N19/46—Embedding additional information in the video signal during the compression process
- H04N19/467—Embedding additional information in the video signal during the compression process characterised by the embedded information being invisible, e.g. watermarking
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N17/00—Diagnosis, testing or measuring for television systems or their details
Abstract
The video motion vector Stego-detection method based on entropy that the invention discloses a kind of；This method chooses several motion vectors in regional area using sliding window, calculates the motion vector of selection in the entropy of the respective minimum 4bit of tetra- variables of horizontal component H, vertical component V, direction D and length L, obtains 16 dimension entropy in a sliding window；By the mobile sliding window in video frame, multigroup entropy is obtained, 16 dimension entropy mean values are calculated, as steganalysis feature.In this way, " entropy increase " exception that video motion vector steganography is brought can be captured, the Stego-detection of motion vector is realized.
Description
Technical field
The present invention relates to computer information safety technique field more particularly to a kind of video motion vector steganography based on entropy
Detection method.
Background technology
It is close as the Information Hiding Techniques of carrier using digital video with the development of Information Hiding in Digital Image and detection technique
It is equally quickly grown over year.In video Steganography, occurred one after another based on intra prediction, frequency domain and motion vector steganographic algorithm.
In numerous steganography domains, the steganographic algorithm based on motion vector has hidden capacity big, the features such as distortion is small.Therefore, it moves
The detection of vector steganography and steganography becomes research hotspot in video information hiding field.
In terms of hidden method, distortion caused by hidden algorithm reduces steganography using a variety of strategies：Selection movement arrow
The component that absolute value is larger in amount carries out steganography；Motion vector is carried out by the macro block for selecting reconstructed block and reference block to differ greatly
Steganography；Selection low-cost channel is encoded using l Water Paper to be embedded in into row information；It is be combined with each other, is realized embedding using STC and l Water Paper coding
Enter cost minimization.Algorithm concealment is become better and better, but algorithm coding is generally a volume with motion vector used in single frames
Code unit, has not yet been reached full video macro block minimum cost, and do not consider the Preserving problems of motion vector correlation.And due to the use of
One or more codings, embedded mobile GIS is more complex, is not easy to realize.
In face of all kinds of steganographic algorithms, the steganalysis detection of motion vector also begins to grow up：Use adjacent difference phase
Closing property construction steganalysis feature carries out Stego-detection；Use correlation detection motion vector steganography of multiple frames on space-time；
It is directed toward optimal reference block characteristic using motion vector and carries out steganalysis, after motion vector is adjusted, under macro block optimum probability
Drop.
Motion vector stego-detecting algorithm Main Basiss motion vector correlation and local optimality at present, are deposited both ways
It is insufficient：(1) all kinds of adjacent probability construction detection features are mostly based on using the Stego-detection method of adjacent correlation, there is not yet
Latent structure method based on comentropy；(2) Stego-detection method based on motion vector local optimum is needed by there is residual error
Macroblock reconstruction after carry out optimum probability statistics, when macroblock residuals be not present or the steganographic algorithm smaller macro block of selection residual error into
Such method detectability is weaker when row steganography.
Invention content
The video motion vector Stego-detection method based on entropy that the object of the present invention is to provide a kind of.This method is using sliding
Window chooses several motion vectors in regional area, calculates the motion vector of selection in horizontal component H, vertical component V, direction D,
With the entropy of the respective minimum 4bit of tetra- variables of length L, 16 dimension entropy in a sliding window are obtained.By being moved in video frame
Dynamic sliding window, obtains multigroup entropy, 16 dimension entropy mean values is calculated, as steganalysis feature.In this way, can capture
" the entropy increase " that video motion vector steganography is brought is abnormal, realizes the Stego-detection of motion vector.
A kind of video motion vector Stego-detection method based on entropy, includes the following steps：
Step 1, tester gets out the non-steganography samples of cover, and steganography insertion is carried out on cover samples, obtains pair
The stego steganography samples answered；
Step 2, label is added per frame HVDL features, and to cover samples and stego samples in extraction video, obtains equity
Cover and stego training sample features；The HVDL features are 16 Wei Te being calculated according to motion vector (H, V)
Sign；Wherein H indicates that horizontal component, V indicate vertical component, and D is the direction of motion vector, and L is the length of motion vector；
The extracting method includes the following steps：
Step 2.1, a video frame for including motion vector is decoded, the motion vector number of each macro block in the frame is obtained
Value；Video frame is converted into the combination of the macro block and its macroblock motion vector (H, V) of each size dimension at this time；
Step 2.2, to step 2.1 obtain include motion vector video frame, choose sliding window；Fortune in sliding window
Dynamic vector more than one regards a two-dimentional variable (H, V) as；By motion vector (H, V), this 2 dimension variable is extended to the change of 16 dimensions
Amount, and the 16 dimension variable entropy in calculation window, finally move sliding window and calculate multigroup entropy；
The step 2.2 includes the following steps：
Step 2.2.1 sets a size as the sliding window of m × n, and wherein m and n are positive integer；Wherein, m is to slide
Dynamic window height represents m times of maximum macro block height；N is the width of sliding window, and it is wide as maximum macro block to represent the width
N times of degree；
Step 2.2.2, for the motion vector (H, V) of macro block in sliding window, by the H components, the V that calculate motion vector
2 variables are converted to 16 variables by the respective minimum 4bit of component, direction D and length L：
Wherein, Q is quantization function, by taking H components as an example,T is represented to be taken t-th reciprocal of H
The quantization method of bit, V, D, L are identical；α is the angle that motion vector is formed with horizontal right direction, α ∈ [0,2
π), therefore D is that α is divided into corresponding 0 to 15 numerical value after 16 deciles；The rounding for representing motion vector is long
Degree；
Step 2.2.3 regards motion vector in this sliding window as each motion vector in sliding window
The observation of (H, V) variable；Based on step 2.2.2, the observation of 16 dimension variables, each variable-value 0 or 1 have been obtained；This 16
The entropy E of a variable is：
E=- (P0log2P0+P1log2P1)
Wherein, P0The probability that i.e. variable is 0, P1I.e. variable is 1 probability；
One group of 16 dimension entropy can be calculated in step 2.2.4, each sliding window；Cumulative calculation sliding window quantity,
And it is accumulative per one-dimensional entropy；
Step 2.2.5, if having scanned through this video frame, jump procedure 2.3；Otherwise, by one sub-level of sliding window
Or vertically move a maximum macro block unit, can obtain one be scanned the new sliding window of window not exclusively repeatedly before this
Mouthful, execute step 2.2.3；
Step 2.3, according to the accumulative sliding window quantity in step 2.2.5, and its sum of the accumulation of each dimension entropy, to multigroup
Entropy data are averaged, and obtain the respective entropy mean value of 16 dimensions, and export it as feature；If also subsequent video frame is returned
To step 2.1, if without subsequent video frame, feature extraction is completed；
Step 3, using SVM or other graders, the training of feature is carried out to the feature of extraction, obtains training pattern；
Step 4, for any one given video to be measured, the same method using step 2 extracts HVDL latent structures
Feature is detected, by the training pattern that training obtains in step 3, the prediction of steganography is made whether to obtained every frame feature, is provided
Testing result.
Further, in the step 2.2.1, the m=n=3.
Further, the maximum macro block height and maximum macro block width are 16 pixels.
The beneficial effects of the invention are as follows：A kind of video motion vector Stego-detection method based on entropy；Using sliding window
Several motion vectors in regional area are chosen, calculate the motion vector of selection in horizontal component H, vertical component V, direction D, and length
The entropy for spending the respective minimum 4bit of L tetra- variables, obtains 16 dimension entropy in a sliding window.It is slided by mobile in video frame
Dynamic window, obtains multigroup entropy, 16 dimension entropy mean values is calculated, as steganalysis feature.In this way, video can be captured
" the entropy increase " that motion vector steganography is brought is abnormal, realizes the Stego-detection of motion vector.
Description of the drawings
Fig. 1 is the motion vector Stego-detection flow of the present invention；
Fig. 2 is the HVDL feature extraction flows of the present invention.
Specific implementation mode
Such as Fig. 1, a kind of video motion vector Stego-detection method based on entropy includes the following steps：
Step 1, tester gets out the non-steganography samples of cover, and steganography insertion is carried out on cover samples, obtains pair
The stego steganography samples answered；
Step 2, label is added per frame HVDL features, and to cover samples and stego samples in extraction video, obtains equity
Cover and stego training sample features；
Such as Fig. 2, the extracting method includes the following steps：
Step 2.1, a video frame for including motion vector is decoded, the motion vector number of each macro block in the frame is obtained
Value；Video frame is converted into the combination of the macro block and its macroblock motion vector (H, V) of each size dimension at this time；
Step 2.2, to step 2.1 obtain include motion vector video frame, choose sliding window；Fortune in sliding window
Dynamic vector more than one regards a two-dimentional variable (H, V) as；By motion vector (H, V), this 2 dimension variable is extended to the change of 16 dimensions
Amount, and the 16 dimension variable entropy in calculation window, finally move sliding window and calculate multigroup entropy；
The step 2.2 includes the following steps：
Step 2.2.1 sets a size as the sliding window of m × n, and wherein m and n are positive integer；Wherein, m is to slide
Dynamic window height represents m times of maximum macro block height；N is the width of sliding window, and it is wide as maximum macro block to represent the width
N times of degree；
In the step 2.2.1, the m=n=3,
The maximum macro block height and maximum macro block width are 16 pixels.
Step 2.2.2, for the motion vector (H, V) of macro block in sliding window, by the H components, the V that calculate motion vector
2 variables are converted to 16 variables by component, direction D and the minimum 4bit of length L：
Wherein, Q is quantization function, by taking H components as an example,T is represented to be taken t-th reciprocal of H
The quantization method of bit, V, D, L are identical；α is the angle that motion vector is formed with horizontal right direction, α ∈ [0,2
π), therefore D is that α is divided into corresponding 0 to 15 numerical value after 16 deciles；The rounding for representing motion vector is long
Degree；
Step 2.2.3 regards motion vector in this sliding window as each motion vector in sliding window
The observation of (H, V) variable；Based on step 2.2.2, the observation of 16 dimension variables, each variable-value 0 or 1 have been obtained；This 16
The entropy E of a variable is：
E=- (P0log2P0+P1log2P1)
Wherein, P0The probability that i.e. variable is 0, P1I.e. variable is 1 probability；
One group of 16 dimension entropy can be calculated in step 2.2.4, each sliding window；Cumulative calculation sliding window quantity,
And it is accumulative per one-dimensional entropy；
Step 2.2.5, if having scanned through this video frame, jump procedure 3；Otherwise, by one sub-level of sliding window or
Vertically move a maximum macro block unit, can obtain one be scanned the new sliding window of window not exclusively repeatedly before this
Mouthful, execute step 2.2.3.
Step 2.3, according to the accumulative sliding window quantity in step 2.2.5, and its sum of the accumulation of each dimension entropy, to multigroup
Entropy data are averaged, and obtain 16 dimension entropy mean values, and export it as feature；If also subsequent video frame returns to step
2.1, if without subsequent video frame, complete feature extraction.
Step 3, using SVM or other graders, the training of feature is carried out to the feature of extraction, obtains training pattern；
Step 4, for any one given video to be measured, the same method using step 2 extracts HVDL latent structures
Feature is detected, by the training pattern that training obtains in step 3, the prediction of steganography is made whether to obtained every frame feature, is provided
Testing result.
HVDL features：According to 16 dimensional features that motion vector (H, V) is calculated, wherein H indicates that horizontal component, V indicate
Vertical component, D are the directions of motion vector, and L is the length of motion vector.
Claims (3)
1. a kind of video motion vector Stego-detection method based on entropy, it is characterised in that：Include the following steps：
Step 1, tester gets out the non-steganography samples of cover, and steganography insertion is carried out on cover samples, obtains corresponding
Stego steganography samples；
Step 2, label is added per frame HVDL features, and to cover samples and stego samples in extraction video, obtains equity
Cover and stego training sample features；The HVDL features are 16 dimensional features being calculated according to motion vector (H, V)；
Wherein H indicates that horizontal component, V indicate vertical component, and D is the direction of motion vector, and L is the length of motion vector；
The extracting method includes the following steps：
Step 2.1, a video frame for including motion vector is decoded, the motion vector numerical value of each macro block in the frame is obtained；This
When video frame be converted into each size dimension macro block and its macroblock motion vector (H, V) combination；
Step 2.2, to step 2.1 obtain include motion vector video frame, choose sliding window；Movement arrow in sliding window
More than one is measured, regards a two-dimentional variable (H, V) as；By motion vector (H, V), this 2 dimension variable is extended to 16 dimension variables,
And the 16 dimension variable entropy in calculation window, it finally moves sliding window and calculates multigroup entropy；
The step 2.2 includes the following steps：
Step 2.2.1 sets a size as the sliding window of m × n, and wherein m and n are positive integer；Wherein, m is sliding window
Open height represents m times of maximum macro block height；N is the width of sliding window, represents the width as maximum macro block width
N times；
Step 2.2.2, for the motion vector (H, V) of macro block in sliding window, by calculating the H components of motion vector, V divides
2 variables are converted to 16 variables by the respective minimum 4bit of amount, direction D and length L：
Wherein, Q is quantization function, by taking H components as an example,T represent take H inverse t-th of bit, V,
D, the quantization method of L is identical；The angle that α is formed for motion vector with horizontal right direction, α ∈ [0,2 π), therefore D
It is that α is divided into corresponding 0 to 15 numerical value after 16 deciles；Represent the rounding length of motion vector；
Step 2.2.3, for each motion vector in sliding window, all regard as motion vector in this sliding window (H,
V) the observation of variable；Based on step 2.2.2, the observation of 16 dimension variables, each variable-value 0 or 1 have been obtained；This 16 changes
The entropy E of amount is：
E=- (P0log2P0+P1log2P1)
Wherein, P0The probability that i.e. variable is 0, P1I.e. variable is 1 probability；
One group of 16 dimension entropy can be calculated in step 2.2.4, each sliding window；Cumulative calculation sliding window quantity, and tire out
Meter is per one-dimensional entropy；
Step 2.2.5, if having scanned through this video frame, jump procedure 2.3；Otherwise, by one sub-level of sliding window or hang down
The maximum macro block unit of dynamic one of translation, can obtain one be scanned the new sliding window of window not exclusively repeatedly before this,
Execute step 2.2.3；
Step 2.3, according to the accumulative sliding window quantity in step 2.2.5, and its sum of the accumulation of each dimension entropy, to multigroup entropy
Data are averaged, and obtain the respective entropy mean value of 16 dimensions, and export it as feature；If also subsequent video frame, step is returned to
Rapid 2.1, if without subsequent video frame, complete feature extraction；
Step 3, using SVM or other graders, the training of feature is carried out to the feature of extraction, obtains training pattern；
Step 4, same to be detected using the method extraction HVDL latent structures of step 2 for any one given video to be measured
The training pattern that training obtains in step 3 is made whether obtained every frame feature the prediction of steganography, provides detection by feature
As a result.
2. a kind of video motion vector Stego-detection method based on entropy according to claim 1, it is characterised in that：It is described
Step 2.2.1 in, the m=n=3.
3. a kind of video motion vector Stego-detection method based on entropy according to claim 1, it is characterised in that：It is described
Maximum macro block height and maximum macro block width be 16 pixels.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610066493.8A CN105721875B (en) | 2016-01-30 | 2016-01-30 | A kind of video motion vector Stego-detection method based on entropy |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610066493.8A CN105721875B (en) | 2016-01-30 | 2016-01-30 | A kind of video motion vector Stego-detection method based on entropy |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105721875A CN105721875A (en) | 2016-06-29 |
| CN105721875B true CN105721875B (en) | 2018-11-13 |
Family
ID=56155275
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610066493.8A Active CN105721875B (en) | 2016-01-30 | 2016-01-30 | A kind of video motion vector Stego-detection method based on entropy |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105721875B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106101713B (en) * | 2016-07-06 | 2018-10-09 | 武汉大学 | A kind of video steganalysis method based on the optimal calibration of window |
| CN108271027B (en) * | 2018-01-10 | 2020-06-12 | 中国人民解放军战略支援部队信息工程大学 | Method for extracting image self-adaptive secret information |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102025997A (en) * | 2010-12-22 | 2011-04-20 | 中兴通讯股份有限公司 | Method and device for concealing information as well as method and device for extracting concealed information |
| CN103856786A (en) * | 2012-12-04 | 2014-06-11 | 中山大学深圳研究院 | Streaming media video encryption method and device based on H.264 |
-
2016
- 2016-01-30 CN CN201610066493.8A patent/CN105721875B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102025997A (en) * | 2010-12-22 | 2011-04-20 | 中兴通讯股份有限公司 | Method and device for concealing information as well as method and device for extracting concealed information |
| CN103856786A (en) * | 2012-12-04 | 2014-06-11 | 中山大学深圳研究院 | Streaming media video encryption method and device based on H.264 |
Non-Patent Citations (1)
| Title |
|---|
| 基于相关性异常的H.264/AVC视频运动矢量隐写分析算法;王丽娜等;《电子学报》;20140831;第42卷(第8期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105721875A (en) | 2016-06-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108141605A (en) | Intra block replicates merging patterns and unavailable intra block replicates the filling of reference zone | |
| GB2590844A (en) | Simplified cross component prediction | |
| CN103034853B (en) | A kind of jpeg image general steganalysis method | |
| CN108462874A (en) | Dynamic image encoding device and moving image decoding apparatus | |
| US9773290B2 (en) | Content watermarking | |
| CN108141594A (en) | For being encoded to image or decoded method and apparatus | |
| CN106131546B (en) | A method of determining that HEVC merges and skip coding mode in advance | |
| CN108293125A (en) | A kind of digital image processing system and method | |
| CN108881910A (en) | Decode the device of Saliency maps | |
| CN101478691B (en) | Non-reference evaluation method for Motion Jpeg2000 video objective quality | |
| CN102972028A (en) | New intra prediction modes | |
| CN106507109A (en) | Picture coding device and method, picture decoding apparatus and method and recording medium | |
| CN101946514A (en) | Use the true motion vector of adaptable search scope to estimate | |
| CN102572499B (en) | Based on the non-reference picture quality appraisement method of wavelet transformation multi-resolution prediction | |
| CN106028047B (en) | A method of the fast mode decision and PU of the intra prediction based on Hadamard transformation divide | |
| CN103262539A (en) | Moving image encoding device, moving image decoding device, moving image encoding method and moving image decoding method | |
| CN105721875B (en) | A kind of video motion vector Stego-detection method based on entropy | |
| CN107040786B (en) | A kind of H.265/HEVC video steganalysis method adaptively selected based on space-time characteristic of field | |
| CN102801977A (en) | Method for embedding robust digital watermark in H.264 based on video complexity analysis | |
| CN106331730A (en) | Double-compression detection method by using quantification factor same as H.264 video | |
| CN114651270A (en) | Depth loop filtering by time-deformable convolution | |
| Lin et al. | Saliency-aware spatio-temporal artifact detection for compressed video quality assessment | |
| CN102843576A (en) | Steganography analyzing method aiming at modem-sharing unit (MSU) | |
| CN110324617A (en) | Image processing method and device | |
| CN106791871B (en) | A kind of hiding detection method of motion vector modulation intelligence |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |