CN105959696A - Video content safety monitoring method based on SIFT characteristic algorithm - Google Patents
Video content safety monitoring method based on SIFT characteristic algorithm Download PDFInfo
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- CN105959696A CN105959696A CN201610280276.9A CN201610280276A CN105959696A CN 105959696 A CN105959696 A CN 105959696A CN 201610280276 A CN201610280276 A CN 201610280276A CN 105959696 A CN105959696 A CN 105959696A
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- Prior art keywords
- video
- finger print
- print information
- key frame
- extraction
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- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N5/00—Details of television systems
- H04N5/76—Television signal recording
- H04N5/91—Television signal processing therefor
- H04N5/913—Television signal processing therefor for scrambling ; for copy protection
Abstract
The invention discloses a video content safety monitoring method based on an SIFT characteristic algorithm. A video source sending end firstly extracts a key frame of a video, then fingerprint information extraction is carried out on the key frame, asymmetric encryption is carried out on the extracted fingerprint information, the encrypted fingerprint information and block information form a binary system sequence, and the video and the encrypted fingerprint information are packaged and transmitted to a video source receiving end through a network transmission channel; after the video receiving end receives the video and the encrypted fingerprint information, the key frame of the received video is extracted, and fingerprint information extraction on the key frame is carried out; the received encrypted fingerprint information is decrypted, and video block content characteristics are obtained; and video fingerprint comparison is carried out on the fingerprint information extracted in the video and the video block content characteristics obtained by decryption. By adopting the video content safety monitoring method, the safety of video transmission is substantially improved, and the good robustness of the video quality is realized.
Description
Technical field
The invention belongs to technical field of video processing, be specifically related in a kind of video based on SIFT feature algorithm
Hold monitoring administration method.
Background technology
Along with development and the fusion of computer technology, communication technology and multimedia technology, various multimedias
Application is developed in society's every field rapidly.Increasing people is enjoying the facility that video information is brought
Time, also receive the infringement of the invalid information of various ways simultaneously.Current the Internet have a large amount of based on bright
Civilian and transparent agreement, is highly susceptible to attack in transmission of video, is easily replaced and distorts, robustness
The strongest.Particularly on outlying district and the platform do not supervised, some lawless persons these utilize technology to leak
Hole, have propagated a large amount of terror, starts information, disturbs normal country order.
In conventional art there is bigger difficulty in field of video transmission in such as digital watermark and Hash translation technology.
Watermark is visible or sightless pattern, it is possible to be embedded in video content, the most such as one anti-counterfeiting label
Label are attached on product carry out certified products checking.But watermarking can change video content, may affect audio-visual
Content quality, it is also possible to be cracked.It is additionally present of multiple digital watermark, the most incompatible, as not
Antifalsification label with type can not be by same device authentication.The most important thing is to there is substantial amounts of video content, than
The film such as TV programme, issued with forms such as DVD and VHS video tapes and amusement audio frequency and video, and mutually
Networking audiovisual material does not has watermark.Therefore can not be transmitted by digital watermark.Hash translation technology
It it is a kind of binary file verification technique.The operations such as its editor to video content, shears, compression are the most very
Sensitive.Small file change all can cause the change of cryptographic Hash, thus while cryptographic Hash is reflected in data
Determine and highly useful in completeness check, but it is not appropriate for transmitting audio-video content.
Summary of the invention
It is an object of the invention to: for problem present in above-mentioned prior art, it is provided that a kind of based on
The video content monitoring administration method of SIFT feature algorithm, solves in the Internet, and transmission of video robustness is low,
The problem that video content supervision is not in place.
To achieve these goals, the technical solution used in the present invention is:
A kind of video content monitoring administration method based on SIFT feature algorithm, video source transmitting terminal is receiving
During the video request of video source receiving terminal, it is decoded video processing, sends original video simultaneously and flow to regard
Frequently source receiving terminal；Specifically,
First video source transmitting terminal to video extraction key frame, then carries out the extraction of finger print information to key frame,
Again the finger print information of extraction being carried out asymmetric encryption, finger print information and block message after encryption form binary system
Sequence, is sent fingerprint packing after video and encryption to video source receiving terminal by network transmission channels；
After video receiver receives the finger print information of video and encryption, then the video extraction received is closed
Key frame, and key frame is carried out finger print information extraction；The finger print information of the encryption received is solved simultaneously
Close, obtain video block content characteristic；The finger print information extracted in video and deciphering are obtained video block content special
Levy and carry out video finger print contrast, contrast unsuccessfully, then explanation video is tampered in transmitting procedure, contrasts successfully,
Video is safety in transmitting procedure.
Preferably, video source transmitting terminal is when the transmission of video receiving video receiver is asked, and server is with T
Second be unit, and key frame in the whole video after extracting, it is divided into N*N block for each key frame,
Block message described in formation, video source transmitting terminal server uses sift characteristics algorithm in order to each key frame
Middle N*N block carries out video content features extraction.
Preferably, described video content features extracts, and specifically includes metric space extremum extracting, key point
Location, direction determine and key point description；
Use metric space extremum extracting, search for the picture position on all yardsticks, by gaussian derivative function
Identify potential for yardstick and the point of interest of invariable rotary；
Employing key point positions, and on the position of each candidate, the model fine by matching determines
Position and yardstick.The selection gist of key point is in their degree of stability；
Employing direction determines, gradient direction based on image local, distribute to one, each key point position or
Multiple directions.All below view data is operated the direction both relative to key point, yardstick and position
Convert, thus the invariance converted for these is provided；
Employing key point describes, and in the neighborhood around each key point, measures image on selected yardstick
The gradient of local.These gradients are transformed into a kind of expression, and this expression allows bigger local shape
Deformation and illumination variation.
Preferably, at the video content features rivest, shamir, adelman that sift characteristics algorithm will be used to obtain
Reason.
Preferably, the finger print information after encryption and block message are carried out quantification treatment, and comes with binary sequence
Represent.
Then preferably, video reception terminal will receive decoding video stream and be cached, the band to be received such as
There is the binary sequence file of encrypted video finger print information.
Preferably, after the binary file that video reception terminal receives with encrypted video finger print information,
First file is decrypted process, then extracts the finger print information in binary file, and video will be entered
Row takes out frame and extraction fingerprint, and the finger print information contrasting both is the most consistent.
Owing to have employed technique scheme, the invention has the beneficial effects as follows:
A kind of based on SIFT feature algorithm the video content monitoring administration method of the present invention, uses SIFT special
Levying a fingerprint, video before transmission, first passes through SIFT algorithm and extracts the characteristic point of the key frame in video, and
Generate the set of vectors of a characteristic point, the then characteristic point set of N number of frame before and after contrast, remove characteristic point
Similar set.Then remaining I frame picture is carried out piecemeal, then extracts the characteristic point set of vectors of each piece,
Preserve into binary form sequentially, be the finger print information of transmission video.First server transmits and refers to
Stricture of vagina information, transmits video the most again and identifies video according to finger print information to terminal, terminal.Relative to
Digital watermark and Hash translation technology, will not change video content, effective to historical content, after contents processing
Keep stable, can identification division content.The method can be greatly improved the safety of transmission of video, for regarding
Frequently quality has extraordinary robustness.
Accompanying drawing explanation
Fig. 1 is the video content monitoring administration method flow chart of the present invention.
Fig. 2 is the SIFT feature point diagram of the present invention.
Fig. 3 is the DOG spatial extrema detection figure of the present invention.
Fig. 4 is the crucial point diagram of the present invention.
Fig. 5 is the fingerprint matching schematic diagram of the present invention.
Detailed description of the invention
As Figure 1-5, the present invention is video content monitoring administration method based on sift characteristics algorithm,
A kind of video content monitoring administration method based on SIFT feature algorithm, video source transmitting terminal is receiving
During the video request of video source receiving terminal, it is decoded video processing, sends original video simultaneously and flow to regard
Frequently source receiving terminal.Video source transmitting terminal, first to video extraction key frame, then carries out fingerprint letter to key frame
The extraction of breath, then the finger print information of extraction is carried out asymmetric encryption, the finger print information after encryption and block message
Form binary sequence, fingerprint packing after video and encryption is connect to video source by network transmission channels transmission
Receiving end.After video receiver receives the finger print information of video and encryption, then to the video extraction received
Key frame, and key frame is carried out finger print information extraction；The finger print information of the encryption received is carried out simultaneously
Deciphering, obtains video block content characteristic；The finger print information extracted in video and deciphering are obtained video block content
Feature carries out video finger print contrast, contrasts unsuccessfully, then explanation video is tampered in transmitting procedure, contrasts into
Merit, video is safety in transmitting procedure.
Video source transmitting terminal is when the transmission of video receiving video receiver is asked, and server is with the T second as list
Position, and by key frame in the whole video after extraction, be divided into N*N block for each key frame, formed
Described block message, video source transmitting terminal server uses sift characteristics algorithm in order in each key frame
N*N block carries out video content features extraction.
Video content features extracts, and specifically includes metric space extremum extracting, key point location, direction determines
Describe with key point.Use metric space extremum extracting, search for the picture position on all yardsticks, by height
This differentiation function identifies potential for yardstick and the point of interest of invariable rotary；Employing key point positions,
On the position of each candidate, the model fine by matching determines position and yardstick.The choosing of key point
Select the degree of stability being dependent on them；Employing direction determines, gradient direction based on image local, distributes to
One or more direction, each key point position.All operations to view data below are both relative to key
Direction, yardstick and the position of point convert, thus provide the invariance converted for these；Use key
Point describes, and in the neighborhood around each key point, measures the gradient of image local on selected yardstick.
These gradients are transformed into a kind of expression, and this expression allows the deformation of bigger local shape and illumination to become
Change.
As preferably technical scheme, the video content features using sift characteristics algorithm to obtain is added with asymmetric
Close algorithm processes.Finger print information after encryption and block message are carried out quantification treatment, and uses binary system sequence
Row represent.
Video reception terminal will receive decoding video stream and be cached, and then etc. to be received regard with encryption
Frequently the binary sequence file of finger print information.
After the binary file that video reception terminal receives with encrypted video finger print information, first to literary composition
Part is decrypted process, then extracts the finger print information in binary file, and video will be taken out frame and
Extraction fingerprint, the finger print information contrasting both is the most consistent.
Video content monitoring administration method specifically includes following steps:
Video source transmitting terminal receives the transmission of video request of terminal, while being decoded video processing,
Send original video and flow to terminal.Transmitting terminal server, in units of the T second, extracts key frame, and one by one
Key frame is divided into N*N block, choose simultaneously key frame in each piece (in the present invention, T is 10 seconds,
Video length is 2 minutes, is divided into 12 blocks, extracts key frame every 1 second).Server uses sift algorithm
Carry out video content features extraction to each piece in order, including metric space extremum extracting, key point location,
Key point describes, direction determines.Wherein:
1) metric space extremum extracting: search for the picture position on all yardsticks.Known by gaussian derivative function
Not potential for yardstick and the point of interest of invariable rotary.In Image Information Processing model, introduce one regarded
For the parameter of yardstick, represent sequence by consecutive variations scale parameter acquisition metric space under multiple dimensioned, right
These sequences carry out the extraction of metric space main outline, and using this main outline as a feature vectors, it is achieved
Feature extraction etc. on edge, Corner Detection and different resolution.Review on Scale Space Method is by traditional single scale
Image Information Processing technology is included in the dynamic analytical framework that yardstick is continually changing, it is easier to obtain the basis of image
Matter feature.In metric space, the fog-level of each scalogram picture becomes larger, it is possible to people is at distance objective in simulation
Target forming process on the retina time from the near to the remote.Metric space meets vision invariance.This invariance
Visual explanation as follows: on the one hand when we are with eye observation object, when the illumination bar of background residing for object
During part change, the luminance level of retina perceptual image and contrast are different, therefore it is required that metric space
The analysis of image is not changed by operator by the grey level of image and contrast to be affected, and i.e. meets gray scale constant
Property and contrast invariance.On the other hand, relative to a certain fixed coordinate system, between observer and object
Relative change in location time, the position of image, size, angle and the shape of the perception of retina institute are different,
Therefore it is required that metric space operator is to the analysis of image and the position of image, size, angle and affine transformation
Unrelated, i.e. meet translation invariance, scale invariability, euclidean invariance and affine-invariant features.
2) key point location: on the position of each candidate, the model fine by matching determines position
And yardstick.The selection gist of key point is in their degree of stability.Come accurately by matching three-dimensional quadratic function
Determine position and the yardstick of key point, remove the key point of low contrast and unstable skirt response point simultaneously
(because DoG operator can produce stronger skirt response), to strengthen coupling stability, to improve noise resisting ability.
3) direction determines: gradient direction based on image local, distributes to each key point position one or more
Direction.All operate the view data direction both relative to key point, yardstick and positions below are carried out
Conversion, thus the invariance converted for these is provided.In order to make descriptor have rotational invariance, need
Utilize the local feature of image for distributing a reference direction to each key point.Use the side of image gradient
Method asks for the stabilising direction of partial structurtes.For the key point point detected in DOG pyramid, gather it
The gradient of pixel and directional spreding feature in the gaussian pyramid image 3 σ window of place.
4) key point describes: in the neighborhood around each key point, measures image local on selected yardstick
Gradient.These gradients are transformed into a kind of expression, and this expression allows the deformation of bigger local shape
And illumination variation.For each key point, have three information: position, yardstick and direction.Shown in
First coordinate axes is rotated the direction as key point, to guarantee rotational invariance.Take centered by characteristic point
Bag, as sampling window, is included into after the relative direction of sampled point Yu characteristic point being weighted by Gauss in the field of 8*8
Containing 8 direction histograms, 32 dimensional features finally obtaining 2*2*8 describe.Next it is exactly for each key
Point sets up a descriptor, describes out by this key point with one group of vector so that it is not with various changes
Change, such as illumination variation, visual angle change etc..This describes son and not only includes key point, also comprises pass
To its contributive pixel around key point, and descriptor should have higher uniqueness, in order to improves
The probability that characteristic point is correctly mated.
Can be had by sift algorithm and quickly form video finger print information, use asymmetric encryption to calculate finger print information
Method is encrypted, and the video finger print information after encryption is formed binary sequence file and is transmitted by network.
Terminal will receive decoding video stream and be cached, and then etc. to be received enter with the two of encrypted video fingerprint
File processed.After receiving the binary file with video finger print, first the encrypted video received is referred to
Stricture of vagina is decrypted process, then video is carried out fingerprint extraction, by the video finger print of contrast block message reduction
The finger print information obtained with deciphering, it is judged that video is the most maliciously distorted in transmitting procedure.
Claims (7)
1. a video content monitoring administration method based on SIFT feature algorithm, it is characterised in that video source
Video, when receiving the video request of video source receiving terminal, is decoded processing, sends simultaneously by transmitting terminal
Original video flows to video source receiving terminal；Specifically,
First video source transmitting terminal to video extraction key frame, then carries out the extraction of finger print information to key frame,
Again the finger print information of extraction being carried out asymmetric encryption, finger print information and block message after encryption form binary system
Sequence, is sent fingerprint packing after video and encryption to video source receiving terminal by network transmission channels；
After video receiver receives the finger print information of video and encryption, then the video extraction received is closed
Key frame, and key frame is carried out finger print information extraction；The finger print information of the encryption received is solved simultaneously
Close, obtain video block content characteristic；The finger print information extracted in video and deciphering are obtained video block content special
Levy and carry out video finger print contrast, contrast unsuccessfully, then explanation video is tampered in transmitting procedure, contrasts successfully,
Video is safety in transmitting procedure.
Video content monitoring administration method based on SIFT feature algorithm the most according to claim 1, its
Being characterised by, video source transmitting terminal is when the transmission of video receiving video receiver is asked, and server is with T
Second be unit, and key frame in the whole video after extracting, it is divided into N*N block for each key frame,
Block message described in formation, video source transmitting terminal server uses sift characteristics algorithm in order to each key frame
Middle N*N block carries out video content features extraction.
Video content monitoring administration method based on SIFT feature algorithm the most according to claim 1, its
Being characterised by, described video content features extracts, and specifically includes metric space extremum extracting, key point fixed
Position, direction determine and key point description；
Use metric space extremum extracting, search for the picture position on all yardsticks, by gaussian derivative function
Identify potential for yardstick and the point of interest of invariable rotary；
Employing key point positions, and on the position of each candidate, the model fine by matching determines
Position and yardstick.The selection gist of key point is in their degree of stability；
Employing direction determines, gradient direction based on image local, distribute to one, each key point position or
Multiple directions.All below view data is operated the direction both relative to key point, yardstick and position
Convert, thus the invariance converted for these is provided；
Employing key point describes, and in the neighborhood around each key point, measures image on selected yardstick
The gradient of local.These gradients are transformed into a kind of expression, and this expression allows bigger local shape
Deformation and illumination variation.
Video content monitoring administration method based on SIFT feature algorithm the most according to claim 1, its
It is characterised by, at the video content features rivest, shamir, adelman that sift characteristics algorithm will be used to obtain
Reason.
Video content monitoring administration method based on SIFT feature algorithm the most according to claim 1, its
It is characterised by, the finger print information after encryption and block message is carried out quantification treatment, and carrys out table with binary sequence
Show.
Video content monitoring administration method based on SIFT feature algorithm the most according to claim 1, its
Being characterised by, video reception terminal will receive decoding video stream and be cached, then etc. to be received with
The binary sequence file of encrypted video finger print information.
Video content monitoring administration method based on SIFT feature algorithm the most according to claim 1, its
It is characterised by, after the binary file that video reception terminal receives with encrypted video finger print information, first
First file is decrypted process, then extracts the finger print information in binary file, and video will be carried out
Taking out frame and extraction fingerprint, the finger print information contrasting both is the most consistent.
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| CN108777688A (en) * | 2018-06-07 | 2018-11-09 | 中国联合网络通信集团有限公司 | Video security monitoring method and system |
| CN109063428A (en) * | 2018-06-27 | 2018-12-21 | 武汉大学深圳研究院 | A kind of altering detecting method and its system of digital cartoon |
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| CN111565331A (en) * | 2020-04-10 | 2020-08-21 | 苏州鑫竹智能建筑科技有限公司 | Optimization method for wireless transmission of video image data |
| CN111565331B (en) * | 2020-04-10 | 2022-05-31 | 苏州鑫竹智能建筑科技有限公司 | Optimization method for wireless transmission of video image data |
| CN114374851A (en) * | 2022-01-13 | 2022-04-19 | 中国铁道科学研究院集团有限公司电子计算技术研究所 | High-speed rail fusion media playing control method, system and equipment and high-speed rail playing equipment |
| WO2024066123A1 (en) * | 2022-09-28 | 2024-04-04 | Guangdong Oppo Mobile Telecommunications Corp., Ltd. | Lossless hierarchical coding of image feature attributes |
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