CN110348464A - A kind of image forge detection algorithm based on more support area local luminance sequences - Google Patents

A kind of image forge detection algorithm based on more support area local luminance sequences Download PDF

Info

Publication number
CN110348464A
CN110348464A CN201910647407.6A CN201910647407A CN110348464A CN 110348464 A CN110348464 A CN 110348464A CN 201910647407 A CN201910647407 A CN 201910647407A CN 110348464 A CN110348464 A CN 110348464A
Authority
CN
China
Prior art keywords
support area
characteristic
image
liop
characteristic point
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.)
Pending
Application number
CN201910647407.6A
Other languages
Chinese (zh)
Inventor
颜普
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Anhui Jianzhu University
Anhui University of Architecture
Original Assignee
Anhui University of Architecture
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Anhui University of Architecture filed Critical Anhui University of Architecture
Priority to CN201910647407.6A priority Critical patent/CN110348464A/en
Publication of CN110348464A publication Critical patent/CN110348464A/en
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • 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
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G06V10/443—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components by matching or filtering

Abstract

The invention discloses a kind of image forge detection algorithms based on more support area local luminance sequences, belong to image forge detection technique field, the detection including S1, characteristic area determines characteristic point；The transformation of S2, characteristic area；The building of LIOP description in S3, each characteristic area；S4, characteristic matching；S5, characteristic point are sorted out；S6, geometric transformation estimation；S7, detection are completed.Son is described by using LIOP and is divided using global brightness ordered pair support area, this division does not need to calculate the principal direction of support area, not only save calculation amount, and it can theoretically guarantee that constructing description has real rotational invariance and monotonic luminance invariance, while obtaining multiple support areas of different scale, different resolution and different directions using NSCT to improve the taste of LIOP description.To improve the robust of image forge Region detection algorithms.

Description

A kind of image forge detection algorithm based on more support area local luminance sequences
Technical field
It is the present invention relates to image forge detection technique field, in particular to a kind of based on more support area local luminance sequences Image forge detection algorithm.
Background technique
Digital picture has very important role in current communication process.With digital imaging processing software and modification The development of equipment, digital picture can easily be tampered without leave it is any significantly distort trace, even if layman Image editing tools (such as Photoshop) can be readily available to modify existing image.The quantity of image operation control and forgery Also in rapid growth, this judges that the originality of piece image and accuracy bring great puzzlement to people, especially to judgement department The true and false in method identification as evidence image is more challenging.Therefore, qualification figure seem it is no be forged be it is vital, Scene of a crime exploration, judicial expertise and many other fields can be widely used in.
Image forge detection technique refers to can be confirmed the credible of original image in the case where no any priori knowledge Degree.Common digital picture, which forges method, resampling, splicing, copy-paste etc., and wherein copy-paste forgery is most simple With the most common digital image tampering method.Copy-paste forgery refers to that the region of arbitrary shape and size is replicated in image Be then placed within another region of image, the purpose is to by enhance image visual effect come cover image significant data or Influence the real goal of identification image.Since replication region is in same image, essential attribute such as noise, color, texture Deng all consistent with general image, this causes discrimination process extremely troublesome.Replication region will do it certain ruler before stickup simultaneously Degree and rotation process, this, which makes detection forge region, has certain challenge.
The image copy-paste counterfeiting detection method based on characteristic point is increasingly taken seriously in recent years, and such methods are being permitted Stronger convincingness is proven to have under more image transformation.Image copy-paste counterfeiting detection method based on characteristic point is main Consider characteristic point region and indicate suspicious region using Feature Descriptor, common Feature Descriptor has Scale invariant special Sign transformation and acceleration robust features.However these methods in order to make building Feature Descriptor have rotational invariance, it is necessary to count It calculates the principal direction of support area and is corrected support area according to the direction, the calculating that this not only considerably increases algorithm is multiple Miscellaneous degree, and inevitably generate error.In addition, these methods make the Feature Descriptor of building using normalization operation There is certain invariance to linear luminance variation, but non-linear brightness variation issue cannot be handled well (as dullness is bright Degree variation).
Summary of the invention
The object of the invention is that in order to solve above-mentioned image forge detection algorithm calculate it is complicated, be easy to produce error with And the problem of non-linear brightness changes cannot be handled well and a kind of image based on more support area local luminance sequences is provided Detection algorithm is forged, having reduces calculation amount, the advantages of reducing error, solve the problems, such as luminance transformation.
The present invention is achieved through the following technical solutions above-mentioned purpose, a kind of figure based on more support area local luminance sequences As forging detection algorithm, comprising the following steps:
The detection of S1, characteristic area determine characteristic point: eliminating the influence of noise first with Gaussian filter, recycle maximum Stable extremal region (MSER) algorithm extracts the maximum stable extremal region of image as support area, and the center of support area Point is characterized a little；
The transformation of S2, characteristic area: the spatial information of detection zone is obtained, is obtained using non-sample Contourlet transformation More support areas of different scale, different resolution and different directions；
The building of LIOP description in S3, each characteristic area: non-drop is carried out to brightness values all in each support area Sequence, is equally spaced divided into B sub-regions according to brightness value size for each support area, by superposition each subregion The LIOP value of all pixels point obtains description of each subregion, obtains each by description for all subregions of connecting Hold final LIOP description in region；
S4, characteristic matching: LIOP description in two neighboring characteristic point is carried out using two-way Euclidean distance ratio method Matching obtains one group of characteristic matching pair if matching；
S5, characteristic point are sorted out: being sorted out using space clustering method to characteristic point；
S6, geometric transformation estimation: to step S5 each classification, using random sampling consistency (RANSAC) algorithm into Row geometric transformation estimation, further rejects inaccurate matching pair；
S7, detection are completed: being calculated optimal convex hull, fillet to one group of characteristic point in class and are carried out morphology operations, obtain To forgery region.
Preferably, the non-sample Contourlet transformation of the step S2 is by the tower-like filter (NSP) of non-sampling and non-pumping Sample directional filters group (NSDFB) composition, and the condition of NSP and NSDFB energy Perfect Reconstruction signal are as follows:
H0(z)G0(z)+H1(z)G1(z)=1
Wherein H0(z)、H1(z) resolution filter, G are indicated0(z)、G1(z) reconstruction filter is indicated.
Preferably, the step S3 is for a characteristic point X0, support area is R, any one in the support area Pixel X ∈ R can establish the coordinate system of an invariable rotary, i.e., withFor x-axis, perpendicular toFor y-axis, then exist Under invariable rotary coordinate system, on unit circle with, for starting point, obtained at equal intervals in positive y-axis pixel X N number of neighborhood point (i.e. away from The N number of point nearest from X).
Compared with prior art, the beneficial effects of the present invention are: describing son by using LIOP and utilizing global brightness sequence Support area is divided, this division does not need to calculate the principal direction of support area, not only saves calculation amount, but also resonable Son is described with real rotational invariance and monotonic luminance invariance by can above guarantee to construct, while being obtained using NSCT Multiple support areas of different scale, different resolution and different directions describe sub taste to improve LIOP, reduce meter It calculates complexity and is effectively prevented from error, finally improve the robustness of image forge Region detection algorithms.
Detailed description of the invention
Fig. 1 is image forge detection algorithm flow chart of the invention.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other Embodiment shall fall within the protection scope of the present invention.
As shown in Figure 1, a kind of image forge detection algorithm based on more support area local luminance sequences, including following step It is rapid:
The detection of S1, characteristic area determine characteristic point: eliminating the influence of noise first with Gaussian filter, recycle maximum Stable extremal region (MSER) algorithm extracts the maximum stable extremal region of image as support area, and the center of support area Point is characterized a little；
The transformation of S2, characteristic area: the spatial information of detection zone is obtained, is obtained using non-sample Contourlet transformation More support areas of different scale, different resolution and different directions；
The building of LIOP description in S3, each characteristic area: non-drop is carried out to brightness values all in each support area Sequence, is equally spaced divided into B sub-regions according to brightness value size for each support area, by superposition each subregion The LIOP value of all pixels point obtains description of each subregion, obtains each by description for all subregions of connecting Hold final LIOP description in region；
S4, characteristic matching: LIOP description in two neighboring characteristic point is carried out using two-way Euclidean distance ratio method Matching obtains one group of characteristic matching pair if matching；
S5, characteristic point are sorted out: being sorted out using space clustering method to characteristic point；
S6, geometric transformation estimation: to step S5 each classification, using random sampling consistency (RANSAC) algorithm into Row geometric transformation estimation, further rejects inaccurate matching pair；
S7, detection are completed: being calculated optimal convex hull, fillet to one group of characteristic point in class and are carried out morphology operations, obtain To forgery region.
Preferably, the non-sample Contourlet transformation of the step S2 is by the tower-like filter (NSP) of non-sampling and non-pumping Sample directional filters group (NSDFB) composition, and the condition of NSP and NSDFB energy Perfect Reconstruction signal are as follows:
H0(z)G0(z)+H1(z)G1(z)=1
Wherein H0(z)、H1(z) resolution filter, G are indicated0(z)、G1(z) reconstruction filter is indicated.
Step S3 is for a characteristic point X0, support area is R, and any one pixel X ∈ R is equal in the support area Can establish the coordinate system of an invariable rotary, i.e., withFor x-axis, perpendicular toFor y-axis, then in invariable rotary coordinate Under system, N number of neighborhood point (i.e. nearest N number of of distance X of pixel X for starting point, is obtained in positive y-axis at equal intervals on unit circle Point).
If the feature point set (i.e. the center of MSER algorithm detection zone) of image to be detected is T:
T={ t1,t2,…,tn} (1)
Wherein each characteristic point ti∈ T, i=1,2 ..., the corresponding region MSER n, available corresponding more supports Region LIOP describes sub- wi, and then obtain Feature Descriptor set ψ:
ψ={ w1,w2,…,wn} (2)
To each Feature Descriptor wi∈ ψ, i=1,2 ..., n, the most simple common two-way Euclidean distance ratio method of use It is matched.I.e. if characteristic point tAAnd tBIt is a pair of of matching pair, then has
Euclidean distance, w are asked in wherein d () expressionAAnd wBIt is t respectivelyAAnd tBCorresponding LIOP description, and wBAnd wCIt is Distance wANearest and secondary close Feature Descriptor, while guaranteeing wAAnd wDIt is distance wBRecently and secondary close Feature Descriptor, η are Threshold value takes 0.6.
It can be obtained by one group of characteristic matching pair: { m in this way1,m2,…,mqAnd { p1,p2,…,pq, wherein characteristic point mr With prIt is a pair of of characteristic matching, mr∈T,pr∈ T, r=1,2 ..., q.Next utilize space length by { m1,m2,…,mqOr {p1,p2,…,pqSorted out, calculate first each characteristic point apart from probability density E (mi):
Being chosen using formula (5) makes E (mi) the smallest characteristic point m0As initial point, characteristic point then is determined by formula (6) ml, l=1,2 ..., q whether with m0For one kind.
||ml-m0||≤δ (6)
Wherein δ is decision threshold, related with distorted image regional location, selects 200.On similarly remaining characteristic point repeats Process is stated until all characteristic points are all sorted out, { p1,p2,…,pqClassification and { m1,m2,…,mqCorrespond.
To each classification, geometric transformation estimation is carried out using RANSAC algorithm, it is inaccurate the purpose is to further reject Matching pair, make detection forgery region it is more accurate.The algorithm rejects inaccurate matching pair using formula (7):
WhereinWithIt is the homogeneous coordinate system of matching characteristic point, | | | |2Indicate the operation of 2- norm, threshold epsilon takes 3, U be the homography matrix between class, using affine transformation estimation, i.e.,
WhereinIt is the transformation such as rotation and scale, s in control coordinate transformxAnd syIt is x and y-axis in coordinate transform Translational movement.Necessary post-processing is finally carried out, i.e., optimal convex hull, fillet are calculated to one group of characteristic point in class and carries out shape State student movement is calculated, and obtains forging region.
The detection method of the detection method and SURF of choosing this algorithm and SIFT compares the standard this it appears that this algorithm True rate and recall ratio are above other algorithms
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims Variation is included within the present invention.Any reference signs in the claims should not be construed as limiting the involved claims.
In addition, it should be understood that although this specification is described in terms of embodiments, but not each embodiment is only wrapped Containing an independent technical solution, this description of the specification is merely for the sake of clarity, and those skilled in the art should It considers the specification as a whole, the technical solutions in the various embodiments may also be suitably combined, forms those skilled in the art The other embodiments being understood that.

Claims (3)

1. a kind of image forge detection algorithm based on more support area local luminance sequences, which comprises the following steps:
The detection of S1, characteristic area determine characteristic point: eliminating the influence of noise first with Gaussian filter, recycle maximum stable Extremal region (MSER) algorithm extracts the maximum stable extremal region of image as support area, and the central point of support area is Characteristic point；
The transformation of S2, characteristic area: obtaining the spatial information of detection zone, obtains difference using non-sample Contourlet transformation More support areas of scale, different resolution and different directions；
The building of LIOP description in S3, each characteristic area: carrying out non-drop to brightness values all in each support area and sort, Each support area is equally spaced divided into B sub-regions according to brightness value size, by owning in superposition each subregion The LIOP value of pixel obtains description of each subregion, obtains each support area by description for all subregions of connecting The final LIOP in domain describes son；
S4, characteristic matching: matching LIOP description in two neighboring characteristic point using two-way Euclidean distance ratio method, One group of characteristic matching pair is obtained if matching；
S5, characteristic point are sorted out: being sorted out using space clustering method to characteristic point；
S6, geometric transformation estimation: each classification to step S5 is carried out several using random sampling consistency (RANSAC) algorithm What transformation estimation, further rejects inaccurate matching pair；
S7, detection are completed: being calculated optimal convex hull, fillet to one group of characteristic point in class and are carried out morphology operations, obtain puppet Make region.
2. a kind of image forge detection algorithm based on more support area local luminance sequences according to claim 1, special Sign is: the non-sample Contourlet transformation of the step S2 is by the tower-like filter (NSP) of non-sampling and non-sampling directionality Filter group (NSDFB) composition, and the condition of NSP and NSDFB energy Perfect Reconstruction signal are as follows:
H0(z)G0(z)+H1(z)G1(z)=1 (1)
Wherein H0(z)、H1(z) resolution filter, G are indicated0(z)、G1(z) reconstruction filter is indicated.
3. a kind of image forge detection algorithm based on more support area local luminance sequences according to claim 1, special Sign is: the step S3 is for a characteristic point X0, support area is R, any one pixel X ∈ in the support area R can establish the coordinate system of an invariable rotary, i.e., withFor x-axis, perpendicular toFor y-axis, then in invariable rotary Under coordinate system, (i.e. distance X is nearest for the N number of neighborhood point for for starting point, obtaining pixel X in positive y-axis at equal intervals on unit circle N number of point).
CN201910647407.6A 2019-07-17 2019-07-17 A kind of image forge detection algorithm based on more support area local luminance sequences Pending CN110348464A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910647407.6A CN110348464A (en) 2019-07-17 2019-07-17 A kind of image forge detection algorithm based on more support area local luminance sequences

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910647407.6A CN110348464A (en) 2019-07-17 2019-07-17 A kind of image forge detection algorithm based on more support area local luminance sequences

Publications (1)

Publication Number Publication Date
CN110348464A true CN110348464A (en) 2019-10-18

Family

ID=68176693

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910647407.6A Pending CN110348464A (en) 2019-07-17 2019-07-17 A kind of image forge detection algorithm based on more support area local luminance sequences

Country Status (1)

Country Link
CN (1) CN110348464A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111768368A (en) * 2020-05-26 2020-10-13 西安理工大学 Image area copying and tampering detection method based on maximum stable extremal area
CN115035533A (en) * 2022-08-10 2022-09-09 新立讯科技股份有限公司 Data authentication processing method and device, computer equipment and storage medium

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
颜普等: "基于多支持区域局部亮度序的图像伪造检测", 《计算机应用》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111768368A (en) * 2020-05-26 2020-10-13 西安理工大学 Image area copying and tampering detection method based on maximum stable extremal area
CN111768368B (en) * 2020-05-26 2022-05-03 西安理工大学 Image area copying and tampering detection method based on maximum stable extremal area
CN115035533A (en) * 2022-08-10 2022-09-09 新立讯科技股份有限公司 Data authentication processing method and device, computer equipment and storage medium
CN115035533B (en) * 2022-08-10 2022-10-21 新立讯科技股份有限公司 Data authentication processing method and device, computer equipment and storage medium

Similar Documents

Publication Publication Date Title
Gao et al. Automatic change detection in synthetic aperture radar images based on PCANet
WO2019169816A1 (en) Deep neural network for fine recognition of vehicle attributes, and training method thereof
CN104933721B (en) Stitching image altering detecting method based on color filter array characteristic
CN103426186B (en) A kind of SURF fast matching method of improvement
CN109214420A (en) The high texture image classification method and system of view-based access control model conspicuousness detection
CN109740572B (en) Human face living body detection method based on local color texture features
CN104809731B (en) A kind of rotation Scale invariant scene matching method based on gradient binaryzation
Vaishnavi et al. Application of local invariant symmetry features to detect and localize image copy move forgeries
CN107506765B (en) License plate inclination correction method based on neural network
CN104217221A (en) Method for detecting calligraphy and paintings based on textural features
CN103400384A (en) Large viewing angle image matching method capable of combining region matching and point matching
CN104408728A (en) Method for detecting forged images based on noise estimation
CN106327534A (en) Tire inner wall texture identification method based on locating block
CN108550165A (en) A kind of image matching method based on local invariant feature
Fan et al. Registration of multiresolution remote sensing images based on L2-siamese model
CN114863464B (en) Second-order identification method for PID drawing picture information
CN103353941A (en) Natural marker registration method based on viewpoint classification
CN110348464A (en) A kind of image forge detection algorithm based on more support area local luminance sequences
CN111275070B (en) Signature verification method and device based on local feature matching
CN106709915B (en) Image resampling operation detection method
CN108491857A (en) A kind of multiple-camera target matching method of ken overlapping
CN109711420B (en) Multi-affine target detection and identification method based on human visual attention mechanism
Jwaid et al. Study and analysis of copy-move & splicing image forgery detection techniques
Changjie et al. Algorithm of remote sensing image matching based on corner-point
Soni et al. Improved block-based technique using surf and fast keypoints matching for copy-move attack detection

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
RJ01 Rejection of invention patent application after publication

Application publication date: 20191018

RJ01 Rejection of invention patent application after publication