Source camcorder identi cation from cropped and scaled videos -...

Source camcorder identification from cropped and scaledvideos

Dai-Kyung Hyun∗, Seung-Jin Ryu, Min-Jeong Lee,Jun-Hee Lee, Hae-Yeoun Lee, and Heung-Kyu Lee

Web Science and Technology Division & Dept. of Computer Science, KAIST

ABSTRACT

In the field of imaging device identification, the unique property of Photo-Response Non-Uniformity (PRNU) iswidely employed. One of disadvantages of the PRNU based methods is sensitive to de-synchronization attacks.In this paper, we propose an improved PRNU based camcorder identification method which performs well withsimultaneously cropped and scaled videos. The proposed method solves the out-of-sync problems by achievingdownscale-tolerance of Minimum Average Correlation Energy Mellin Radial Harmonic (MACE-MRH) filter. Theexperimental results demonstrate that the proposed method identifies source devices faster and more accuratethan the existing method.

1. INTRODUCTION

Ever since analog imaging devices have been replaced by their digital counterparts, demands for reliable multi-media forensics methods also have been increased. Among them, establishing the origin of digital video is oneof the most important forensics fields. These methods are employed to arrest child pornography filmmakers ormovie pirates. In the field of video forensics, the unique property of Photo-Response Non-Uniformity (PRNU)is most actively being researched.1–4 There are numerous advantages of PRNU based approaches. PRNU basedmethods reveal high accuracy of device identification. Moreover, the methods are able to identify not only devicemanufactures but also individual camcorders between the same models. These methods, however, suffer fromgeometric distortion attacks such as scaling and cropping which spatially de-synchronize target PRNU withreference PRNU. More specifically, if an input video is slightly cropped or scaled, the detection process withoutsynchronization fails.

To solve this kind of out-of-sync problem, three approaches were proposed so far. Goljan et al. investigatedmost of scaling factors by inverting a target PRNU pattern.4 However, this method required hundreds ofsimilarity metrics computation. Moreover, the performance was declined due to decrement error in scaling ratio.The second approach to synchronize PRNU is to adopt blind estimation method5–8 of re-sampling parameters.Hyun et al. proposed forgery detection for surveillance video by employing statistical characteristics.9 However,even the latest method is hard to detect against video compression; and they cannot estimate the scalingparameter if the parameter is less than 0.5. The third possibility is to use scale- and rotation-invariant transformssuch as Fourier-Mellin transform. However, Fourier-Mellin transform introduces severe distortion so that it isnot adequate to identify a source camcorder with geometric attacks. Unfortunately, to the best of our knowledge,other invariant transforms applicable to video forensics were not actively researched.

In this paper, we propose a camcorder identification method based on MACE-MRH filter.10 By adopting theMACE-MRH filter, the proposed method achieves scaling invariance. One of benefits of the MACE-MRH filter isthat the range of scale-tolerance is designable. On the contrary, the filter demonstrates high performance whenthe range of scale-tolerance is narrow. In general, for private or public distribution, people usually downscaleoriginal video; they rarely upscale their video. Therefore, we assume that the scaling parameter is less than 1.0in this paper. Cropping invariance is achieved by the property of correlation filter.

The rest of paper is constructed as follows. Sec. 2 provides a brief overview for MACE-MRH filters. Aproposed camcorder identification method is introduced in Sec. 3. After that, experimental results are exhibitedin Sec. 4 and Sec. 5 concludes.

∗ Dai-Kyung Hyun: E-mail: [email protected]

Media Watermarking, Security, and Forensics 2012, edited by Nasir D. Memon, Adnan M. Alattar, Edward J. Delp III,Proc. of SPIE Vol. 8303, 83030E · © 2012 SPIE · CCC code: 0277-786X/12/$18 · doi: 10.1117/12.907311

Proc. of SPIE Vol. 8303 83030E-1

Downloaded from SPIE Digital Library on 15 Feb 2012 to 143.248.135.186. Terms of Use: http://spiedl.org/terms

2. SCALE-TOLERANT CORRELATION FILTER

In this section, we provide the overview of the MACE-MRH correlation filter and their scale-tolerant property.Firstly, we briefly overview the basics of correlation filters. After that MACE-MRH filter theory will be explainedin detail. Some of the materials in the following are based on [10],[14].

2.1 Review of Correlation-Filter BasicsCorrelation is a natural metric for characterizing the similarity between a reference pattern and a test pattern. Acorrelation filter is designed to have high correlation peak with the pattern of interest. Advanced correlation filterdesigns have been developed to achieve more improved performance criteria such as correlation peak sharpness,noise-tolerance, and distortion-tolerance.11–14

Correlation filters are typically used in the following manner. Let x[m,n] and h[m,n] denote the 2-D testpattern and the impulse response of a correlation filter, respectively. If we let X[k, l] and H[k, l] denote the 2-DDiscrete Fourier Transforms (DFTs) of x[m,n] and h[m,n], respectively, then correlation output c[m,n] can beexpressed as the inverse 2-D DFT of the frequency domain conjugate product as follows:

c[m,n] = F−1{X[k, l]H∗[k, l]}. (1)

Once the output array is computed, a correlation value may be compared with a predetermined threshold fordetection. The correlation value is measured in terms of sharpness, i.e., relative height compared to surroundingvalues. There exist several different measures of peak sharpness.15 In this paper, we chose the Peak to CorrelationEnergy (PCE) measure defined as

PCE =c(ipeak,jpeak)

2∑i,j c(i, j)

2(2)

where (ipeak,jpeak) is the peak positions in correlation output.

Correlation filtering may be divided into two stages: the design stage and the use stage. It is importantto distinguish between these stages, because although the design of correlation filter may be computationallyexpensive, the design complexity does not reflect in any way upon the usage complexity of the correlation filter.

The MACE-MRH filter is designed to tolerate target scale distortion. This filter approximates control scaleresponse, and this ability to specify the scale response is useful in applications where multiple filters or multiplescaled inputs are used in parallel. In the following subsections, we describe MACE-MRH filter design theory.

2.2 MACE-MRH filter design theoryThe MACE-MRH filter is based on the Mellin Radial Harmonic (MRH) transform, given by the following pairof equation :

F (ρ, φ) =

∞∑m=−∞

Fm(φ)ρj2πm−1 and Fm(φ) = L−1

R∫r0

F (ρ, φ)ρ−j2πm−1ρdρ, (3)

where F (ρ, φ) is Fourier Transform (FT) of a 2-D reference pattern f(x, y) in polar coordinates and Fm(φ) isthe m-th MRH function of F (ρ, φ). The limits of integration must be chosen to satisfy the relation such thatL =ln(R/r0) is an integer. This relationship is derived from the condition that MRH components be orthogonal.

Let gβ(x, y) be a scaled version of reference pattern, i.e. gβ(x, y) = f(βx, βy). Then the MRH transform ofthe 2-D polar FT of gβ(x, y) is given by

Gβm(φ) = L−1β−j2πm−1Fm(φ). (4)

Thus, the scaling operation only affects the MRH transform coefficient functions by a phase factor β−j2πm−1.



Figure 1. Computed MACE-MRH filter

If we take FT of reference pattern F (u, v) as the input to filter H(u, v), the resulting correlation value at theorigin is given by

c =

∫ ∫F (u, v)H∗(u, v)dudv =

2π∫0

∞∫0

F (ρ, φ)H∗(ρ, φ)ρdρdφ = L

∞∑m=−∞

Cm where Cm =

2π∫0

Fm(φ)H∗m(φ)dφ.

(5)Suppose a scaled pattern is gβ , we can rewrite Eq. (5) as a function of the scale factor β:

c(β) = β−1∞∑

m=−∞e−j2πm ln βCm. (6)

We can simplify the expression by a invertible logarithmic transformation Λ defined as follows:

c(β) = Λ{c(β)} = eLβ2π · c(eLβ

2π ) =

∞∑m=−∞

Cme−j(Lm)β . (7)

The relationship between the transformed scale response c(β) and the coefficients Cm has exactly the same formas Eq. (8) for the frequency response of the Finite Impulse Response (FIR) filter.

A(w) =

M∑k=−M

a[k]e−jkw (8)



Figure 2. Proposed identification procedure

where a[k] is the k-th coefficient of the impulse response of the filter. This relationship allows us to design scaleresponse by employing the FIR filter design methods to solve for Cm.

Once the Cm are determinded, the next task is to find the correlation filter Hm(ρ) in order to determineH(ρ, φ). To improve correlation output, the Average Correlation Energy (ACE) of a filter is minimized whilesatisfying the relationship in Eq. (5). The ACE is equal to the average energy the correlation filter and can beexpressed as follows:

ACE =

2π∫0

∞∫0

|H(ρ, φ)|2 PF (φ)ρdρdφ ≈ L

∞∑m=−∞

⎡⎣

2π∫0

|Hm(φ)|2 PF (φ)dφ

⎤⎦ (9)

where PF (φ) is the average power spectrum of the test reference obtained by computing the average of PF (ρ, θ).Using the method of Lagrange multipliers, the solution to the minimization problem can be found to be

Hm(φ) = λ∗m

Fm(φ)

PF (φ) + α, λm =

Cm∫ 2π

0|Fm(φ)|2PF (φ)+αdφ

, (10)

where α is a small additive constant. The correlation filter H(u, v) is found by applying the inverse MRHtransforms of Hm(φ) and applying a polar-to-cartesian coordinate transform. Fig. 1 shows the MACE-MRHcorrelation filter computed from a PRNU.

3. CAMCORDER IDENTIFICATION METHODThe proposed method identifies source camcorder as shown in Fig. 2. First, Reference PRNU (RPRNU) isextracted from the blue-sky video. The extracted RPRNU is downscaled by a factor of T. After that, wegenerate the MACE-MRH filtered pattern from the downscaled RPRNU. The MACE-MRH filter shows highperformance when the passband∗ of scale response is narrow. Therefore, we need to design the appropriate rangeof scale response applicable to the camcorder identification. Hence, the scale response c(β) is determined asfollows:

∗The term "passband" refers to the range of scales for which the correlation output is specified to be nonzero.



Figure 3. The range of scale-tolerance

c(β) =

{1, if |β| ≤ βt

0, otherwise (11)

With constrained scale response c(β), the filter tolerates against scaling at the following range of G0.

G0 : L0 ≤ scaling factor ≤ U0, L0 = T − βt, U0 = T + βt (12)

In this paper, the RPRNU is downscaled by a factor of 0.875 and generated the MACE-MRH filter to be scale-tolerant within the range of 0.75 ≤ scaling factor ≤ 1.0 as shown in Fig. 3. However, the filter does not properlywork if the scaling factor is less than L0. Thus, we calculate the correlation with Test PRNU (TPRNU) upscaledby a factor of Kn in order to solve this problem. Among correlation values calculated between the MACE-MRHfiltered RPRNU and upscaled TPRNUs, a high correlation value is yielded if the test video is scaled within therange of Gn.

Gn : Ln ≤ scaling factor ≤ Un, Un = Ln−1, K0 = 1, Kn =U0

Un, Ln =

L0

Kn(13)

Particularly, we calculate correlation values (PCE0, PCE1, ..., PCEn) with the TPRNU upscaled by factors of(K0,K1, ...,Kn), then compute PCEmax.

PCEmax = max(PCE0, PCE1, ..., PCEn) (14)

PCEmax has a high correlation value when the test video is scaled within range of Ln ≤ scaling factor ≤ U0. IfPCEmax is higher than the threshold τpce, the proposed method determines that the test video and the referencevideo were taken by the same device. Cropping invariance is easily acquired by the property of cross-correlation.

The size of the test video determine the range of upscaling. If Lmin is within the range of Gk, we calculatethe correlation with TPRNU upscaled by factors of (K0,K1, ...,Kk)

Lmin = max{M/m,N/n} (15)

where the size of the test video is M ×N and the size of the reference video is m× n.

Although complicated computation to generate the MACE-MRH filter is required, the proposed method needsonly one generation of the filter during the whole process. Once the filter is generated, it is used to calculatecross-correlation with the test pattern. For instance, if the test video is downscaled by a factor of 0.25, theproposed method demands only five computations with upscaled TSPN by factors of 1, 1.3333, 1.7778, 2.3704,and 3.1605.

4. EXPERIMENTAL RESULTSWe conducted experiments with seven camcorders as seen in Table 1. With the videos taken by these camcorders,we totally prepared 91 high quality test videos. All the videos were taken with resolution of Full-HD(1920×1080),H.264 with 5 Mb/sec, and 5 minutes recoding time. Other conditions such as white balance, sharpness, andcontrast are automatically set. We extracted RSPNs from blue-sky videos for each camcorder then generatedMACE-MRH filters from these RSPNs. To decide identification threshold τpce, we calculated PCE values betweenMACE-MRH filters which represent each camcorder and TSPNs from every test. From the results, τpce wasdetermined at which false positive rate (FPR) was 10−2.



Figure 4. The processing time

4.1 Comparison with an existing methodIn this test, we compared the proposed method with Goljan’s method.4 We resized test videos in decrements of0.1 from 1.0 to 0.2. We carried out the proposed method and Goljan’s method for every test video. After words,the average processing time and identification rate according to each scaling factor is calculated. The intervalfor brute force search for Goljan’s method was setted to 0.005.

Figure 4 exhibits the average processing time for each method when they were performed 100 times. It showsthat the proposed method is faster than the Goljan’s method at most 40 times. Figure 5(a) shows test resultson the identification rate at FPR = 10−2. The proposed method performed as well as Goljan’s method.

Furthermore, If the interval of brute force search is 0.005, the Goljan’s method calculates the correlation bydecrementing the scaling parameter in 0.005 units from 1 to low bound. Thus, if the test video is resized by ascaling factor which is not multiple of 0.005, e.g, 0.202, the Goljan’s method does not perform well because ofthe error of 0.002. Figure 5(b) shows the identification rate with FPR=10−2 when the scaling factors were in therange from 0.196 to 2.004. While the accuracy of Goljan’s method decreased with the scaling factor 0.196, 0.202,and 0.204, the proposed method was not influenced by such a problem because of its scale-tolerance property.

4.2 Performance test with various size of videosIn the second experiments, we tested the performance with various size of videos. While resizing Full-HD videosas smaller videos which are appropriate for video players such as iPhone or iPad, both scaling and croppingoccur. More specifically, test videos are resized and cropped by 320 × 240, 480 × 320, 640 × 480, 800 × 480,960× 640, and 1024× 768. With these videos, we measured the identification accuracy. We also calculated theminimum (MIN), average (AVE), and maximum (MAX) value of PCEs in case of a same camcorder and othercamcorders.

Table 1. Camcorder models used in the experiments

Brand Model Full ResolutionKODAK ZI8 1920 × 1080

SAMSUNG HMX-H205BD 1920 × 1080SONY HDR-CX550 1920 × 1080SONY HDR-CX360 1920 × 1080SONY HDR-CX500 1920 × 1080SONY HDR-SR10 1920 × 1080SONY HDR-SR12 1920 × 1080



(a) (b)

Figure 5. The identification rate : (a) The accuracy of each method for test videos resized from 1.0 to 0.2, and (b) Theaccuracy of each method for test videos resized from 0.196 to 0.204

The identification rate and PCEs are shown in Table 2. The proposed method identifies source camcorderwith very high accuracy. Regardless of the video size, the PCEs obtained for the matched cases are larger thanthe unmatched cases.

5. CONCLUSION

In this paper, we proposed an improved PRNU based camcorder identification method which performs well withsimultaneously cropped and scaled videos. By employing the scale-tolerance property of the MACE-MRH filter,the proposed method could solve the out-of-sync problems of source camcorder identification. More specifically,we designed appropriate range of scale response of the MACE-MRH filter. The experimental results indicatedthat the proposed method detect the source camcorder fast and high accuracy with various size of videos. Inreal world, a video might be resized by different aspect ratio. However, the proposed method only considers avideo covered by the same downsizing argument. Thus, in our future work, a method which can identifies sourcedevice from a video resized by different aspect ratio will be presented. Furthermore, we will extend the proposedmethod to the image based method.

Table 2. Identification rate from scaled and cropped videos

Resolution Identification rate (%)

PCE

matching the camcorder not matching the camcorder

Max Min AVE Max MIN AVE

320 × 240 100% 726.66 76.50 263.95 59.21 30.35 40.14

480 × 320 100% 1169.70 108.50 388.53 62.30 31.38 41.20

640 × 480 100% 2013.40 177.60 665.93 55.21 29.40 38.86

800 × 480 100% 1531.56 155.57 476.96 60.54 30.06 42.24

960 × 640 100% 1943.38 142.09 550.95 50.40 28.06 41.05

1024 × 768 100% 2598.20 156.93 693.46 57.86 13.37 39.10



Acknowledgments

This research project was supported by Ministry of Culture, Sports and Tourism(MCST) and from Korea Copy-right Commission in 2011, WCU (World Class University) program (Project No: R31-30007), and NRL (Na-tional Research Lab) program (No. R0A-2007-000-20023-0) under the National Research Foundation of Koreaand funded by the Ministry of Education, Science and Technology of Korea

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