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Recaptured video detection based on sensor pattern noise


With the advances in digital camcorders, video recapturing (screen camcording), which is also called camcorder theft, is becoming a significant problem. Nevertheless, little research on recaptured video detection has been undertaken. In this paper, an automated method for the detection of recaptured videos is proposed based on the shot-based sensor pattern noise (SPN). The SPN, which is considered to be the fingerprint of digital imaging sensors, is used due to its identifiable attribute. Furthermore, the differences between the production processes of the original videos and recaptured videos are analyzed, and this results in the shot-based method being proposed. Moreover, the SPN merging and high-frequency map are derived in order to overcome the low quality of the shot-based SPN. Empirical evidence from a large database of test video, including compressed and scaled video, indicates that superior performance of the proposed method.


With greater accessibility to the Internet than ever before, a significant amount of information is being shared online. Furthermore, many people are now able to communicate with each other through the Internet without the limitations of the time and space. The advantages of the Internet has improved the quality of life; however, the increased use of the Internet has also resulted in many misuses. The most common misuse is copyright infringement. Various types of digital content have been uploaded by pirates, and they have spread quickly through illegal pathways. Among these types, pirated movies have resulted in significant problems in the film industry worldwide. Most illegal copies are pirated by video recapturing, which includes screencasting and screen camcording. Screencasting, which is also known as video screen capture, is a digital recording of the output from a computer screen; however, it is not a common video recapturing technique because direct access to the machine that is playing the movie is required. In contrast, screen camcording, which is the most common method of video recapturing, records a movie that is being projected on a movie screen. It only requires a single camcorder to pirate a movie in a movie theater.

The most significant problem of pirated movies recaptured by camcorders is that the illegal copies appear on the Internet just a few hours after a film’s release and before the legal DVDs become available. Only one camcorder is needed to copy a movie and to trigger massive unauthorized reproductions and distribution of millions of illegal copies of the movie. The annual amount of revenue loss caused by these illegal copies has reached US$ 6.1 billion, and approximately 90 % of these illegal copies are pirated via video recapturing [1]. For this reason, many movie associations including the Motion Picture Association of America (MPAA) and the Federation Against Copyright Theft (FACT) refer to this act as camcorder theft, which is a type of content theft, and they consider it to be the most significant problem facing the film industry in the modern era.

In order to mitigate the amount of unauthorized recapturing activities, most countries have enacted statutes that prohibit unauthorized recording in motion picture exhibition facilities, and they have conducted anti-recapturing campaigns including public education/training and reward programs for identifying illegal recorders. With these efforts, the frequency of illegal recapturing has decreased; however, the very nature of the digitalized content allows one successful recapture in any theater in the world to become significant through duplication and distribution of the recaptured video. As a result, video recapturing remains one of the most significant threats to the film industry worldwide.

A key problem with catching pirated and bootlegged copies of movies is that it requires significant amounts of man-hours to manually inspect the downloaded content. Furthermore, with high-end camcorders and the increasing quality of camcording, the quality of the recaptured videos is improving, which contrasts with the early recaptured videos that were easily detected by the naked eye. The increase in the quality of the recaptured videos has accelerated the revenue loss in film industries.

In order to counter camcorder theft, we present an automated recaptured video detection method that is based on the characteristics of sensor pattern noise (SPN). The proposed method is based on the recaptured video being recorded by a single digital camcorder. In the experiments, 400 recaptured videos were used to verify the performance of the proposed method. Originally, video recapturing included screen camcording, and screencasting, among other methods. However, this study focuses on the detection of screen camcording.

The remainder of this paper is organized as follows. Section 2 discusses the related work in recaptured content detection. Section 3 presents the preliminary understanding and approach to detecting recaptured videos. The proposed method is presented in Section 4. The experimental results and conclusion are presented in Sections 5 and 6, respectively.

Related work

In order to counter content recapturing, various techniques have been investigated. These techniques can be classified into four categories. The first category is related to camcorder recapture resilient watermarking. Various watermarking schemes that are robust to analog-digital conversion attack have been proposed [25]. In particular, Lee et al.’s method estimates the position of the camcorder piracy through geometric distortion estimation based on the auto-correlation function [2]. However, the watermark degrades the quality of the original video and the watermark needs to be inserted before the film screening.

The second category involves video recapturing prevention. Yamada et al. have undertaken a number of studies [68]. In these studies, an infrared light is projected on the theater screen to contaminate the video content of the recaptured videos. Furthermore, the real-time warning system is provided using reflected infrared light. However, a significant installation cost is required and the infrared light is blocked by IR-cut or IR-absorb filters inside digital camcorders. Furthermore, the real-time warning system suffered from poor detection results when the pirate was positioned in a diagonal (5°) seat.

The third category is recaptured image detection. The support vector machine (SVM) classifier is the most frequently used means of recaptured image detection. Cao et al. proposed a method of identifying the images recaptured from LCD screens [9]. They extracted 155 features (local binary patterns, multi-scale wavelet statistics, and color features) from a single image. Gao et al. presented a general model for image recapturing [10] and 166 features (specularity, surface gradient, color histogram, chromaticity, contrast, and blurriness) were used to identify the recaptured images.

The fourth category includes studies on the recaptured video detection. Only a small number of studies have been undertaken in recaptured video detection. Image features are not used; instead, geometry, global jitter, ghosting artifact, and SPN are used. Wang et al. proposed a re-projected video detection method that utilizes geometric parameters [11]. This method estimates the skew within the intrinsic matrix introduced by the recapturing camcorder. If the estimated skew is not zero, the video is determined to be a recaptured one. However, the robustness of this method decreases as the recapturing camcorder plane approaches a configuration parallel to the cinema screen. Moreover, validation in real sequences (a single 42-frame segment) was limited. Visentini-Scarzanella et al. proposed a recaptured video detection method using scene jitter [12]. The recaptured video is assumed to contain uniform jitter that is caused by handheld camcorders. The model for the uniform additive jitter noise is trained and used in identifying recaptured videos. However, the recapturing process is typically undertaken using a tripod, which provides stability and reduces jitter. In addition, the experimental results exhibited a high false positive ration (FPR) (30 %). Bestagini et al. proposed a video recapture detection method based on ghosting artifact analyses [13]. The ghosting artifact is modeled for the analyses, and the specific conditions where the ghosting artifact does not have periodicity are targeted. However, the videos that have undergone inter-frame interpolation (including frame rate change) would exhibit a high FPR. Jung et al. proposed a recaptured video detection method using the shot-based SPN [14]. The shot-based SPNs are estimated and examined to identify the recaptured videos. Warshall’s algorithm [15] is adopted to resolve the low-quality problem of the shot-based SPN; however, none of the techniques that enhances the quality of estimated SPNs is used. As a result, this scheme is limited in detection accuracy. The proposed method is based on this study and deals with the limitation of this method.

Approach to detecting recaptured videos

This section presents our approach to detecting recaptured videos. First, we discuss some preliminary knowledge. Afterwards, we describe the video recapturing process and the properties of the recaptured videos. Then, we give our assumptions of the recaptured videos. Finally, the approach to identifying the recaptured videos is deduced based on the suggested assumptions.

Preliminary understandings


A shot, which is recorded by one camera, is a basic temporal unit in a film. The term “shot” can refer to two different parts in the filmmaking process: first, in production, a shot is the moment from when the camera starts recording until the moment it stops. Second, in film editing, a shot is the continuous footage or sequence between two edits or cuts. The second definition of a shot is used in this paper because it is more suitable for the proposed method. When one shot ends and another begins, it is called a “shot change.” Shot changes can be detected using shot change detectors [16].

Sensor pattern noise (SPN)

Digital recording devices such as digital cameras and digital camcorders adopt various types of digital image sensors, e.g., a charge-coupled device (CCD), complementary metal-oxide-semiconductor (CMOS), and a junction field-effect transistor (JFET). The digital image sensor consists of numerous photon detectors that convert photoelectrons into electrical signals using the photoelectric effect. The strength of the electrical signal is affected by the sensitivity of the photon detectors to light. The light sensitivity of the photon detectors varies slightly depending on the imperfections created during the manufacturing process of the silicon that forms the photon detectors. This difference in light sensitivity of each pixel generates uncorrelated multiplicative pattern noise. Consequently, every digital sensor casts a unique sensor pattern noise (SPN) onto images (frames) it takes. The SPN acts as a sensor fingerprint that identifies a source digital imaging device. Using the identifiable attribute of SPN, source digital camera (camcorder) identification methods have been proposed [1719]. In order to identify the source digital camera, the fine quality of the reference SPN is estimated from uniform images, e.g., blue sky images. Then, the test SPN is estimated from the test image. If the correlation value calculated using the reference SPN and test SPN is higher than a specified threshold, the digital camera is determined to be the source camera of the test image.

Video recapturing model

The video recapturing process can be modeled through expanding the general image recapturing model presented by Gao et al. [10]. Figure 1 depicts the overall process of the original video shooting and recapturing. The video recapturing process includes three steps: the first capture (scene shooting), the display on a medium m (including the beam projector and the LCD screen), and the recapture. Assume that N 1 camcorders (\(\phantom {\dot {i}\!}c_{(1,1)}, \ldots, c_{(1,N_{1})}\)) and N 2 camcorders (\(\phantom {\dot {i}\!}c_{(2,1)}, \ldots, c_{(2,N_{2})}\)) are set up for the first capture and the recapture, respectively. Then, the shot (first capture) s(x), which captures the real scene radiance R(x) using the ith (i=1,…,N 1) camcorder c (1,i) of the first camcorder set, is described as follows:

$$ s(x) = f_{(1,i)}(R(x)), $$
Fig. 1
figure 1

An example of the video recapturing process in a theater. a Scene shooting and original video frames and b original video projection/recapturing and recaptured video frames

where f (1,i) denotes the camera response function (CRF) of the camcorder c (1,i). The term “shot” is used instead of “frame” because each frame belonging to one shot has common properties. After the first capture, s(x) undergoes various post-processing P 1, and then the shot \(\tilde {s}(x)\) is completed.

$$ \tilde{s}(x) = P_{1}(s(x)) = P_{1}(f_{(1,i)}(R(x))) $$

The post-processing P 1 includes geometric operations, A/D conversion, frame rate change, recompression, inserting computer graphics (CGs), and so on. The shot screened on the medium m, which includes the beam projector and wide screen in the example of Fig. 1 b, is expressed as follows:

$$ s_{m}(x) = f_{m}(\tilde{s}(x)). $$

The recaptured shot S(x), which is recorded using the jth (j=1,…,N 2) digital camcorder c (2,j) of the second camcorder set, is written as follows:

$$ S(x) = f_{(2,j)}(P_{m}(s_{m}(x)\alpha(x) + E(x)(1-\alpha(x)))), $$

where f (2,j),P m , E, and α denote the CRF of the camcorder c (2,j), the geometric processing, which includes rotation, shearing, and translation that occurs from the piracy camera positioning during recapturing, the radiance of the recapture environments, and the weight factor of E, respectively. The recaptured shot s 2(x) also undergoes post-processing P 2, which only includes simple operations such as cropping, scaling, and recompression to reduce the file size for uploading. Then, the final recaptured shot is acquired as follows:

$$ \tilde{S}(x) = P_{2}(S(x)). $$

Assumptions of recaptured videos

Based on the video recapturing model, the assumptions of the recaptured videos are condensed into lists below.

  1. A.

    Recaptured videos are recorded by digital camcorders.

  2. B.

    A single recaptured video is recorded by a single digital camcorder.

  3. C.

    Recaptured videos undergo minimal post-processing.

From the listed assumptions of the recaptured videos, the shot-based analysis method is proposed as a favorable solution for detecting recaptured videos. Because the CRF signal of the recapturing camcorder remains clearly in recaptured shots and every recaptured shot is recorded using a single camcorder, the recaptured videos can be detected by verifying the coherence of the CRFs that are estimated from each shot.

In this context, SPN is suggested as the suitable feature for the recaptured video detection. SPN is the best solution for identifying a digital recording device and it usually withstands video processing. The shot-based SPNs estimated from each shot in a recaptured video are correlated to each other because every shot is recorded using the same digital camcorder and undergoes minimal post-processing. In contrast, the shot-based SPN estimated from each shot in an original video could not be correlated to each other. Furthermore, analog cameras do not leave SPN on the video frames. Even if only digital camcorders were used in the original video shooting, the source camcorder for each shot would vary. Finally, the heavy post-processing in the original videos makes the shot-based SPNs decorrelated.

Proposed method

In this section, the recaptured video detection method is described in detail. Figure 2 depicts the proposed method, which is divided into three steps. First, the input video is divided into shots in the shot division step. Then, the shot-based SPNs are estimated from the divided shots. Finally, the input video is determined to be a recaptured video or an original video using correlation-based SPN merging. In the last step, each pixel in the estimated SPN is examined using a high-frequency map (HFM) to exclude the high-frequency components from the estimated SPN.

Fig. 2
figure 2

An overview of the proposed recaptured video detection method

Shot division

Once an input video is selected, it is divided into multiple shots using the shot change detector. An accurate shot change detector is important because incorrect shot change declarations can affect the results of the proposed method. Figure 3 depicts two different shot change detection errors in original videos. If a shot change is missed, as depicted in Fig. 3 a, two actual successive shots are declared to be a single shot. The declared shot in Fig. 3 a might have two different source camcorders. It results in a mixture of two SPNs and it can be correlated to any SPN that is estimated from a shot recorded by the source camcorders of those shots. As a result, it increases the false positive ratio (FPR) in the calculation of the SPN correlation. In contrast, the false shot change alarm in the original video does not have an enormous effect on the result of the proposed method. The actual single shot is divided into two different shots in Fig. 3 b. It only increases the count of the correlation calculation and shortens the length of the falsely divided shots. In addition, a wrong shot change declaration in the recaptured video does not have an enormous effect on the result of the proposed method because every shot in the recaptured video is assumed to be recorded by an identical digital camcorder. Therefore, the performance of the shot change detector needs to be tuned in order to minimize the missed shot changes.

Fig. 3
figure 3

Shot change detection errors. a False negative shot change detection and b false positive shot change detection

From the various shot detection techniques available, the histogram comparison is used. The histogram comparison is discriminant, easy to compute, and mostly insensitive to translational, rotational, and zooming shots [16, 20]. In the proposed method, two detectors (one for intensity and the other for hue) are used in order to minimize the missed shot changes. The detail of the shot change detectors is described as follows:

$$ SD_{p} = \sum_{q=0}^{G-1} {\vert H_{p} (q) - H_{p+1} (q) \vert}, $$

where H p (q) denotes a histogram value for the pth frame, with q being one of G (256 for intensity and 360 for hue) possible levels of the histogram. S D p , which is the criterion for the shot change, is the sum of the absolute differences between the histograms derived from the pth frame and the (p+1)th frame. In order to utilize S D p for shot change detection with any frame size, S D p is normalized using the frame size. If the normalized S D p is greater than a given threshold, the shot change is declared. As a result, the consecutive frames between two successive shot changes are declared as one shot.

SPN estimation

In this step, the shot-based SPN is estimated from each shot. Before the estimation, inappropriate shots for the estimation including dark shots and short-length shots are excluded. Each shot-based SPN is estimated using the maximum likelihood estimation method based on the SPN model [18, 19]. Assuming that the shot is composed of d grayscale frames (I 1,I 2,…,I d ), the detailed SPN estimation process is as follows:

$$ \mathbf{\widehat{K}} = \frac{\sum_{n=1}^{d}{\mathbf{W}_{n} \hat{\mathbf{I}}_{n}}}{\sum_{n=1}^{d}{(\hat{\mathbf{I}}_{n})^{2} }}, $$

where \(\mathbf {\widehat {K}},\mathbf {W}_{n}\), and \(\hat {\mathbf {I}}_{n}\) represent the estimated SPN, a noise frame and a noiseless frame that correspond to the nth frame I n , respectively. Each noise frame, which is used in the SPN estimation, is obtained by filtering the frame using a wavelet-based Wiener filter [21]. After the SPN estimation, the codec noise that is generated by the video codec is removed. Normally, the videos are transformed using the differential pulse-code modulation block discrete cosine transform and it produces block artifacts in each video frame [19]. These block artifacts should be removed prior to the correlation calculation between estimated shot-based SPNs because they make uncorrelated pairs of SPNs correlated. These artifacts are periodic with a 16 ×16 block size and have a high energy in the frequency domain. They are removed using the Wiener filter in the frequency domain after zeroing out the means of rows and columns of the estimated shot-based SPN [18, 19]. The codec noise-free SPN is denoted by \(\mathbf {\widehat {SPN}}\).

Correlation-based SPN merging using high-frequency map

In this step, every shot-based SPN, which was estimated in the previous step, is examined to make the final decision. However, each estimated shot-based SPN is low in quality so that the wrong decision might be driven at the decision-making step. To resolve the low-quality problem of the estimated shot-based SPN, Warshall’s algorithm was used in Jung et al.’s method [14]. However, Warshall’s algorithm only helped the erroneously uncorrelated shot-based SPN pairs to be linked to each other. Furthermore, none of the techniques that enhanced the quality of the estimated SPN was used.

In the proposed method, correlation-based SPN merging and a HFM are used to resolve the low-quality problem of the estimated shot-based SPN. A better quality of SPN, which minimizes the false decisions of the proposed method, can be obtained using correlation-based SPN merging. Furthermore, HFM helps to remove the regions that might have a detrimental effect on the SPN merging and the correlation calculation.

High-frequency map (HFM)

The estimated shot-based SPNs are investigated to determine whether they are correlated with each other using correlation-based SPN merging. However, estimated shot-based SPNs have low quality. The low quality of the estimated SPN influences the correlation calculation and SPN merging. If \(\widehat {\mathbf {SPN}}_{i}\) is the shot-based SPN estimated from the ith shot S h o t i (i=1,…,N S ), then it is expressed as follows:

$$ \widehat{\mathbf{SPN}}_{i} = \mathbf{SPN}_{i} + \mathbf{\Omega}_{i}, $$

where S P N i and Ω i denote the ideal SPN and the composite error, which is caused during the SPN estimation process, corresponding to \(\widehat {\mathbf {SPN}}_{i}\), respectively. The lower energy of Ω i results in better quality of \(\widehat {\mathbf {SPN}}_{i}\); furthermore, the lower variance of the sequence Ω i [ x,y] for each pixel (x,y) provides the better quality of the merged SPN. Thus, both a better quality of the merged SPN and a low false negative correlation can be achieved if high-energy regions in Ω i are suppressed. High-energy regions in Ω i are typically introduced by high-frequency components such as the strong edges in the video frames. Therefore, the high-frequency components need to be excluded from the estimated SPN. If only the high-quality regions in the SPNs are selected before the SPN merging, high quality of the merged SPN is guaranteed.

Li proposed an approach to attenuating the influence of the high-frequency components on the estimated SPNs [22]. Li’s method improved the accuracy of the source camera identification by assigning weighting factors inversely proportional to the magnitude of the test SPN components. However, this method was designed for the source digital camera identification using fine quality of reference SPN. Furthermore, two different SPNs still can be correlated after Li’s method is applied if they have correlated high-frequency components in the same position.

Based on the above observation, we propose a HFM, which is a Boolean matrix that has the same frame size as the estimated SPN, to determine whether any pixel of the estimated SPN is contaminated by a high-frequency component. The high-frequency components in the scene leave relatively high absolute pixel values because denoising filters are applied in the noise estimation. Figure 4 depicts the histogram difference caused by high-frequency components. Figure 4 a, which is a histogram that is calculated from each pixel value of a high-quality SPN, presents a small variance; Fig. 4 b, which is a histogram that is calculated from each pixel value of a low-quality SPN contaminated by the high-frequency components, presents a relatively large variance. Therefore, high absolute values are filtered to remove the high-frequency components in the estimated SPN. However, it is not desirable to set a fixed pixel threshold to remove high-valued pixels because the pixel values in the estimated SPN vary depending on the recording environment. A normal distribution fitting is used to avoid the fixed thresholding. Afterward, the SPN is standardized using the mean and variance of the estimated normal distribution model. Moreover, a certain portion of the HFM is marked by thresholding the standardized SPN. The HFM is calculated as follows:

$$ \mathbf{HFM}[x,y] = \left\{ {\begin{array}{*{20}{c}} {1,\,\,\,\,\left| {\mathbf{\widehat{SPN}}_{Z}[\!x,y]} \right|\, > \,z_{\alpha}}\\ {0,\,\,\,\left| {\mathbf{\widehat{SPN}}_{Z}[\!x,y]} \right|\, \le \,z_{\alpha}} \end{array}} \right.\,\,\,, $$
Fig. 4
figure 4

SPN histograms. a An estimated high-quality SPN and b an estimated SPN contaminated by high-frequency components

where \(\mathbf {\widehat {SPN}}_{Z}\) is the standardized \(\mathbf {\widehat {SPN}}\) using the estimated model and z α is the critical point of the standard normal distribution (1 = high-frequency component, 0 = clean SPN).

After the HFM is created, it is refined by removing the erroneously detected parts. Since high-frequency components are normally connected and grouped together, the connected pixel groups that are extremely small are pruned. The HFM is morphologically dilated and eroded, then the eight-way connected components with sized smaller than a specified percent (0.01 % in the proposed method) of the frame size are converted to zero.

The HFM indicates the high-frequency components so that the relative regions in the estimated SPN are removed using HFM, as follows:

$$ \mathbf{\widetilde{SPN}} = \mathbf{\widehat{SPN}} \cdot \neg \mathbf{HFM}, $$

where \(\mathbf {\widetilde {SPN}},\cdot \), and ¬ denote the high-frequency-component-free SPN, the pair-wise multiplication, and Boolean negation operation, respectively. Figure 5 exhibits an example of an HFM calculated from a single frame.

Fig. 5
figure 5

An HFM example. a A single frame; b SPN estimated from a; c HFM corresponding to (a) using Z α (α=0.005); d refined HFM

Correlation-based SPN merging

In order to determine whether a given video is recaptured or not, the estimated shot-based SPN is investigated. If N shots are selected from the given video, then N shot-based SPNs are estimated and the corresponding HFMs are applied. Let \(\widetilde {\mathbf {SPN}}_{i}\) be a shot-based SPN estimated from the ith shot S h o t i (i=1,…,N). Two different shot-based SPNs shall be correlated if the corresponding shots are recorded by the identical digital camcorder. In order to verify this, the correlations between the estimated shot-based SPNs are measured. The correlation between \(\widetilde {\mathbf {SPN}}_{i}\) and \(\widetilde {\mathbf {SPN}}_{j}\) (ij) is measured by calculating the peak-to-correlation energy (PCE) value between them. The PCE is described as follows:

$$ {}{\small{\begin{aligned} PCE\left(\widetilde{\mathbf{SPN}}_{i}, \widetilde{\mathbf{SPN}}_{j}\right) = \frac{Corr\left(\widetilde{\mathbf{SPN}}_{i}, \widetilde{\mathbf{SPN}}_{j}\right)\left[u_{\text{center}},v_{\text{center}}\right]^{2}}{\sum_{u,v}^{}{Corr\left(\widetilde{\mathbf{SPN}}_{i}, \widetilde{\mathbf{SPN}}_{j}\right)[u,v]^{2}}}, \end{aligned}}} $$

where C o r r(·,·)[u,v] represents the value of the normalized cross-correlation (NCC) surface that is calculated between the SPN pair at the position of (u,v). The center coordinate of the correlation plane was used in the PCE calculation rather than using the peak coordinate because the coordinate offset of each shot is synchronized in the recaptured video, while it might not be in the original video. If the PCE value is greater than a given threshold T, then two shots S h o t i and S h o t j are determined to have been recorded using the identical digital camcorder. Otherwise, S h o t i and S h o t j are determined to have been recorded using two different digital camcorders.

The input video is declared to be a recaptured video if every possible pair of the estimated shot-based SPNs presents greater PCE values than the threshold T. However, the low-quality problem of the estimated shot-based SPN still remains. The low quality of the estimated SPNs increases the false negative correlations. To minimize the false negative correlation, the correlation-based SPN merging is used. The SPN merging increases the number of frames in the SPN estimation of the merged SPN; and a large number of frames produce a small variance of the estimated SPN [17, 18].

The correlation-based SPN merging on a given shot-based SPN array is divided into 4 steps (refer to Fig. 2): (1) iterate steps 2 to 4 until the size of the shot-based SPN array does not change; (2) the HFM of each shot-based SPN is estimated; (3) the PCE value of every pair in the shot-based SPN array is calculated using HFM; (4-1) if a correlated pair is found, it is merged using HFM; (4-2) the shot-based SPN array is updated by replacing the correlated pair with the newly merged SPN.

In the SPN merging step, the HFMs are used. Let \(\widehat {\mathbf {SPN}}_{i}\) and \(\widehat {\mathbf {SPN}}_{j}\) be merged into \(\widehat {\mathbf {SPN}}_{M}\), then each pixel in \(\widehat {\mathbf {SPN}}_{M}\) is computed as follows:

$$ {\footnotesize{\begin{aligned} {}\widehat{\mathbf{SPN}}_{M}\!= \!\left\{ \begin{array}{cc} Ave(\widetilde{\mathbf{SPN}}_{i}[x,y], \widetilde{\mathbf{SPN}}_{j}[x,y]), & \mathbf{HFM}_{i} [x,y]= 0 \land \mathbf{HFM}_{j} [x,y]= 0 \\ \widetilde{\mathbf{SPN}}_{j}[x,y], & \mathbf{HFM}_{i} [x,y] = 1 \land \mathbf{HFM}_{j} [x,y]= 0 \\ \widetilde{\mathbf{SPN}}_{i}[x,y], & \mathbf{HFM}_{i} [x,y]= 0 \land \mathbf{HFM}_{j} [x,y]= 1 \\ 0, & \text{otherwise} \end{array} \right. \!\!, \end{aligned}}} $$

where \(Ave(\cdot,\cdot),\mathbf {HFM}_{i},\mathbf {HFM}_{j},\widetilde {\mathbf {SPN}}_{i}\), and \(\widetilde {\mathbf {SPN}}_{j}\) denote an averaging operation based on the number of frames that are used in the SPN estimation (including SPN merging), the HFMs and the high-frequency-component-free SPNs that correspond to \(\widehat {\mathbf {SPN}}_{i}\) and \(\widehat {\mathbf {SPN}}_{j}\), respectively. The final decision is made after the correlation-based SPN merging process is finished. If the final output is a single merged SPN, then the input video is declared to be a recaptured video. Otherwise, the input video is declared to be an original video.

Finally, we analyze the computational complexity of the proposed method. Let F s ,N T ,N TS , and N S denote the maximum value between the row and column sizes of a test video, the total length of the video frames, the sum of the frame lengths of selected shots, and the number of selected shots. The shot division step is processed in time O(F s 2·N T ) and the SPN estimation step is in time O(F s 2·N TS ) + O(F s 2 logF s ·T S ) by assuming that the size of the wavelet filter is negligible compared to F s [23]. Computing the NCC takes O(F s 2 logF s ) by a fast Fourier transform; thus, the computational complexity of correlation-based SPN merging is O(F s 2 logF s ·N S 3). Since N T T TS N S , the computational complexity of the proposed method can be expressed as below.

$$ O\left({F_{s}}^{2} \log F_{s} \cdot {N_{T}}\right) $$

Experimental results

In this section, the performance of the proposed method is evaluated. The specific settings for the experiments are also provided. Furthermore, the results of the experiments are presented and analyzed.

Experiment settings

For the experiments, 130 original videos were used. Most of the videos were originally shot partially or completely using digital camcorders [24]. The original video list includes Attack on Titan (TV animation), Game of Thrones (season 3), Homeland (seasons 1 and 2), The Office (season 7), and Sherlock (seasons 1 and 2). Seven digital camcorders (refer to Table 1 for specifications) were used to recapture the original videos. In total, 400 videos were created via recapturing. The original videos with a resolution of 1280 × 720 were collected, and the video recaptures were conducted with the same resolution conditions. In the high-definition (HD) resolution videos, the robustness of the proposed method was tested at different video quality factors (QFs) and scaling factors (SFs). The recaptured videos were recompressed with QFs of 100, 90, 80, 70, and 60. In addition, they were resized with SFs of 80 and 60 and recompressed with QFs of 100 and 80. As a result, a total of 3600 recaptured videos were created for the experiments. In the shot division step, the 20 longest shots (N S =20) were selected from each test video for the SPN estimation step. Each selected shot had more than 100 frames in general and the average shot length of the selected shots was approximately 130.

Table 1 Specifications of the cameras used in the experiments

An auditorium was selected for the video recapturing. The auditorium was approximately 18 m wide and 21 m long, and it had 286 fixed seats. The width of the screen in the auditorium was approximately 4 m, and the video clips projected using an NEC PA600X projector on the screen were displayed at a size of 4 × 2.25 m in the horizontal and vertical directions, respectively. Because the auditorium had its own slope between the seating rows and the zooming of the digital camcorders was restricted, four seats, which are indicated using red dashed circles in Fig. 6, were selected for the recapturing in order to have the entire projected video screen within the camera angle. Furthermore, every light source (except the beam projector) was blocked in order to simulate a movie theater; thus, the average luminance was 0.2 LUX while the recapturing was conducted. The camcorders were also mounted on tripods for the recapturing.

Fig. 6
figure 6

Test theater environment

In the experiments, several modified decision-making measures of the SPN merging were tested. The proposed SPN merging uses a strict criteria that declares a detection only if a single merged SPN remains after the SPN merging iterations stop. However, two or more SPNs will remain if one or more shot-based SPNs that have extremely low quality are included. In order to take this case into account, the modified measures that tolerate remaining k additional SPNs, which were never merged, except a single merged SPN. Three different k values (0 original criteria, 1, and 2) were used in the tests. Furthermore, in order to demonstrate the performance of the proposed method, it was compared with two recaptured image detection methods (Cao and Kot’s method [9], Gao et al.’s method [10]) and three recaptured video detection methods (Wang and Farid’s method [11], Bestagini et al.’s method [13], and Jung et al.’s method [14]). In order to apply the recaptured image detection methods to the obtained videos, every I frame was extracted from the N S shots that were used in testing the proposed method. We randomly selected 80 % of the extracted frames for training and the remaining for testing. Furthermore, those N S shots were used to examine the above listed recaptured video detection methods. The parameters for Wang and Farid’s method were set as follows: the number of points for feature matching was 32 and the number of fundamental matrices for skew estimation was 5. In Bestagini et al.’s method, two detectors for two different video recording environments were proposed. Therefore, the detector that fits our test environment (video frame rate ≠ camcorder frame rate) was tested.

HFM experiment

To evaluate the performance of the proposed HFM, we conducted an experiment. The PCE values that were calculated from each pair of the shot-based SPNs were analyzed. Since 20 shots were selected from each test video, 190 PCE values were calculated per a test video. In this test, six values (0.0005, 0.001, 0.005, 0.01, 0.05, and 0.1) were used for α, which is the parameter of the HFM. Furthermore, Li’s method [22] was benchmarked. Model 5, which presented the best results in source digital camera identification, was used in the experiment. Figure 7 presents the PCE values from both the original video and the recaptured video (including the recompressed and scaled video) using 1.5 interquartile range (IQR) boxplots. As Fig. 7 describes, a wide overlapping range was exhibited in the PCE values between the original video and the recaptured video when no post-processing was applied. The overlapping range caused a significant number of false positive SPN correlations. However, the overlapping range was significantly narrowed after the techniques that degrade the influence of the high-frequency components were applied. This indicates that the number of false positive SPN correlations decreased. Li’s method was effective in attenuating the influence of high-frequency components. However, the attenuation in the pixel values of the high-frequency components still creates a false positive correlation. Since the proposed HFM changes the pixel values with the high-frequency components to zero, the range of the overlap is narrower than that of Li’s method. The increment of α value did not have a significant effect on the test results because the majority of the detected pixels in the HFM was filtered by post-processing. The range of the overlap was minimized where α was 0.005. Therefore, we used 0.005 for the HFM parameter α in the following experiments. Furthermore, Li’s method was not tested in the rest of the experiments because Li’s method cannot be applied to SPN merging.

Fig. 7
figure 7

The 1.5 IQR of the HFM test (the PCE values were calculated from pairs of SPNs)

Quality factor experiment

In general, the recaptured videos are recompressed in order to reduce their file size before the online distribution. For this reason, we tested the robustness to compression. The recaptured videos were compressed using various quality factors (QFs) while other compression attributes, including the resolution and frame rate, were not changed. The range of QFs for the test was set from QF100 to QF60 with a step size of 10. The ffdshow H.264 codec and TMPGEnc 4.0 Xpress video encoder were used to recompress the test videos. In addition, we used the variable key frame interval, which was the default setting. The parameter α of the HFM was set to 0.005.

The test results are presented in Fig. 8. First, Jung et al.’s method [14] exhibited steep slopes in the receiver operating characteristic (ROC) curves; these slopes were caused by a few low-quality SPNs. Several low correlations between shot-based SPNs were compensated for using Warshall’s algorithm, but the limitation of the low quality of the shot-based SPN was not. Bestagini et al.’s method [13] presented constant test results while the QFs decreased. However, the detection results were not satisfactory. The frame rate change, which was used to unite various shots in the original video production, might have produced false positive detection. The proposed method without the HFM produced slightly better results than Jung et al.’s; however, it also did not provide satisfactory detection results. In contrast, the proposed method with the HFM exhibited a high level of detection ratios. Overall, the every recaptured video detection method that utilized the SPN exhibited a decreasing detection ratio as the QF decreased.

Fig. 8
figure 8

Quality factor experiment results. a QF100, b QF90, c QF80, d QF70, and e QF60

The recompression lowers the quality of the estimated shot-based SPN and results in low correlation values between the shot-based SPNs. Furthermore, the severe quality degradation of the shot-based SPN disturbs the SPN merging and makes the shot-based SPN uncorrelated even to the high-quality SPN. In the high QF tests (Fig. 8 ac) with the strict criteria (k=0), most missed detections resulted from the low correlations between the merged SPN and several shot-based SPNs that had the severely degraded quality due to the recompression. However, with a less strict criteria (k=1,2), the detection ratio increased because the number of the shots that underwent the severe quality degradation was small. In contrast, in the low QF tests (Fig. 8 de), most missed detections arose from the failure of the SPN merging because the quality of most of the estimated shot-based SPNs was heavily degraded. The proposed method presented a low detection ratio in the QF60 test. However, in general, QF60 is not a common quality factor that is used in video compression due to the severe visual quality degradation. Thus, the low detection ratio in the QF60 test is acceptable considering that QF60 is a rare compression parameter for recaptured videos.

Table 2 presents the test results for Cao and Kot’s method, Gao et al.’s method, and Wang and Farid’s method. The test results of these methods could not be presented in graph form; thus, we compared the proposed method by fixing the false positive ratio. In addition, we also included the test results for Bestagini et al.’s method and Jung et al.’s method at a zero false positive ratio. The table demonstrates that the proposed method outperformed the other methods, except in the QF60 test where Gao et al.’s method outperformed the proposed method.

Table 2 Detection result at different levels of video quality factors

Scaling factor experiment

The recaptured videos were scaled using various scaling factors (SFs). Because up-scaling is rare for videos, only SFs lower than 100 (%) were tested. SFs lower than 60 were excluded in the experiment considering that the resolution of the original recaptured videos was 1280 × 720. Normally, the video quality is also adjusted when the scaling is adjusted; thus, the experiments for QF80, which is a common default recompression quality factor, were also conducted.

The detailed settings of recompression followed the quality factor experiment except for the QF and SF. Figure 9 presents the experiment results for the scaled test videos. The proposed method (k=2) with the HFM outperformed the other methods. The proposed method exhibited good results for QF80 and SF0.8, which are common parameters for video scaling. Table 3 presents the test results compared with Cao and Kot’s method, Gao et al.’s method, Wang and Farid’s method, Bestagini et al.’s method, and Jung et al.’s method. The table demonstrates that the proposed method outperformed the other methods at every SF level. The compared methods that did not utilize the SPN exhibited a constant detection ratio while the QF and SF decreased.

Fig. 9
figure 9

Scaling factor experiment results. a SF80, b SF60, c SF80 with QF80, and d SF80 with QF80

Table 3 Detection result at different levels of scaling factors


This paper investigated a recaptured video detection method. The proposed method operates automatically for a given video and does not use additional information such as watermarks. The proposed method is based on the SPN, which is the unique fingerprint of digital imaging sensors. The proposed method consists of three steps: first, the video is divided into shots; second, the shot-based SPN is estimated from the divided shots; and third, the video is determined to be a recaptured video or not using the correlation-based SPN merging. Furthermore, an HFM was used to exclude the low-quality regions in the estimated SPNs during the SPN correlation calculation and merging processes. We demonstrated the superior performance of the proposed method by comparing the test results for various quality factors and scaling factors with other recaptured content detection methods. However, the proposed method remains weak against severe video compression. The next research focus will include considerations of this weakness.


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This research project was supported by Ministry of Culture, Sports and Tourism (MCST) and from Korea Copyright Commission in 2015.

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Correspondence to Heung-Kyu Lee.

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Jung, DJ., Hyun, DK. & Lee, HK. Recaptured video detection based on sensor pattern noise. J Image Video Proc. 2015, 40 (2015).

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  • Recaptured video
  • Screencast
  • Camcorder theft
  • Sensor pattern noise
  • Recapture
  • Digital forensics