Non‐redundant frame identification and keyframe selection in DWT‐PCA domain for authentication of video

IET Image ProcessingVolume 14, Issue 2 p. 366-375 Research ArticleFree Access Non-redundant frame identification and keyframe selection in DWT-PCA domain for authentication of video Sethuraman Ponni alias Sathya, Corresponding Author Sethuraman Ponni alias Sathya sathyaashok2007@gmail.com Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, IndiaSearch for more papers by this authorSrinivasan Ramakrishnan, Srinivasan Ramakrishnan Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, IndiaSearch for more papers by this author Sethuraman Ponni alias Sathya, Corresponding Author Sethuraman Ponni alias Sathya sathyaashok2007@gmail.com Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, IndiaSearch for more papers by this authorSrinivasan Ramakrishnan, Srinivasan Ramakrishnan Department of Information Technology, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamil Nadu, IndiaSearch for more papers by this author First published: 10 January 2020 https://doi.org/10.1049/iet-ipr.2019.0341Citations: 3AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract This study is intended to protect video data and watermark from unauthorised access. The proposed methodology accentuates two new algorithms, namely structural similarity index metric–absolute difference metric (SSIM-AMD) based non-redundant frame identification (NRFI) and entropy–AMD based keyframe selection (KFS) to reduce the challenges posed by traditional discrete wavelet transform–singular value decomposition. Traditional techniques embed the entire watermark to all existing frames in the video, which is cumbersome and time-consuming. In this methodology, NRFI algorithm is applied to segregate the redundant and non-redundant frames to specific database. The KFS algorithm is used to identify suitable keyframes. DWT is applied into keyframes, which decomposes the frames into subbands. The middle band is selected for embedding. The principal component of watermark image block is embedded into identified keyframes in the video. The chaotic map is adapted to reorder the watermark block for improving the authentication level of the watermarking. The ant colony optimization (ACO) technique is adapted to select the suitable scaling factor for watermarking process. The principal component analysis technique is employed for avoiding false-positive attacks. Experimental results show the proposed methodology can withstand image processing, video processing, false-positive attacks and produces good results in terms of perceptual quality and robustness. 1 Introduction In recent times, the frequent use of multimedia data and Internet is high. For this reason, thedigital data from the web can be easily disseminated around the world. However,accessing this data without owner consent is a major challenge. Traditionally,encryption techniques have been used to protect the multimedia data but problemssuch as ownership and unauthorised access are not resolved. This concern isaddressed by adapting digital watermarking techniques [1-5]. 1.1 Flow graph of the traditional method Nowadays, several algorithms for securing video data have been developed, where discrete wavelet transform (DWT)–singular value decomposition (SVD)-based algorithms are the most popular. The working principle of this traditional method is shown in Fig. 1. In this approach, all frames in the video are used for processing. DWT and SVD are applied to the frames. The watermark image is subjected to SVD, and then the singular values of the watermark are embedded to the singular values of the video with scaling factor. In Section 1.3, the literature survey reveals the various watermarking techniques and its merits and demerits. Fig. 1Open in figure viewerPowerPoint Process flow of traditional DWT–SVD 1.2 Motivation Digital data dissemination has become user-friendly due to the evaluation of the Internet, which can be easily distributed and shared with anyone. This kind of distribution makes illegitimate copying of original files without any constraint. It violates the ownership rights. Various techniques in video watermarking are on the avail, which is inherited with certain limitations in terms of quality of the video and watermark, combating the image processing attacks, video processing attacks and false-positive attacks [6-9]. Even then there is a scope for developing an effective watermarking algorithm in terms of visual quality, robustness and resistant for various attacks with lower computational time which encouraged to probe into non-redundant frame identification (NRFI) and keyframe selection (KFS) in DWT–principal component analysis (PCA) domain for authentication of video. The motivation for developing this paper is to provide authentication to video data with high imperceptibility and robustness for maintaining the ownership rights as well as safeguard the copyright data with minimum processing time. The authentication and integrity of watermarking process is improved by devising the NRFI and KFS algorithms. The perceptual quality of the watermarked video is enhanced by developing the ACO-based scaling factor selection algorithm. 1.3 Literature review In the recent past, various techniques are developed for protecting video copyright. Each technique has its advantages and limitations in terms of robustness and imperceptibility. Discrete cosine transform (DCT)-based methods: Karmakar et al. [10] suggested a watermarking method for video data using DCT. The Zernike moments of the squared watermark block was embedded in the middle of the luminance component. This method withstands the rotation, collusion and video processing attacks and does not resist for filtering and median attacks. Xu et al. [11] proposed video authentication system for H.264 and advanced video coding videos. Authentication code was embedded into the diagonally presented DCT coefficients using modulation concept. The watermark was the frame index of the video frame. This method was robust against the common signal processing attacks but not for video processing attacks. Thanh et al. [12] proposed watermarking technique based on frame patch matching for video. The KAZE feature-matching algorithm was employed for matching the features of frame patch with all the frames in the video. The watermark was embedded into the DCT-based randomly selected blocks in the matched region. This methodology was robust against the temporal attacks. The performances of the video processing attacks are low for this algorithm. Ling et al. proposed a watermarking technique for video based on the affine invariant features [13]. This technique was appropriate for DCT-based compressed video. In this methodology, the Harris affine detector was used to obtain the invariant feature points. This algorithm was robust against signal processing, and frame dropping attacks. It does not resist for filtering and frame averaging attacks. Generally, DCT-based methods do not withstand the filtering, median, scaling and some of the video processing attacks. Dual tree (DT)-CWT-based methods: Asikuzzaman et al. [14] proposed a method for securing video data using DT-CWT and SVD. In this method, the watermark was embedded into the singular values of the complex wavelet transform coefficients. This method was robust against the rotation, scaling, cropping, compression and noise addition attacks. Zebbiche et al. [15] proposed additive watermarking in the DT-CWT based on masking technique. These methods considered the coefficients of high-frequency band of DT-CWT. This method was robust for image processing and geometrical attacks. Asikuzzaman et al. [16] proposed a blind video watermarking algorithm in DT-CWT domain. In this method, the dual-tree complex wavelet transform was used. The watermark was embedded in the chrominance channel of the wavelet transform. This method was robust for various attacks such as temporal synchronisation, compression, re-modulation, downscaling and geometric attacks. Asikuzzaman and Pickering [17] in their review paper on video watermarking revealed several challenges in video watermarking and compared the different domain-based watermarking technique such as compressed domain, spatial domain and transform domain. Sake and Tirumala [18] proposed a video watermarking based on biorthogonal wavelet transform and optimisation techniques. Artificial bee colony algorithm was chosen for the selection of frames for embedding and extraction. This method was robust for image processing attacks not for video processing attacks. Allaf and Kbir [13] analysed the different watermark algorithms for its less processing time. Usually, CWT-based methods produce reasonable quality in terms of imperceptibility and robustness for image processing and geometrical attacks, except some of the video processing attacks. DWT-based methods: Masoumi et al. [19] proposed a technique for video ownership verification based on spatio-temporal data hiding in frequency domain. In this paper, the three-dimensional (3D) DWT was performed in the video frame. The motion frames are considered as a keyframe and whole watermark was embedded in the video. This method survives the frame dropping, swapping attacks and addition of noise. It does not withstand the filtering and frame averaging. Loganathan and Kaliyaperumal [20] proposed a video watermarking method based on bi-directional associative memory (BAM) neural networks and fuzzy system. In this algorithm, DWT was applied into the video frames. The weighed matrix was embedded into the middle frequency of all available frames in the video. This method withstands certain image and video processing attacks. It does not resist for median and frame averaging attack. Ejima and Miyazakia [21] proposed a method based on wavelet packets. Here, the same watermark was inserted into all available frames in the video. This method does not withstand the following attacks, namely frame averaging, swapping and filtering. Da-wen [22] proposed an algorithm based on DWT. In this method, the 3D blocks of the video are taken for embedding process. All the frames in the video are taken for watermarking process. This algorithm shows resistance for certain images and video processing attacks, except filtering attack. Preda and Vizireanu [23] proposed a method for video copyright. Here, the binary image was embedded into the detail coefficients of the middle-frequency band in all available frames of the video. This algorithm did not withstand the filtering attack and produced the minimum results for video processing attacks. Yahya et al. [24] proposed a watermarking algorithm using DWT and neural networks to reduce the processing time. Joshi et al. [25] suggested a DWT-based real-time image watermarking algorithm with least computation time. Guan et al. [26] proposed the key point-based KFS algorithm for reducing the computational complexity. Mehta et al. [27] suggested a flying watermarking algorithm for real-time video with minimum processing time. In general, DWT-based methods produce reasonable quality for signal processing and image processing attacks. It does not produce good results for filtering and frame averaging attack. DWT–SVD-based methods: Thind and Jindal [28] proposed a watermarking technique for video based on DWT–SVD. The watermark was embedded in the HH sub-band. Here, all the frames are embedded with the same watermark. This algorithm was resistant to image processing attacks. It does not withstand for video processing and false-positive attacks. Faragallah [29] proposed a method for video watermarking using DWT and SVD. The watermark with error correction code was embedded into the middle- and high-frequency sub-bands of entire frames in the video. This method withstands certain kinds of image processing attacks but not for video processing and false-positive attacks. Rajab et al. [30] proposed a video watermarking technique based on SCHUR with DWT. The binary image was used as a watermark. It was embedded into the middle sub-band of the randomly selected frames. This algorithm was only resistant to the attacks such as addition of noise, frame swapping and averaging. On the other hand, it does not withstand the attacks, namely median, filtering and false-positive attacks. Chen and Zhu [31] suggested the SVD and slope-based video watermark algorithm with minimal computational time. Mostly, DWT–SVD-based methods produce good performance in terms of imperceptibility and robustness for various attacks, namely image processing, geometrical and video processing attacks. In all the above methods, the watermark was embedded into all the frames, randomly selected frames or motion frames. The false-positive attack was not properly addressed. On the basis of the survey, the following research gaps are identified from the existing watermarking methods: Most of the existing methods embedded the whole watermark into all the frames in the video; however, if the watermark is embedded only in the selected sub-bands, the processing time is reduced and imperceptibility of the video is improved. Hence, selection of keyframe is an important gap in the research. Few attempts are made for KFS such as randomly selecting the frame or choose the motion frame. Embedding the whole watermark suffers the frame dropping attack. DWT–SVD-based methods only embed the singular values. It does not resist the false-positive attack. Watermark is embedded in the frames in a pre-determined sequential order. 1.4 Contributions The above-said issues are addressed in the proposed method by implementing the following proposed algorithms and approaches: Structural similarity index metric–absolute difference metric (SSIM–AMD)-based NRFI algorithm on identifying the non-redundant frames for processing. Entropy–AMD-based KFS algorithm to select the suitable keyframe for watermark embedding. An algorithm for pseudo-randomly embedding the watermark blocks is proposed to avoid frame dropping attack and enhance watermark authentication. Computational time analysis for the proposed method with existing techniques. The false-positive attack is resolved by incorporating the principal component of the watermark block in the watermark embedding. ACO-based scaling factor selection algorithm is devised for improving the quality of the watermarked video and extracted watermark. The rest of this paper is organised as follows. The workflow of the traditional algorithm is shown in Section 2. Section 3 indicates the workflow of the proposed approach and Section 4 presents the outcome and discussion. The conclusion is expounded in Section 5. 2 Proposed methodology Proposed methodology efforts to provide copyright protection to video data by adapting DWT, PCA and SSIM, AMD-based NRFI algorithm (SSIM–AMD NRFI) and entropy–AMD-based KFS algorithm. The main aim of the proposed technique is to reduce the processing time of watermarking process by implementing SSIM–AMD NRFI algorithm. For improving the authentication level of the watermark data, entropy–AMD-based KFS algorithm is devised. The video is preprocessed and segmented into different scenes based on the proposed absolute difference of frame variance (ADFV) method. The grey-scale image is used as a watermark. For resolving the false-positive attack, the principal component of the watermark block is embedded into the keyframes. The workflow of the proposed methodology is shown in Fig. 2. Fig. 2Open in figure viewerPowerPoint Process flow of the proposed approach 2.1 Video preprocessing and scene change detection The video is preprocessed into number of frames and converted into Ycbcr, and the luminance component is only considered for further processing [32]. The scene change detection technique is employed for detecting abrupt scene changes in the video. It is carried out by using ADFV method [7, 8]. FV is calculated by using the expression, as shown in the equation below: (1)where n−1 is the successive frame of n and FVn–FV of the nth frame. 2.2 Identification of non-redundant frames The non-redundant frames are identified by the proposed SSIM–AMD-based NRFI algorithm. The workflow of this algorithm is shown in Fig. 3. Fig. 3Open in figure viewerPowerPoint Identification of non-redundant frames The non-redundant frames are found by using the metrics, namely SSIM and AMD. The SSIMdiff is calculated by using the adjacent frames. The SSIM is used to measure the structural similarity between the adjacent frames; furthermore, AMD is used for measuring the absolute difference among the adjacent frames. The threshold is calculated by considering the average SSIM difference and AMD difference. If adjacent frames have the highest structural similarity and lowest mean difference, any one of the frame is kept in the non-redundant frame database (NRFD) and other frames are stored in the RFD. The SSIM–AMD-based non-redundant frames identification algorithm is explained in Section 2.2.1. 2.2.1 Structural similarity index metric SSIM metric is used to measure the perceived changes in structural information between the neighbouring frames in a scene. The formula for finding SSIM and average SSIM is represented in (2) and (3) which is computed by taking the SSIM average of the available frames in the scene (2)where µa is the average of x, µb is the average of b, σa2 is the variance of a, σb2 is the variance of b, σab is the covariance of a and b, k1 = (c1L)2, k2 = (c2L)2, L is a range of the pixel values, c1 = 0.01 and c2 = 0.03 by default (3)where f1, f2, …, fN are frames in a scene and N is the number of frames in a scene. 2.2.2 Absolute difference metric The AMD is used for eliminating the redundant frames in the scenes. It is performed by computing the absolute difference between the adjacent frames. The formula for AMD is represented in (4). The average AMD is calculated by (5) (4) (5)where f1, f2, …, fN are frames in a scene and N is the number of frames in a scene. 2.3 Keyframe selection The keyframes are selected from the non-redundant database by the proposed entropy–AMD-based KFS algorithm (see Fig. 4). This entropy and AMD are tied up for selecting the keyframes in the segmented scenes. The workflow of this algorithm is shown in Fig. 5. Fig. 4Open in figure viewerPowerPoint Non-redundant frames identification algorithm Fig. 5Open in figure viewerPowerPoint Identification of keyframes The keyframes are selected from the non-redundant database based on the entropy and AMD values of the frames. The limitation of the number of keyframes depends on the number of the changed scene in the video. The maximum entropy and minimum motion are the constraints for choosing suitable keyframes in each scene. The entropy–AMD-based KFS algorithm is described in Section 2.3.1. 2.3.1 Entropy The entropy is a statistical measure used to measure the randomness that can be used to classify the image [9]. It is calculated by using the equation below: (6)P encloses the histogram counts of image and f1 is the frame 1. 2.4 Watermark embedding The watermark embedding is the process of embedding the watermark image into the source video. The process flow of watermark embedding is shown in Fig. 2. The input video is preprocessed into number of frames and grouped into various scenes by applying ADFV-based scene change detection method. The non-redundant frames are identified by the proposed algorithm, and then keyframes are selected from the NRFD; furthermore, DWT and SVD are performed in the keyframes (see Fig. 6). The grey-scale image is selected as a watermark. The principal component of the watermark block is inserted into the singular values of the selected keyframes with a suitable scaling factor. Here, the scaling factor k = 0.45, which is selected based on ACO algorithm which is described in Section 2.6. The embedding process is explained vividly in Section 2.4.1. Fig. 6Open in figure viewerPowerPoint KFS algorithm 2.4.1 Discrete wavelet transform The Daubechies wavelet is used to decompose the video frame into approximation component LL and detailed components HL, LH and HH. The Daubechies wavelet transform is fast, it reduces the computation time. The middle-level sub-band LH is selected for watermark embedding process because it is more stable compared with other sub-bands [8, 9]. It is shown in Fig. 7. Fig. 7Open in figure viewerPowerPoint Two-level decomposition of Daubechies wavelet (a) Original video, (b) Daubechies wavelet level 2 decomposition 2.4.2 Singular value decomposition SVD transform is a tool used to decompose the matrix into singular values and singular vectors. It is represented in the equation below: (7)U and V are the singular vectors and S are singular values. The principal component is calculated by using U and S. It is represented in the equation below: (8) 2.4.3 Watermark block processing The reordered, non-overlapping, 16 × 16 watermark block was embedded in the selected keyframes in each scene. The number of watermark block equals the number of keyframes in a scene. In each scene, the different orders of the watermark block were embedded. This order is selected by Arnold's cat map. It is a kind of chaotic map used to generate the pseudo-random number sequence. To provide additional authentication and also make the algorithm more robust, Arnold's cat map is adapted. It is represented in the equation below: (9) is the n×n matrix of the image. 2.5 Watermark extraction The watermark extraction is the reverse process of watermark embedding (see Fig. 8). The workflow is shown in Fig. 9. The process flow of watermark extraction process is described in Section 2.5.1. Fig. 8Open in figure viewerPowerPoint Watermark embedding algorithm Fig. 9Open in figure viewerPowerPoint Watermark extraction algorithm 2.6 Selection of scaling factor using ACO algorithm Scaling factor is the important metric in the watermark embedding (see Fig. 10). It controls the robustness and imperceptibility of the watermarked video and extracted watermark. For the purpose of improving the quality of the watermarking process, the ACO algorithm is proposed for identifying the optimal scaling factor [33]. It is based on the ant searching the food surrounding the nest in an optimal way. The optimisation range is fixed between 0.01 and 1.0. The peak-signal-to-noise ratio (PSNR) and normalised cross-correlation (NCC) are the objective functions engaged for selecting the optimal scaling factor. The overall process is represented in the algorithm. The population of the ant is based on the normal distribution function fp. It is represented in the equation below: (10)where ymin is the optimal point in the vector space and σ is the standard deviation of the ants around the current minimum. Fig. 10Open in figure viewerPowerPoint Process flow of watermark extraction On the basis of the previous research work, a range of initialisation is chosen from 0.01 to 1: Prior inputs: Number of ants YN = {y1, y2, …, yN}. Step size = 0.01. Optimisation range: 0.01–1.0. Objective function: PSNRmax and NCCmax. The ACO algorithm is performed within the initial boundary value (0, 1) (see Fig. 11). Nearly, 100 scaling values in the interval were used in the experiment. Each ant in the population performed the optimisation process, which is explained in the algorithm. On the basis of the result of the objective function, the optimal scaling factor is fixed. The variations of PSNR and NCC for different scaling factors are showcased in Fig. 12. It is observed from Fig. 12, when the scaling factor is 0.45, the PSNR is maximum and NCC getting stabilised. Finally, the scaling value 0.45 is selected from the experiment. Fig. 11Open in figure viewerPowerPoint ACO algorithm Fig. 12Open in figure viewerPowerPoint Scaling factor selection using ACO algorithm 3 Results and discussion MATLAB software is conferred to conduct various simulation experiments to test the proposed NRFI and KFS in DWT–PCA domain for authentication of video. The input video sequences Suzie, Vipmen and Foreman in the red, green and blue, audio–video interleave format with frame rate of 30 fps are selected for testing, which are available in the standard library. The sample watermarks are cameraman, nature and logo. The input videos and watermark images tried in the experiments are shown in Fig. 13. Fig. 13Open in figure viewerPowerPoint Original video snapshots and watermarks (a) Suzie, (b) Vipmen, (c) Foreman, (d) Cameraman, (e) Nature, (f) Logo The sample watermark, watermarked video and extracted watermark are shown in Figs. 14a–c, respectively. Fig. 14Open in figure viewerPowerPoint Original watermark, watermarked video and extracted watermark (a) Original watermark, (b) Watermarked video, (c) Extracted watermark The quality of the watermarked video is evaluated in terms of the PSNR value and that of watermark is evaluated by using metrics such as PSNR, NCC, similarity ratio (SR) and bit error rate (BER). NCC, BER and SR are measured between the original and extracted watermarks. These metrics are evaluated using (11)–(13). 3.1 Assessment of imperceptibility The imperceptibility is the measure of the perceptual quality. It is measured between the watermarked video and original video as well as watermark and extracted watermark. It is measured by PSNR metric. The resulting PSNR values of watermark lies between 51 and 53.6 dB as shown in Fig. 15 and the watermarked videos appear visually identical to the original one (11) (12) (13)where P = L×K; L, K are the sizes of the frame in pixel (14)where S is the number of matching pixel values D is the number of different pixel values. Fig. 15Open in figure viewerPowerPoint Different watermarks embedded in the video with PSNR (a) Cameraman (51.2 dB), (b) Nature (53.6 dB), (c) Logo (52.5 dB) The PSNR value obtained for the watermarked video Suzie is 52.3 dB. Extracted watermark has a PSNR value of 53.6 dB. The SR between the extracted and original watermarks is 0.94. The extracted watermark looks like an original one. 3.2 Assessment of robustness To evaluate the robustness of the proposed method, various types of image processing attacks such as blurring, brightening, median, adding noise, filtering and video editing attacks such as frame averaging, dropping and swapping of the video frames have been performed. The watermark is extracted after each attack and compared with the original watermark to measure the NCC and BER [20, 33]. The result of the various performance metrics for the traditional method and proposed algorithm are shown in Table 1. It was concluded that the proposed method is highly robust to the various images and video processing attacks. Table 1. Performance metrics of traditional versus the proposed algorithm Name of the attack Traditional algorithm Proposed algorithm PSNR, dB of video PSNR, dB of watermark NCC SR BER PSNR, dB of video PSNR, dB of watermark NCC SR BER no attack 45.6 47.2 0.95 0.89 0.21 52.30 53.6 0.97 0.94 0.15 Blur 44.5 46.3 0.93 0.88 0.23 49.80 52.7 0.95 0.92 0.16 brighten 43.9 45.7 0.92 0.87 0.24 49.10 52.4 0.94 0.92 0.18 median 38.7 40.2 0.91 0.85 0.31 43.80 47.1 0.93 0.91 0.24 salt and pepper 40.6 41.7 0.85 0.86 0.30 45.60 48.4 0.87 0.88 0.23 Gaussian 39.6 42.5 0.90 0.86 0.29 47.80 49.1 0.92 0.89 0.21 Poisson 41.8 43.3 0.87 0.87 0.32 46.90 50.2 0.89 0.90 0.26 frame dropping 41.5 43.8 0.89 0.87 0.31 46.70 49.9 0.95 0.92 0.23 frame average 40.1 40.3 0.87 0.85 0.33 45.50 49.1 0.9 0.89 0.25 frame swapping 40.9 42.3 0.88 0.86 0.32 46.40 49.7 0.92 0.91 0.24 3.3 Computational time The computational complexity of the proposed methodology is measured using Suzie video. Table 2 represents number of redundant and non-redundant frames in each identified scene. About 27% of the redundant frames are available in each scene. These frames are excluded for the watermarking process. Thus, the overall time for watermark embedding is reduced. Table 2. Number of redundant frame analysis of Suzie video Scene number Total number of frames in a scene Number of redundant frames in a scene Number of non-redundant frames in a scene S1 31 13 18 S2 43 12 31 S3 35 9 26 S4 56 15 41 S5 69 17 52 S6 48 10 38 S7 59 16 43 S8 35 11 24 S9 75 19 56 total number of frames 451 122 (27%) 329 computational time for the proposed method including non-redundant frames and keyframe identification 65 s computational time for the conventional method 120 s Table 3 shows the percentages of redundant frame and time complexity of the videos, namely Vipmen, Suzie, traffic, news and surgical. On the basis of Table 3 inputs, it was concluded that nearly 27% redundant frames were present in each video. These frames are excluded from the watermarking process, which reduces processing time. The computational time of the proposed method is compared with the existing techniques developed by Chen et al. and Guan et al. It is inferred from Table 3 that the computational time of the proposed method is comparatively lesser than the existing methods due to effectively embedding the watermark block in the keyframes. Table 3. Percentage of redundant frames versus computational time comparison Name of the video Number of frames in the video Percentage of redundant frames, % Computational time taken to embed a watermark in a conventional method, s Computational time taken to embed watermark Chen and Zhu [31], s Computational time taken to embed the watermark in Guan et al. [26], s Overall computational time of the proposed approach including NRFI, KFS and watermark embedding, s Vipmen 283 20 80 73 110 42 Suzie 451 27 120 110 150 65 traffic 600 29 137 145 180 89 news 1000 32 229 240 275 148 surgical video 2000 35 400 410 445 297 3.4 False-positive attack This is the type of attack performed in the watermarked video to extract the original watermark using a reference watermark [34, 35]. The false-positive test is carried out by replacing the original watermark (W1) data (u, v) with the reference watermark (W2). The extraction method is performed according to the extraction algorithm in the watermarked video. With the assistance of the reference image, the original watermark could not be obtained from the watermarked video, which guarantees the integrity of the proposed technique, which withstands the false-positive problem. The experimental validation is represented as follows. The proposed methodology violates the false-positive attack by integrating the principal component into the watermark embedding process. Therefore, it is impossible to retrieve the original watermark using a reference image. The experimental validation is shown in Fig. 16. Fig. 16Open in figure viewerPowerPoint Experimental result for false-positive attack (a) Original watermark (W), (b) Reference watermark (WR), (c) Extracted watermark with PCA using WR, (d) Extracted watermark without PCA using WR 3.5 Assessment of geometrical attacks The geometrical attacks are also called as N attacks. This is a kind of distortion to an image. Geometric attacks are rotation, scaling, cropping and translation [14, 17]. The proposed methodology withstands these attacks by adapting discrete wavelet transform and PCA. The DWT has stable spatial-frequency localisation properties as well as multi-resolution characteristics. According to dimensionality reduction properties of PCA, the loss of information during geometrical attack can be reduced. The PSNR and NCC performances of the proposed methodology against geometric attack are presented in Table 4. Table 4. PSNR and NCC of extracted watermark for geometric attacks Geometric attacks PSNR, dB NCC no attack 53.6 0.97 rotation 47.25 0.89 scaling 44.54 0.87 cropping 49.21 0.90 translation 42.32 0.84 The extracted watermark has the PSNR and NCC values of 53.6 dB and 0.97, respectively, for no attack. The rotation attack was carried out to an angle of 1°. The scaling attack is performed with 0.5 distortions; similarly, the cropping attack was performed with 10%. The translation attack was carried out with 10 px movement. It is inferred from Table 4 that for various geometrical attacks, the average PSNR value is 47 dB and average NCC is 0.89. It indicates that the proposed approach withstands the geometrical attacks. 3.6 Performance comparisons The PSNR value of the video obtained from the proposed algorithm is compared with existing algorithms. PSNR variations of watermarked video between the proposed approach and existing approach are shown in Fig. 17. NCC variations between the proposed and existing algorithms are represented in Fig. 18. On the basis of the results obtained, the proposed methodology is highly robust against image and video processing attacks in comparison with the existing approach. Fig. 17Open in figure viewerPowerPoint PSNR variations of watermarked video between the proposed approach and existing approach Fig. 18Open in figure viewerPowerPoint NCC variations of watermarked video between the proposed approach and existing Approach 4 Conclusion In this paper, NRFI and KFS in DWT–PCA domain for authentication of video are proposed. This methodology offers several advantages over existing methods. 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Res., 2018, doi:10.1080/03772063.2018.1496801, ISSN: 0377-2063, Online First Citing Literature Volume14, Issue2February 2020Pages 366-375 FiguresReferencesRelatedInformation