A blind steganography method based on histograms on video files

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A blind steganography method based on histograms on video files O Cetin*a, F Akarb, A T Ozcerita, M Cakiroglua and C Bayilmisa a Department of Computer Science, Technical Education Faculty, Sakarya University, Sakarya 54188, Turkey b

Faculty of Electrical and Electronics Engineering, Naval Academy, Tuzla, Istanbul 34940, Turkey

Abstract: Several techniques of data embedding and data hiding have been proposed and developed especially during the last two decades due to continually increasing needs of secure communication. Still image, audio and video files are the most promising digital mediums for steganography applications. However, video files have a vast potential for embedding secret data compared to other alternatives in terms of storage size. Selecting the most appropriate pixels is of great importance in the procedure of embedding secret data into video files. Unsuccessful pixel selection can trigger some negative spatial and/or temporal awareness, which eventually causes an ineffective data embedding process. In this paper, we have proposed and developed an effective blind steganography method, which uses an appropriate pixel selection mechanism, based on histogram techniques. The method we have proposed proves its success by means of perceptibility of the secret data in both spatial and temporal domains. Keywords: steganography, data embedding, data hiding, information hiding, histogram, digital video

1

INTRODUCTION

Advanced digital and analogue communication technologies along with the Internet enable many users to share digital media files without sacrificing their quality. However, those digital media could incorporate hidden messages for either defensive or adversary purposes. For instance, several companies use different watermarking techniques because of their commercial worries over copyright infringements.1 Another aspect is the security concerns that hidden messages can be embedded into innocent digital media files to distract inquisitive people away from the message. However; this technique, which is called steganography, can also be used by malevolent organisations such as terrorist groups.2,3

Whether in economic or in security terms, those negative aspects of sharing digital media necessitate protecting them by secure communication techniques such as watermarking and steganography. Even though both techniques resemble each other in terms of the methods used, they are employed for distinct purposes. The most important difference between each method is the risk rate of attacks. In watermarking, the attack risk is higher than in steganography since watermarked digital media has mostly commercial value and this attribute can attract many malevolent people. In steganography, however; there is no such high risk of attack due to the ordinary nature of digital media.

2 The MS was accepted for publication on 24 February 2011. * Corresponding author: Ozdemir Cetin, Computer Science, Technical Education Faculty, Sakarya University, Sakarya 54188, Turkey; email: [email protected]

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DOI: 10.1179/1743131X11Y.0000000004

RELATED WORKS

Steganography techniques have been applied on text, images, sounds and video files. In some applications, The Imaging Science Journal Vol 60

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a high capacity requirement for embedding data has directed researchers to video files.4 In video steganography, there are two alternatives for target media: raw videos [i.e. audio–video interleave (AVI)] and compressed videos (i.e. MPEG). The first steganography method applied in raw video files was based on spread-spectrum technique. In that study, the hidden data are represented by binary arrays whose size equals the carrier video. The extraction of the hidden data depends on a correlation-based method in which the hidden data cannot be obtained if one or more video frames are corrupted.5 In another study, researchers developed a steganography method against cropping attacks. The frames of carrier video are divided into sub-regions and hidden data are compressed and encrypted followed by embedding into sub-regions. The hidden data were obtained with the help of the Lagrange interpolating polynomial method.6 In our latest study, we have used histogram methods to embed the secret data into cover video. As research objectives, we have tried to maximise the embedding capacity along with minimising the perceptibility level. In order to achieve these objectives, the cover video file is divided into frames and each frame’s histogram value is calculated to decide whether the frame is beyond the human vision system (HVS). Having completed this step, the hidden data are embedded into the frames that are beyond HVS.7 In this approach, appropriate frames are used to embed hidden data in respect to their histogram values. However, this cannot guarantee successful steganography since each region in the frames cannot be suitable as required. If these occurrences are neglected, inappropriate regions in the frames can engage attentions of third parties and this ultimately increases the perceptibility level of hidden data. Owing to many continual improvements in compression algorithms and widespread use, steganography methods have been utilised in compressed video standards such as MPEG1, MPEG2 and MPEG4. In order to embed data into compressed video files, frequency-based methods are mainly used: discrete cosine transform, discrete wavelet transform and Fourier transform.8 As a first example, Swanson proposed a wavelet transform technique to embed data into digital video media in Refs. 9 and 10, where low-pass and highpass frames are obtained by applying temporal wavelet transform to each frame. In the extraction process of data, the original video is needed though. The Imaging Science Journal Vol 60

The most critical and interrelated parameters for data hiding/embedding methods are security, reliability, invisibility, complexity and data-embedding capacity.11 If the data embedding capacity is the most desired factor in secret communication, compressed video files cannot promise much space in contrast to raw video files. On the other hand, compressed video files can be used in watermarking applications because of low capacity requirements for copyright data. In this paper, different from earlier studies, we have used a new approach based on HVS to determine appropriate cover video pixels, which are utilised as storage space in data embedding procedures. In this approach we propose, appropriate pixels in the cover video are determined by means of histogram values of each frame; however; we have focused on data embedding purposes for secure communication.12 The primary objective of our study is to keep the perceptibility level of cover video files at a minimum especially when potential malicious attacks are expected. In order to achieve this objective, a criterion namely, peak signal-to-noise ratio (PSNR), was utilised to evaluate the perceptibility levels of stego video and cover video.

3 3.1

THE FUNDAMENTALS OF DIGITAL VIDEO Digital video

A digital video is composed of a series of motionless images, called as frames, which are played in consecutively within a standard speed. The frame rate of the video must be over a certain speed because of some visual discomforts caused by HVS. The more the rate of video frames increases, the more comfortable the HVS gets. However, after certain rates of the frames, there is no effect on HVS in terms of perceptibility. A colour pixel in a video frame has three colour compounds in red–green–blue (RGB) standard; however, in cyan–magenta–yellow–black (CMYK) standard, the pixel is composed of four colour compounds. Other colours are obtained the mixture of these primary colours. The colour is represented by many formats such as 16-, 24- and 32-bit. For instance, in 16-bit standard, each colour compound is represented by the following rules: red is represented by 5 bits, green by 6 bits and blue by 5 bits as seen in Table 1. Since green colour occupies wider IMAG 60 # RPS 2012

A BLIND STEGANOGRAPHY METHOD

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divided into four identical squares. Each square contains 2500 pixels (50650) with four distinct grey levels, namely, 50, 100,150 and 200. The histogram graph of the squares is illustrated below the image and as seen in the graph, each grey level corresponds to equal number of pixels, i.e. 2500.

4 PROPOSED DATA EMBEDDING ALGORITHM 1 The pixel map of a digital image7

spectrum and is located in the middle of the vision spectrum, human eyes are more comfortable with green colour compared to others. Therefore, in 16-bit colour standard, green colour is represented by more bits. In true colour standard, each colour compound consists of 8 bits and each pixel is composed of 24 bits; 32-bit colour depth can also be used especially in computer displays in which extra 8 bits represent the transparency.7,13 A digital image along with its zoomed-in region and the compounds of blue colour dominant pixel illustrated in RGB standard is given in Fig. 1. Most common raw video format used in steganography applications are AVI of which video frames are composed of consecutive bitmap images. In this study, we have used ‘Vipmen.avi’, ‘Foreman.avi’ and ‘Miss.avi’ video files, which are very popular test video files among researchers, during the experimental work. Each video stream used is given in Fig. 2. 3.2

Histograms for digital image

A histogram is a presentation of the frequency components of a signal. As for a digital image, a histogram is a distribution array incorporating colour components of each pixel. Thus, several deductions can be performed by means of the distribution array created. An example of grey-scale image, which is composed of 1006100 pixels, accompanying with its colour histogram graph, is illustrated in Fig. 3. The digital image includes 10 000 pixels and the image is Table 1 Bit order Colour intensity

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15 Red

14

13

12

11

In recent steganography studies, researchers have focused on adaptive steganography techniques and many algorithms have been developed using HVS mechanisms.14 The key objective in adaptive steganography is to give priority to the criteria of HVS. Thus, the perception of secret data in the cover video by unauthorised people can be avoided to a great extent. This histograms approach is one of the techniques used in adaptive steganography.7 For this approach, an HVS-based data-embedding algorithm was developed.2 In Fig. 4, the steps of the proposed algorithm are illustrated as a block diagram. As seen in Fig. 4, the secret data are embedded into the cover video by way of coding and embedding algorithms. At the receiver side, the secret data are extracted from the stego video using the same algorithms. 4.1

Regional histogram optimisation method

Colour histogram is a set of values that represents the distribution of colours in an image according to their colour intensity. In histogram-based methods, histogram values for each consecutive frame in a cover video are calculated first and then evaluated. Once histogram values belonging to each colour compounds (R,G,B) are acquired, the mean values of three compounds are obtained. Mean values can be used to evaluate the transition of colours and motions in the video frames. While larger histogram values suggest more colour or motion transitions, smaller histogram values imply either little or no transition between consecutive frames. A numerical value is utilised as a threshold value to detect colour or motion transitions in video frames. The maximum

Bit order of 16-bit RGB colour 10 09 Green

08

07

06

05

04 Blue

03

02

01

00

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2 Video frames of Vipmen.avi (a), Foreman.avi (b) and Miss.avi (c)

threshold value can be as high as the number of total pixels in video frames (M6N). By comparing threshold values with histogram values, the appropriate frames and pixels, which are used as storage space for secret data, are determined. In Fig. 5, a set of three consecutive digital video frames is given. Note that there is no substantial transition between the first and second video frames;

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therefore; histogram difference between first and second frames is insignificant. However, the third frame is quite dissimilar with respect to histogram values of the first and second frames because of the absence of the car on the lower left. The data embedding procedure is unsuccessful when applied to the frames that have minor histogram differences (i.e. frames dominated by

An image of 1006100 pixels and its grey-scale histogram

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4 General block diagram for the proposed algorithms7

uniform colours) since temporal and spatial perceptibility is increased substantially. The regional histogram optimisation method is one of the strong alternatives to these drawbacks. In this method, each frame is divided into predetermined and identical blocks once a set of required numerical calculations is realised. Algorithm-1 reveals the sequence of related procedures to obtain regional blocks.7 The histogram dispersion of each block in the frames represents the colour contents of that block. If this dispersion degree is lower than the specified threshold value, the block is assumed to have uniform colours and eventually, it is not considered a proper block for data-embedding procedures. On the contrary, the block can be used as target storage for data embedding. Uniform coloured videos have a high risk of revealing their embedding data; however, an object itself in front of such a background or the edges of that object can be utilised safely. For example, as seen in Fig. 6, data embedding can be performed in the fifth and eighth blocks since the rest of the blocks do not have a high enough degree of colour dispersion. By using regional block methods, we have intended for decreasing the perceptibility level along with keeping the embedding capacity as high as possible.

5

EXPERIMENTAL RESULTS

In this section, we present obtained results of the proposed method by using ‘Vipmen.avi’, ‘Foreman.avi’ and ‘Miss.avi’ video files. The number of frames for ‘Miss’ and ‘Foreman’ files is 150 and 400 frames respectively and their frame size is 1446176. On the other hand, ‘Vipmen’ video file has 283 frames and a frame size of 1606120. In the evaluation stage of the experiments, we utilised PSNR which is an important parameter used to measure the performance of steganography.15 In our experimental set-up, PSNR presents estimated similarity ratio between the original and stego image. As PSNR gets higher, the similarity of each video stream is considered higher as well. To determine the PSNR value between two distinct images, initially mean squared error (MSE) value, which can be obtained by using either equation (1) or (2), must be calculated.16 {1 X n{1 1 mX MSE~ kI ði, j Þ{K ði, j Þk m|n i~0 j~0

P MSE~

2

(1)

½I ði, j Þ{K ði, j Þ2

M,N

(2)

M|N

where I and K stand for images to be compared, and I

5 A set of consecutive video frames with different scene transitions and histograms

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have also employed a few participants to assess stegovideo quality and eventually the quality of the proposed data-embedding methods. 5.1 Performance analysis of the regional histogram optimisation method

6 A video frame divided into blocks

represents cover video and K represents stego video. The dimension of video is m6n. Having obtained MSE, PSNR parameter then can be calculated by the help of equation (3).17,18 MAX2 PSNR~10log10 (3) MSE where MAX represents the pixel size of the image. For example, MAX is 255 when 8-bit colour intensity is used. In our application software, images are converted into a colour space that separates the intensity portion of the image from the colour compound and then PSNR parameter is computed from the intensity portion of the images. The number of deteriorated bits depends on hidden data capacity and the perceptibility level. In fact, conventional PSNR measurements do not correspond to an individual’s perception. Therefore, we

In experimental works, the ‘Vipmen.avi’, ‘Foreman.avi’ and ‘Miss.avi’ video files of which characteristics are given above have been used as the cover data throughout the test procedures. Figure 7 has been created by using three parameters: histogram constant value (HCV), the size of embedding data capacity and the number of deteriorated bits. HCV parameter, which affects directly the perceptibility level of hidden data in the cover video, is chosen as an evaluation criterion for regional histogram method. The graph in Fig. 7 demonstrates the optimum result obtained from carrier video files in regional histogram method. It also demonstrates the embedding data capacity along with the number of deteriorated bits for three video streams. As HCV increases, the perceptibility level of the hidden data decreases, or in other words, the security level of the application is raised. While ‘Foreman.avi’ file has the highest embedding data capacity for each HCV value, ‘Miss.avi’ video file has the lowest as seen in Fig. 7. This occurrence implies a crucial fact that the number of total deteriorated bits is higher in ‘Foreman.avi’

7 Regional histogram optimisation method analysis by using the HCV parameter


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PSNR values obtained from regional histogram optimisation method when HCV is the highest 9

compared to ‘Miss’ video stream. This fact can be utilised to determine the video file that is appropriate for a specific application. For example, ‘Foreman.avi’ is more appropriate for applications where large embedding capacity is the most essential. In contrast, ‘Miss.avi’ is preferred for highly secure applications. 5.2

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PSNR analysis for regional histogram method

We have applied some statistical methods on ‘Vipmen’, ‘Foreman’ and ‘Miss’ video streams and implemented PSNR statistics to compare our results with the published results in the recent studies. Following a successful compression process for the lossy image/video data, PSNR values between 30 and 50 dB can be considered acceptable.17,18 PSNR values calculated from the proposed regional histograms are illustrated in Figs. 8 and 9. HCV parameter is adjusted to the highest and lowest values respectively. Also the graphic lines are normalised with that of the acceptable value of 40 dB, which is indicated by the pink straight line. The values in Fig. 8 are considerably higher than 40 dB. As test procedures, we have embedded identical secret files into each cover video. When HCV parameter is adjusted to the maximum level, the hidden data capacity is decreased to the minimum as seen in Fig. 8. Another deduction for Fig. 8 suggests that the hidden capacity is also affected by the number of frames in the cover video files. Note that, for the sake of effective embedding procedures, PSNR parameter should be maintained as high as possible. According IMAG 60 # RPS 2012

PSNR values obtained from regional histogram optimisation method when the HCV is the lowest

to results shown in Fig. 8, the regional method demonstrates comparatively better PSNR values than those of the rest in most cases for ‘Vipmen’ video. In Fig. 9, 40-dB quality value is also represented by a pink line. When HCV is altered to the minimum, the hidden capacity is increased to the maximum. According to Fig. 9, ‘Miss.avi’ video file, which achieves the best scores, can be utilised when minimum HCV level is selected in regional histograms method.

6

CONCLUSION

In this paper, we have developed a histogram-based data-embedding algorithm to keep the perceptibility level of hidden data in the cover video at a minimum. In the algorithm developed, the cover video frames are divided into sub-regions and their histogram values are calculated separately in order to determine the most appropriate regions for embedding hidden data. HCV, which is a parameter of the communication channel or a histogram constant, can be modified to determine the appropriate security level. The size of the hidden data can also be determined by the HCV parameter that is inversely proportional to the security level. PSNR analysis and experiments have proved that our obtained PSNR values are between 30 and 50 dB and therefore, these values can be considered acceptable for almost all steganography applications. The Imaging Science Journal Vol 60

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The algorithm can use the following file extensions as hidden data sources: ‘rar’, ‘doc’, ‘pdf’, ‘htm’, ‘mp3’ and ‘xls’. In addition, a hidden file with .rar extension can promise further security along with capacity levels for steganography applications.

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