- Research Article
- Open Access
Video Data Hiding for Managing Privacy Information in Surveillance Systems
© Jithendra K. Paruchuri et al. 2009
Received: 10 May 2009
Accepted: 15 September 2009
Published: 15 October 2009
From copyright protection to error concealment, video data hiding has found usage in a great number of applications. In this work, we introduce the detailed framework of using data hiding for privacy information preservation in a video surveillance environment. To protect the privacy of individuals in a surveillance video, the images of selected individuals need to be erased, blurred, or re-rendered. Such video modifications, however, destroy the authenticity of the surveillance video. We propose a new rate-distortion-based compression-domain video data hiding algorithm for the purpose of storing that privacy information. Using this algorithm, we can safeguard the original video as we can reverse the modification process if proper authorization can be established. The proposed data hiding algorithm embeds the privacy information in optimal locations that minimize the perceptual distortion and bandwidth expansion due to the embedding of privacy data in the compressed domain. Both reversible and irreversible embedding techniques are considered within the proposed framework and extensive experiments are performed to demonstrate the effectiveness of the techniques.
Video Surveillance has become a part of our daily lives. Closed-circuit cameras are mounted in countless shopping malls for deterring crimes, at toll booths for assessing tolls, and at traffic intersections for catching speeding drivers. Since the 9–11 terrorist attack, there have been much research efforts directed at applying advanced pattern recognition algorithms to video surveillance. While the objective is to turn the labor intensive surveillance monitoring process into a powerful automated system for counter-terrorism, there is a growing concern that the new technologies can severely undermine individual's rights of privacy. The combination of ubiquitous cameras, wireless connectivity, and powerful recognition algorithms makes it easier than ever to monitor every aspect of our daily activities.
M. W. Hail has conducted a recent survey assessing citizens across demographic groups to see if they were comfortable with the expansion of government video surveillance if it protected privacy rights. (The survey was a cooperative effort through the University of Kentucky annual Kentucky Survey and the research was sponsored by a grant from the US Department of Homeland Security through the National Institute for Hometown Security.) The survey research was conducted utilizing a modified list-assisted Waksberg-Mitofsky random-digit dialing procedure for sampling and the population surveyed was noninstitutionalized Kentuckians eighteen years of age and older. The margin of error is 3.3% at the confidence interval. The respondents were asked, "Do you have a video security system that is used routinely?'' The results reflected that of employed Kentuckians have an operative video surveillance system at their workplace. We then asked of those employed, "Would you be interested in a video surveillance system at work if you knew it could protect an individual's privacy?'' The solid majority of expressed that they were interested in privacy protecting video surveillance. Urban residents, those in higher income levels, and those with advanced education attainment all were more disposed to privacy protecting video technology. Additionally, focus groups of law enforcement, first responders, hospitals, and public infrastructure managers have all reflected strong interest in privacy protecting video technology.
As explained earlier, except for the simplest organization, merely keeping the original video in encrypted form will not be sufficient in addressing these needs. On the other hand, it is advantageous to reuse the infrastructure of existing standard based video surveillance systems as much as possible. In this work, we propose using video data hiding for preserving the privacy information in the modified video itself in a seamless fashion. Using data hiding, the video bit stream will be accessible for both regular and authorized decoders but only the later can retrieve the hidden privacy information. The use of data hiding for privacy data preservation makes it completely independent from the obfuscation step unlike in some other work [10, 11]. Also, the presence of a single bit stream makes the process of video authentication much simpler to handle. Digitally signing the data hidden bit stream will authenticate the original video as well as all levels of privacy protected data.
a Privacy-Protected Video Surveillance System which can authenticate and preserve the privacy information.
a data hiding framework for managing privacy information which can support any kind of video modification.
a compression domain data hiding algorithm which offers high level of hiding capacity by embedding privacy information in selected transform coefficients optimized in terms of distortion and bit-rate.
The rest of the paper is organized as follows. First in Section 2, we briefly review the state-of-the-art in privacy protection and management systems and video data hiding. In Section 3, we describe the higher level design of our privacy protection system and its components. Section 4 introduces the data hiding framework for managing privacy information and various embedding techniques and perceptual distortion and rate models. Keeping the special constraints of data hiding for this application in consideration, we propose the optimization framework to find the embedding locations in Section 5. Experimental results are presented in Section 6 followed by conclusions in Section 7.
2. Related Work
All the afore-mentioned work target only at the modification of the video but not at the feasibility of recovering original video securely. To securely preserve the original video, selective scrambling of sensitive information using a private key have been recently proposed in [10, 11, 15]. These schemes differ in terms of the types of information scrambled which leads to different complexity and compression performances—spatial pixels are scrambled in , DCT signs and Wavelet coefficients are used in [11, 15], respectively. With the appropriate private key, the scrambling can be undone to retrieve the original video. These techniques have the advantages of simplicity with modified regions clearly marked. However, there are a number of drawbacks. First, similar to pixelation and blocking, scrambling is unable to fully protect the privacy of individuals, revealing their routes, motion, shape, and even intensity levels . Second, as obfuscation is usually the first step in a complex process chain of a smart surveillance system, it introduces artifacts that can affect the performance of subsequent image processing. Lastly, the coupling of scrambling and data preservation prevents other obfuscation schemes like object replacement or removal to be used.
Using data hiding for privacy data preservation is more flexible as it completely isolates preservation from modification. Since our introduction of using data hiding for privacy data preservation in , there have been other work like [9, 17–20] that employ a similar approach. Data hiding has been used in various applications such as copyright protection, authentication, fingerprinting, and error concealment. Each application imposes a different set of constraints in terms of capacity, perceptibility, and robustness . Privacy data preservation certainly demands a large embedding capacity as we are hiding an entire video bitstream in the modified video. Perceptual quality of the embedded video is also of great importance as it effects the usability of the video for further processing. Robustness refers to the survivability of the hidden data under various processing operations. While it is a key requirement for applications like copyright protection and authentication, it is of less concern to a well-managed video surveillance system targeted to serve a single organization. Thus, we are focusing mainly on high-capacity fragile data hiding schemes. Another dimension is the reversibility of the hiding process which dictates if the embedded video can be fully restored after the hidden data is removed. While irreversible data hiding usually produces higher hiding capacity, reversible data hiding may be important for maintaining the authenticity of the original video. We shall consider both in this work.
Most irreversible data embedding and extracting approaches can be classified into two classes—spread spectrum and quantization index modulation (QIM). Spread spectrum techniques treats the data hiding problem as the transmission of the hidden information over a communication channel corrupted by the covered data . QIM techniques use different quantization code-books to represent the covered data with the selection of code-books based on the hidden information . QIM-based techniques usually have higher capacities than spread-spectrum schemes. The capacity of any QIM scheme is determined by the design of the quantization schemes. In , the authors propose to hide large volume of information into the nonzero DCT terms after quantization. This method cannot provide sufficient embedding capacity for our application because surveillance videos have high temporal correlation with a very large fraction of DCT coefficients being zero in the intercoded frames. In , the authors propose to implement the embedding in both zero and non-zero DCT coefficients but only in macro blocks with low inter frame velocity. This framework deals only with minimizing perceptual distortion without considering the increase in bit rate. Our initial scheme in  embeds the watermark bits at the high-frequency DCT coefficients during the compression process. Similar to , this method works well in terms of maintaining the output video quality but at an expense of much higher output bit rate.
Reversible data embedding can be broadly classified into three categories. The first class of methods like [26, 27] basically use lossless compression to create space for data hiding. The key idea is to embed the recovery information along with the hidden data to enable the reversibility at the decoder. This method is not suitable for our application because of its low capacity and that the information to be embedded is already a compressed bit stream. The second class of methods like [28, 29] work on residual expansion between pairs of coefficients in various transform domains. These methods assume high correlation between coefficients, hence most of the pairs would not overflow even after expanding the difference. The drawback of these schemes is the higher perceptual distortion caused due to significant changes in coefficient values. The third category of algorithms like  work on the concept of histogram bin shifting. This is suitable for our application because the histogram of DCT residue is Laplacian so that we can hide information at small-magnitude coefficients without imposing significant perceptual distortion.
In Section 5, we describe a new approach of optimally placing hidden information in the DCT domain that simultaneously minimizes both the perceptual distortion and output bitrate. Our algorithm considers both rate and distortion and produces an optimal distribution of hidden bits among various DCT blocks. Our main contribution in the data hiding algorithm is an optimization framework to combine both the distortion and rate together as a single cost function and to use it in identifying the optimal locations to hide data. This allows a significant amount of information to be embedded into compressed bitstreams without disproportional increase in either output bit rate or perceptual distortion. This algorithm works for both irreversible and reversible embedding approaches.
3. Privacy Protected Video Surveillance
In this paper, we limit our discussion to the data hiding unit used for integrating the privacy information with the modified video. The privacy information contains the image objects of the individuals carrying the RFID tags, each padded with a black background to make a rectangular frame and compressed using a H.263 version 2 video encoder . The embedding process is performed at frame level so that the decoder can reconstruct the privacy information as soon as the compressed bitstream of the same frame has arrived. Before the embedding, the compressed bitstream for each object is encrypted using the Advanced Encryption Standard (AES) with a 128-bit key and appended with a small fixed-size header. Details of the encryption process, key management and the header format can be found in . It is this encrypted data stream that is embedded into the modified video. The data hiding scheme is combined with the video encoder and produces a H.263-compliant bitstream of the protected video to be stored in the database. The privacy protected video can be accessed without any restriction with a standard decoder as all the privacy information are encrypted and hidden in the bitstream. With a special decoder, the hidden data can be retrieved and the authorized user can decrypt the privacy information corresponding to his access level.
4. Hiding Privacy Information
4.1. Perceptual Distortion
To identify the embedding locations that cause the minimal disturbance to visual quality, we need a distortion metric to input into our optimization framework. Mean square distortion does not work for our goal of finding the optimal DCT coefficients to embed data bits—as DCT is an orthogonal transform, the mean square distortion for the same number of embedded bits will always be the same regardless of which DCT coefficients are used. Instead, we adopt the DCT perceptual model proposed by Watson , which has been shown to better correlate with the human visual system than standard mean square distortion. While there are other more sophisticated video-based perceptual models such as the one in , we adopt the Watson model for its simplicity to be included in our optimization algorithm.
4.2. Irreversible Embedding Process
This embedding, however, is not invertible. Since the quantization is altered to a coarser level as part of data embedding, it causes irrecoverable loss of data. For a single bit embedding, the maximum quantization noise doubles compared to that of without embedding. Beside the irreversible changes to the coefficient, the modified reference frame in the motion loop propagates the effect of data hiding into future frames, making the changes permanent. This implies that the reconstructed video will be slightly different from the originally compressed version. Such an irreversible embedding method is not suitable for certain applications that demand the original video to be unaltered by the data hiding process.
4.3. Reversible Embedding Process
Using the previous embedding technique, the decoder has no way to remove the distortion introduced by the embedding process. In this subsection, we explain a reversible embedding algorithm whose effect can be reversed on the decoder side after data extraction. A key requirement for our application is that the output bit-stream with hidden data must be decodable with good quality by a standard-compliant decoder unaware of the embedding. This implies that we need to avoid any error caused by drifting and as such, the decoded frame with the hidden data must be used in the feedback path in the motion loop. As the motion compensation does not respect the DCT block boundary, the effect of hiding one bit in a DCT coefficient may spread to different spatial areas after many frames. It is an open question on how to make this temporal spreading reversible. In our current implementation, we focus on making the DCT embedding process reversible and prevent temporal spreading by restricting our attention to either intracoded frames or intracoded-enhanced frames in a two-layer scalable codec.
The embedding is done only at zero coefficients while all the other coefficients visited in the scan order are displaced in either positive or negative direction. Compared with the irreversible embedding, the capacity here is smaller as data can only be embedded to zero coefficients. Also reversible embedding induces higher distortion as even some nonzero coefficients must be altered by without actually embedding at that position.
4.4. Rate Model
Data hiding effects the compression performance—simply choosing the distortion-optimal locations based on the perceptual model may increase the output bit-rate manyfold. As surveillance video is typically quite static, many DCT blocks do not have any non-zero coefficients. Hiding bits into these zero blocks, while perceptual optimal, may significantly increase the bit-rate. This is caused by the fragmentation of the long run-length patterns which are assumed to be frequent by the entropy coder. One possible approach to mitigate this problem is to limit the number of blocks to be modified . However, the fewer blocks used for embedding, the more spatially concentrated the embedding becomes which will make the distortion more visible. As such, we need to measure the increase in rate by different embedding strategies so as to produce the optimal tradeoff with the distortion. The rate increase for a particular embedding is calculated using the actual entropy coder used for compression. As both the encoder and the decoder need to compute the rate function so as to derive the optimal data hiding positions, the actual privacy data cannot be used as it is not available at the decoder. Instead, we approximate the embedding by assuming the "worst-case'' embedding, that is, we choose the hidden bit value that causes the higher increase in bit-rate.
5. Rate-Distortion-Optimized Data Hiding
where is the variable that denotes the number of coefficients to be modified, is the target number of bits to be embedded, is the cost function as described in (9), and is any selection of DCT coefficients for embedding the data. We assume that a constant number of bits are embedded at each DCT coefficient and focus the optimization on choosing the coefficients for embedding (with the exception of the last DCT coefficient for embedding which may contain less than the target number). While it is entirely feasible to explore the dimension of embedding different numbers of bits to different coefficients, our preliminary experiments indicate that the gain is too small to justify the significant expansion of the search space for the optimization.
The second problem is an efficient way to search for that provides an optimal allocation of embedded bits to each block. The following two subsections describe our approach in tackling these problems.
5.1. Cost Function Computation for DCT Blocks
There are two components to the cost function introduced in (9): distortion and rate increase due to data hiding. Our distortion function as described in (4) is additive with each coefficient having an independent contribution. The rate increase due to the modification of a coefficient is far more complex. It depends on neighboring coefficients as consecutive coefficients along the zigzag scan are encoded together as a single run-length pattern. In the H.263 standard, a run-length pattern is defined as a run of zero coefficients followed by a nonzero coefficient. The length of the run and the nonzero coefficient determine the length of the codeword, and the longer the run-length, the shorter the codeword in the Huffman table becomes. Embedding a bit in any zero coefficients will break the run-length pattern into two and the bit-rate increase will depend on the original and the resulting run-length patterns.
At first glance, the interdependency created by the run-length coding seems to evade any structural exploitation of the optimization problem. Exhaustive search of patterns, where is the number of candidate coefficients and is the number of embedded bits, seems inevitable. For a DCT block, such an exhaustive search will need to encode more than patterns in order to determine all the optimal positions for embedding bits. This is clearly impossible in practice. Fortunately, the "worst-case'' embedding assumption in our rate model as described in Section 4.4 provides a Dynamic-Programming- (DP-) based solution to the optimization problem. In the actual embedding procedure as described in (6) and (7), embedding a specific bit may turn a nonzero DCT coefficient into zero and actually reduces the bit-rate by making a run-length pattern longer. The "worst-case'' embedding, which is employed without the knowledge of the hidden bit, assumes the worst case and never makes a nonzero coefficient zero. This simple observation enables us to develop a recursive solution to the optimization problem based on the position of the last embedded bit.
This is precisely the Bellman principle that leads to a dynamic programming formulation to solve for . Now we can state the full algorithm to compute for as follows.
(1)There are 64 stages with each stage representing the embedding of one bit. At stage where , there are states representing all possible DCT coefficients in the zigzag order that can store the th embedded bit. The minimum cost function will be computed at stage and state . The trellis depicting this construction is shown in Figure 3.
(2)The calculation starts from stage one. At stage , we compute the cost function at state by first worst-case embedding a bit at the th coefficient and then identifying the minimum combined cost among all the states up to in stage plus the extra cost incurred by the embedding at the th coefficient.
5.2. Bit Allocation by Lagrangian Approximation
Sweeping through from to will examine the convex hull of all the block cost functions . While there exist efficient tree pruning techniques to search for the optimal value , the large number of DCT blocks in a frame can still render such techniques computationally intensive. As we will demonstrate in Section 6, the block cost functions in most cases can be well approximated by a second order curve. This allows us to devise a simple search strategy to quickly identify the appropriate value of .
Since the actual problem is a discrete one, we can only use from (21) as an initial slope and search for the exact slope in its neighborhood to match our target embedding requirement. At this optimal slope on each curve, we can identify the number of embedding locations for each DCT block. These embedding locations within each block are chosen from the same optimal order which are already calculated during the cost cuve generation process.
Minnesota . Two persons walk towards and cross each other while the camera is slowly panning (39 frames).
Board. One person walk across the scene, briefly occluded by a partition board (101 frames).
Two-persons. Two persons walk towards and cross each other (89 frames).
Three-persons. Two persons walk towards the right and one to the left, occluding each other briefly (73 frames).
Conference. Five persons sit around a conference table with two leaving one after the other (356 frames).
Hall. A standard sequence used in video compression (299 frames).
The data hiding algorithm is implemented based on the TMN Coder Version 3.0 of the ITU-T H.263 version 2 by University of British Columbia. All sequences are compressed using a constant quantization parameter with the first frame intracoded and the remaining intercoded. Despite the differences in the original frame-rates among the sequences, the compression frame rate has been set to 30 Hz. The encoding performance is measured based on running the program on a Windows XP Professional machine with Intel Xeon Processor at 2 GHz with 4 GB memory.
6.1. Selection of DCT Coefficients for Embedding
In the first experiment, we consider the performances among different schemes in selecting DCT coefficients to embed hidden data. The three tested schemes are the DP-based optimal scheme, the greedy scheme and the fixed reversed zigzag patterns as described in Section 5.
Comparing the performances among DP-optimal, Greedy, and Zigzag on four different in-painted sequences.
We should point out that the computational speeds provided in Table 2 are based on a nonoptimized implementation of the algorithms and also include the entire compression process, which amounts to roughly 0.6 second. Significant speedup can be achieved, for example, by updating only those blocks that are different from the previous frames as indicated by the macroblock modes. In fact, as the motion in typical surveillance videos is scarce, it is conceivable to update the cost function only occasionally rather than at every frame without losing much optimality. Furthermore, the complexity is mainly due to the computation of the cost functions for different DCT blocks which are certainly amenable to parallel implementation. While it is not the focus of this paper on the real-time implementation of the data hiding process, we believe that significant improvement in computational speed is indeed possible.
6.2. Bit Allocation for DCT Blocks
Comparing the performances between Lagrangian and Equal distribution of hidden data among DCT blocks.
6.3. Different Privacy Protection Schemes
Comparing the performances among different video obfuscation schemes.
PSNR-Y drop %
Bitrate increase %
6.4. Different Operating Parameters
Rate and Distortion for irreversible embedding Hall-Monitor at varying QP and δ values.
Rate increase %
PSNR-Y drop %
Rate and distortion for irreversible embedding for Conference at varying QP and δ values.
Rate increase %
PSNR-Y drop %
6.5. Reversible Embedding
Rate and distortion for reversible embedding using either intracoded frames (I) or enhanced intraframes (EI) at varying QP and δ values.
In this paper, we have presented a privacy-protecting video surveillance system which offers multiple levels of secure privacy information preservation. Novel irreversible and reversible data hiding methods have been proposed to hide large amount of privacy information into the host video. An optimization framework has been proposed to identify DCT coefficients for hiding information that simultaneously minimize the perceptual distortion and the rate increase caused due to embedded information. Extensive experimental results have been presented to demonstrate the efficient implementation of our algorithms and their effectiveness in preserving privacy data.
The authors would like to acknowledge the support from Department of Justice for this work and also thank the anonymous reviewers for their constructive comments.
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