KokSheik Wong

An Overview of Information Hiding in H.264/AVC Compressed Video

Information hiding refers to the process of inserting information into a host to serve specific purpose(s). In this paper, information hiding methods in the H.264/AVC compressed video domain are surveyed. First, the general framework of information hiding is conceptualized by relating the state of an entity to a meaning (i.e., sequences of bits). This concept is illustrated by using various data representation schemes such as bit plane replacement, spread spectrum, histogram manipulation, divisibility, mapping rules, and matrix encoding. Venues at which information hiding takes place are then identified, including prediction process, transformation, quantization, and entropy coding. Related information hiding methods at each venue are briefly reviewed, along with the presentation of the targeted applications, appropriate diagrams, and references. A timeline diagram is constructed to chronologically summarize the invention of information hiding methods in the compressed still image and video domains since 1992. A comparison among the considered information hiding methods is also conducted in terms of venue, payload, bitstream size overhead, video quality, computational complexity, and video criteria. Further perspectives and recommendations are presented to provide a better understanding of the current trend of information hiding and to identify new opportunities for information hiding in compressed video.

UniSpaCh: A text-based data hiding method using Unicode space characters

This paper proposes a text-based data hiding method to insert external information into Microsoft Word document. First, the drawback of low embedding efficiency in the existing text-based data hiding methods is addressed, and a simple attack, DASH, is proposed to reveal the information inserted by the existing text-based data hiding methods. Then, a new data hiding method, UniSpaCh, is proposed to counter DASH. The characteristics of Unicode space characters with respect to embedding efficiency and DASH are analyzed, and the selected Unicode space characters are inserted into inter-sentence, inter-word, end-of-line and inter-paragraph spacings to encode external information while improving embedding efficiency and imperceptivity of the embedded information. UniSpaCh is also reversible where the embedded information can be removed to completely reconstruct the original Microsoft Word document. Experiments were carried out to verify the performance of UniSpaCh as well as comparing it to the existing space-manipulating data hiding methods. Results suggest that UniSpaCh offers higher embedding efficiency while exhibiting higher imperceptivity of white space manipulation when compared to the existing methods considered. In the best case scenario, UniSpaCh produces output document of size almost 9 times smaller than that of the existing method.

Complete Video Quality-Preserving Data Hiding

Although many data hiding methods are proposed in the literature, all of them distort the quality of the host content during data embedding. In this paper, we propose a novel data hiding method in the compressed video domain that completely preserves the image quality of the host video while embedding information into it. Information is embedded into a compressed video by simultaneously manipulating Mquant and quantized discrete cosine transform coefficients, which are the significant parts of MPEG and H.26x-based compression standards. To the best of our knowledge, this data hiding method is the first attempt of its kind. When fed into an ordinary video decoder, the modified video completely reconstructs the original video even compared at the bit-to-bit level. Our method is also reversible, where the embedded information could be removed to obtain the original video. A new data representation scheme called reverse zerorun length (RZL) is proposed to exploit the statistics of macroblock for achieving high embedding efficiency while trading off with payload. It is theoretically and experimentally verified that RZL outperforms matrix encoding in terms of payload and embedding efficiency for this particular data hiding method. The problem of video bitstream size increment caused by data embedding is also addressed, and two independent solutions are proposed to suppress this increment. Basic performance of this data hiding method is verified through experiments on various existing MPEG-1 encoded videos. In the best case scenario, an average increase of four bits in the video bitstream size is observed for every message bit embedded.

A DCT-based Mod4 steganographic method

This paper presents a novel Mod4 steganographic method in discrete cosine transform (DCT) domain. Mod4 is a blind steganographic method. A group of 2x2 spatially adjacent quantized DCT coefficients (GQC) is selected as the valid message carrier. The modulus 4 arithmetic operation is then applied to the valid GQC to embed a pair of bits. When modification is required for data embedding, the shortest route modification scheme is applied to reduce distortion as compared to the ordinary direct modification scheme. Mod4 is capable in embedding information into both uncompressed and JPEG-compressed image. To compare Mod4 with other existing methods, carrier capacity, stego image quality, and results of blind steganalysis for 500 various images are shown. Visual comparison of three additional metrics is also presented to show the relative performance of Mod4 among other existing methods.

Universal data embedding in encrypted domain

In this work, a Universal Reversible Data Embedding method applicable to any Encrypted Domain (urDEED) is proposed. urDEED operates completely in the encrypted domain and requires no feature of the signal prior to the encryption process. In particular, urDEED exploits the coding redundancy of the encrypted signal by partitioning it into segments referred to as imaginary codewords (ICs). Then, ICs are entropy encoded by using Golomb-Rice codewords (GRCs). Finally, each GRC is modified to accommodate two bits from the augmented payload. urDEED is designed to preserve the same file-size as that of the original input (encrypted) signal by embedding the quotient part of the GRCs as side information. Moreover, urDEED is consistently reversible and universally applicable to any digital signal encrypted by any encryption method. Experimental results show that urDEED achieves an average embedding capacity of ~0.169 bit per every bit of the encrypted (host) signal.

A Unified Data Embedding and Scrambling Method

Conventionally, data embedding techniques aim at maintaining high-output image quality so that the difference between the original and the embedded images is imperceptible to the naked eye. Recently, as a new trend, some researchers exploited reversible data embedding techniques to deliberately degrade image quality to a desirable level of distortion. In this paper, a unified data embedding-scrambling technique called UES is proposed to achieve two objectives simultaneously, namely, high payload and adaptive scalable quality degradation. First, a pixel intensity value prediction method called checkerboard-based prediction is proposed to accurately predict 75% of the pixels in the image based on the information obtained from 25% of the image. Then, the locations of the predicted pixels are vacated to embed information while degrading the image quality. Given a desirable quality (quantified in SSIM) for the output image, UES guides the embedding-scrambling algorithm to handle the exact number of pixels, i.e., the perceptual quality of the embedded-scrambled image can be controlled. In addition, the prediction errors are stored at a predetermined precision using the structure side information to perfectly reconstruct or approximate the original image. In particular, given a desirable SSIM value, the precision of the stored prediction errors can be adjusted to control the perceptual quality of the reconstructed image. Experimental results confirmed that UES is able to perfectly reconstruct or approximate the original image with SSIM value after completely degrading its perceptual quality while embedding at 7.001 bpp on average.

Information hiding in HEVC standard using adaptive coding block size decision

In this work, an information hiding techniques is proposed using the coding block size decision in HEVC. This approach manipulates the CB (coding block) size decision on every coding tree unit to embed information based on the predefined mapping rules. Each CB is forced to assume certain size to encode the external information without significantly compromising perceptual quality. To improve payload, the odd-even based information hiding technique is further deployed by manipulating the nonzero DCT coefficients in certain ranges, in which case each range depends on the CB size. Results suggest that by combining both approaches, improvement is achieved in the terms of payload for the higher bitrate scenario and insignificant degradation in perceptual video quality for the low bitrate scenario.

Optical strain based recognition of subtle emotions

This paper presents a novel method to recognize subtle emotions based on optical strain magnitude feature extraction from the temporal point of view. The common way that subtle emotions are exhibited by a person is in the form of visually observed micro-expressions, which usually occur only over a brief period of time. Optical strain allows small deformations on the face to be computed between successive frames although these subtle changes can be minute. We perform temporal sum pooling for each frame in the video to a single strain map to summarize the features over time. To reduce the dimensionality of the input space, the strain maps are then resized to a pre-defined resolution for consistency across the database. Experiments were conducted on the SMIC (Spontaneous Micro-expression) Database, which was recently established in 2013. A best three-class recognition accuracy of 53.56% is achieved, with the proposed method outperforming the baseline reported in the original work by almost 5%. This is the first known optical strain based classification of micro-expressions. The closest related work employed optical strain to spot micro-expressions, but did not investigate its potential for determining the specific type of micro-expression.

A Scalable Reversible Data Embedding Method with progressive quality degradation functionality

This paper proposes a novel reversible information hiding method aiming to achieve scalable carrier capacity while progressively distorting the image quality. Unlike the conventional methods, the proposed method HAM (Histogram Association Mapping) purposely degrades the perceptual quality of the input image through data embedding. To the best of our knowledge, there is no method that attempts to significantly increase the carrier capacity while introducing (tolerating) intentional perceptual degradation for avoiding unauthorized viewing. HAM eliminates the expensive pre-processing step(s) required by the conventional histogram shifting data embedding approach and improves its carrier capacity. In particular, the host image is divided into non-overlapping blocks and each block is classified into two classes. Each class undergoes different HAM process to embed the external data while distorting quality of the image to the desired level. Experiments were conducted to measure the performances of the proposed method by using standard test images and CalTech 101 dataset. In the best case scenario, an average of ~2.88 bits per pixel is achieved as the effective carrier capacity for the CalTech 101 dataset. The proposed method is also compared with the conventional methods in terms of carrier capacity and scalability in perceptual quality degradation.

Vehicle Verification Using Gabor Filter Magnitude with Gamma Distribution Modeling

This letter presents a new method to derive the image feature descriptor for vehicle verification. The effectiveness of the proposed feature descriptor is based on the nature of the Gabor filter magnitude that tends to obey the Gamma distribution. The statistical parameters of the Gabor magnitude are computed using the Maximum Likelihood Estimation (MLE), which is later utilized to construct the feature descriptor. Conventionally, the Gabor magnitude is simply modeled by using Gaussian distribution, and thus the image descriptor consists of mean, standard deviation, and skewness values of the Gabor filter magnitude. However, recent investigations found that the skewness parameter is not contributing towards class separation. Based on our observation, the Gamma distribution provides a better statistical fitting to represent the Gabor filter magnitude when compared to the Gaussian distribution. As documented in the experimental results, the proposed feature descriptor yields higher accuracy for vehicle verification when compared to the conventional schemes.