Data hiding encompasses a wide range of applications for embedding messages in content. However, hiding data inevitably destroys the host image, even though the distortion is imperceptible. To enhance the hiding capacity and maintain the quality of the host image after embedding hidden data, in this paper, we present a high payload reversible data hiding scheme that is based on the absolute moment block truncation coding (AMBTC) compression domain. We explore the redundancy in a block of AMBTC-compressed images to determine if a block is embeddable or non-embeddable. Next, we create four disjoint sets for embeddable blocks to embed data using different combinations of the mean value and the standard deviation. Performance comparisons with other BTC-based schemes are provided to demonstrate the superiority of the proposed scheme.

A novel reversible data hiding scheme based on AMBTC compression technique

This paper presents an improved image steganography scheme based on absolute moment block truncation coding (AMBTC). The purpose of the proposed scheme is to achieve high payload, good visual quality and low computation complexity at the same time. In the scheme, a threshold is predefined to classify the blocks of the AMBTC-compressed codes as smooth or complex blocks, in which data are then embedded. For the smooth blocks, the bit planes of them are used to embed the data. Later, the two quantization levels in the smooth block are re-calculated to minimize the distortion in image quality. For the complex blocks, a proportion of secret bits are concealed by exchanging the order of two quantization levels with together toggling the bit plane, by which the payload can be increased without any distortion. Furthermore, the proposed scheme inherits the advantages of the AMBTC method, such as pleasing image quality, ease to be implemented and low computational complexity. With adjustable threshold, the application of the proposed scheme becomes flexible, that means different thresholds can be used for different applications. Experimental results and analysis demonstrate the effectiveness and superiority of the proposed scheme.

High payload image steganography with minimum distortion based on absolute moment block truncation coding

This paper presents a lossless data hiding method for an absolute moment block truncation coding (AMBTC) images, which is a compressed grayscale image. It is not easy to hide secret data in an AMBTC-compressed image because it is composed of bit planes. Thus, it is very sensitive to change some pixels. Nevertheless, to improve the hiding capacity, we present an efficient extension of the histogram modification technique by counting the coefficients of the bit planes in each 4 × 4 block. In addition, our proposed scheme has low complexity and achieves a high embedding capacity with the good perceptual quality compared to the prior arts. In an experiment, we verified our proposed data hiding scheme through the comparison with previous methods.

Lossless data hiding for absolute moment block truncation coding using histogram modification

In this paper, a novel steganographic scheme is proposed based on chaotic map in the DCT domain. The proposed method apply the DCT on the cover image, scan the AC coefficients in a zigzag form from the least significant to the most significant one, this scan will lead us eventually to precise the embedding positions through a chaotic function as well as the maximum allowed payload relative to a computed SSIM threshold. A quantitative study shows the check of the designed method to the requirement of imperceptibility and flexibility.

A new adaptive image steganography scheme based on DCT and chaotic map

In this paper, we propose a new Absolute Moment Block Truncation Coding (AMBTC) based data hiding scheme using hamming distance and pixel value differencing methods. The proposed method firstly pre-processes the original image using a smoothing filter so that its quality can be maintained after AMBTC compression. It then applies AMBTC compression on the processed image and uses two thresholds to categorize the image blocks into three categories namely Smooth, Less_Complex, and Highly_Complex blocks so that the complex block can also be used to embed the secret data. The secret data is then embedded into the smooth blocks using simple replacement strategy. Further, the proposed method embeds 8 bits of secret data into the bit plane of Less_Complex blocks using hamming distance calculation and few bits into the quantization levels of Highly_Complex blocks using pixel value differencing (PVD) method to increase the data hiding capacity without having any major impact on image quality. Thus, the proposed method additionally embeds on average 6 more bits in complex blocks. Experimental results also prove that the proposed method is superior to the contemporary AMBTC based data hiding methods in term of data hiding capacity and has comparable image quality.

Enhanced AMBTC based data hiding method using hamming distance and pixel value differencing

Reversible data hiding has been a hot research topic because it can recover both the host media and hidden data without distortion. Because most digital images are stored and transmitted in compressed forms, such as JPEG, vector quantization, and block truncation coding (BTC), the reversible data hiding schemes in compressed domains have been paid more and more attention. Compared with transform coding, BTC has a significantly low complexity and less memory requirement, it therefore becomes an ideal data hiding domain. Traditional data hiding schemes in the BTC domain modify the BTC encoding stage or BTC-compressed data according to the secret bits, and they have a relatively low efficiency and meanwhile may reduce the image quality. This paper presents a novel reversible data hiding scheme based on the joint neighbor coding technique for BTC-compressed images by further losslessly encoding the BTC-compressed data according to the secret bits. First, BTC is performed on the original image to obtain the BTC-compressed data that can be represented by a high mean table, a low mean table, and a bitplane sequence. Then, the secret data are losslessly embedded in both the high mean and low mean tables. Our hiding scheme is a lossless method based on the relation among the current value and the neighboring ones in mean tables. In addition, it can averagely embed 2 bits in each mean value, which increases the capacity and efficiency. Experimental results show that our scheme outperforms three existing BTC-based data hiding works, in terms of the bit rate, capacity, and efficiency.

High performance reversible data hiding for block truncation coding compressed images

This paper presents a new approach for hiding information in digital audio based on the audio-to-image wavelet transform (A2IWT) and vector quantization (VQ). In our scheme, the cover audio signal is first transformed into an image by utilizing the wavelet transform and re-sampling the coefficients, then the secret data are embedded in the obtained image using a VQ-based image steganographic scheme, and finally the image together with the remaining wavelet coefficients is inversely transformed back to a stego audio signal. Experimental results show that the proposed scheme is effective and efficient.

Data Hiding in Audio Based on Audio-to-Image Wavelet Transform and Vector Quantization

Audio fingerprinting is the process to obtain a compact content-based signature that summarizes the essence of an audio clip. In general, existing audio fingerprinting schemes based on wavelet transforms are not robust against large linear speed changes. The authors present a novel framework for content-based audio retrieval based on the audio fingerprinting scheme that is robust against large linear speed changes. In the proposed scheme, 8 levels Daubechies wavelet decomposition is adopted for extracting time-frequency features and two fingerprint extraction algorithms are designed. The experimental results from this study are discussed further into the article. DOI: 10.4018/jdcf.2012040104 50 International Journal of Digital Crime and Forensics, 4(2), 49-69, April-June 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. local energy centroid (LEC) was proposed to represent the energy conglomeration degree of the relative small region in the spectrum (Pan et al., 2011), while a robust audio fingerprinting algorithm in the MP3 compressed domain was proposed with high robustness to time scale modification (Zhou & Zhu, 2011). Among existing transform-based audio fingerprinting schemes, the schemes based on the wavelet transform are very popular, since the wavelet transform or more particularly the discrete wavelet transform is a relatively recent and computationally efficient technique for extracting information about non-stationary signals like audio. Wavelet transform is a local transformation on a signal in time and frequency domains, which can effectively extract information from the signal, and do multi-scale detailed analysis on a function or signal by functions such as scaling and translation, thereby can solve many difficult issues which cannot be solved by the Fourier transform. Therefore, our paper focuses on the wavelet transform based schemes. The existing works based on wavelet transforms can be classified into the following two categories. The first type of fingerprinting schemes performs the wavelet transform on each audio frame directly to extract time-frequency features for audio fingerprinting. In Lu (2002), the one dimensional continuous Morlet wavelet transform is adopted to extract two fingerprints for authentication and recognition purposes, respectively. In Ghouti and Bouridane (2006), a robust perceptual audio hashing scheme using balanced multiwavelets (BMW) is proposed. They first perform 5 levels wavelet decomposition on each audio frame and divide the 5 decomposition sub-bands’ coefficients into 32 different frequency bands. Then the estimation quantization (EQ) with a window of 5 audio samples is adopted. Finally, 32 bits subfingerprinting is extracted according to the relationship between the log variances of each sub-bands’ coefficients and the mean of all the log variances for each audio frame. They do several experiments to demonstrate that their scheme is robust to several signal processing attacks and manipulations except for linear speed change. The other type of fingerprinting schemes introduces the computer vision technique to convert the audio clip into a 2-D spectrogram and then apply the wavelet transform. In Ke et al. (2005), the spectrogram of each audio snippet is viewed as a 2-D image and the wavelet transform is used to extract 860 descriptors for a 10 seconds audio clip. Then apply the pairwise boosting scheme to learn compact, discriminative, local descriptors that are efficient in audio retrieval. This algorithm can finish retrieving quickly and accurately in practical systems with poor recording quality or significant ambient noises. In Baluja and Covell (2006, 2007), the so-called Waveprint, combining of computer vision and data stream processing, was proposed. The Harr wavelet is used for extracting the t top magnitude wavelets for each spectral image. And the selected features are modeled by the Min-Hash technique. In the retrieval step, the locality sensitive hashing (LSH) technique is introduced. This algorithm exhibits an excellent identification rate against content-preserving degradations except for linear speed changes. Furthermore, the tradeoffs between the performance, memory usage, and computation are analyzed through extensive experiments. As an extension, the parameters of the system are analyzed and verified in Baluja and Covell (2008). This system shows superiority in terms of memory usage and computation, while being more accurate when compared with Ke et al. (2005). From above, we can obviously know that the existing works based on wavelet transforms are not robust against large linear speed changes in common. Based on this, a novel framework of audio fingerprinting which is robust against large linear speed changes is proposed in this paper. We adopt the Daubechies wavelet transform Daub8 in our framework. Compared with the Haar wavelet transform, the scaling signals and wavelets for the Daubechies wavelet transforms have slightly longer supports, i.e., averages and differences are produced using just a few more values from the signal. This 19 more pages are available in the full version of this document, which may be purchased using the \"Add to Cart\" button on the product's webpage: www.igi-global.com/article/daubechies-wavelets-basedrobust-audio/68409?camid=4v1 This title is available in InfoSci-Journals, InfoSci-Journal Disciplines Computer Science, Security, and Information Technology, InfoSci-Select, InfoSci-Surveillance, Security, and Defense eJournal Collection, InfoSci-Computer Science and IT Knowledge Solutions – Journals. Recommend this product to your librarian: www.igi-global.com/e-resources/libraryrecommendation/?id=2

Rong-Jun Shen

Papers

High performance reversible data hiding for block truncation coding compressed images

Data Hiding in Audio Based on Audio-to-Image Wavelet Transform and Vector Quantization

Daubechies Wavelets Based Robust Audio Fingerprinting for Content-Based Audio Retrieval