Absolute Moment Block Truncation Coding Research Articles

A reversible data hiding method based on bitmap prediction for AMBTC compressed hyperspectral images

In the transmission of hyperspectral images that have been compressed using absolute moment block truncation coding (AMBTC), confidentiality and security of crucial information is often a concern. Although many data hiding (DH) methods based on AMBTC work well in guaranteeing a large amount of secret information can be embedded, the requirements of actual user scenarios, such as reversibility and imperceptibility, are degraded sometimes. To address these challenges, we propose an embedding pattern that utilizes bitmap prediction to embed secret information within the bitmaps while preserving the standard format of AMBTC codes and enabling recovery of the cover image without loss. Our proposed method, therefore, belongs to the category of reversible data hiding (RDH) techniques. Since the embedding distortion (ED) reduction is an essential object, an adaptive embedding order based on the features of AMBTC codes is conducted. Furthermore, we propose a dynamical embedding scheme to reduce ED when we are striving to achieve a larger embedding capacity (EC).

A High-Payload Data Hiding Scheme Based on Absolute Moment Block Truncation Coding for Minimizing Hiding Impact

Data hiding encompasses a wide range of applications related to hiding messages in digital images. The visual redundancy of images makes it possible to embed data in the images without attracting attention. Increasing the hiding capacity and decreasing the hiding distortion are prime objectives that data hiding intends to achieve. In this paper, we propose a high-payload data hiding scheme based on absolute moment block truncation coding (AMBTC) to minimize the impact of hiding. A two-level minimum mean square error (MMSE) quantizer generated by AMBTC is used to decrease the distortion associated with hiding. Also, we present a lookup table based on the symmetric property for adaptively hiding secrets in pixels to achieve high hiding capacity. We can embed almost 1.9 bits per pixel (bpp) with a high image quality of an average of 31 dB. Only 5.3% of pixels are changed during the data-hiding process. Compared with other schemes, we can use 1 bpp more relative payload for embedding with the same stego image quality. The experimental results show that the proposed scheme has better hiding performance because it allows a huge amount of secret data to be hidden while maintaining the high visual quality of the stego image.

Hiding Coded Speech in Color Images using Zaslavsky-Map and AMBTC Technique

The idea of hiding information is to shield a powerful message in public records. The present application-based hidden information is sensitive information disguised in many testing sectors, such as watermarking, fingerprinting, and steganography, which is the art of covered or hidden writing, which is covert contact with coded communications to hide the presence of a document on a communication channel from hostile attackers. This paper presents hiding speech in a coded image, depending on the quantization and discrete wavelet transformation (DWT). The proposed method used the discrete wavelet transform on a three-color band of covered images. Moreover, the term AMBTC which refers to “Absolute Moment Block Truncation Coding” is used for hiding information. The secret information is a short speech (representing a password for example) embedded based on the quantization level modification technique, which will be hidden in a codded image. The three color-bands of the image are transformed using discreet wavelet transform all decomposed regions are used for hiding except the Low-Low band is kept without any change to get high quality of the stego-image with respect to original image. In the bitmap, the secret bits of a speech data are replaced. The experimental results produced an average of PSNR for tested images is about 35.0. Secret speech is compressed using discrete cosine transform (DCT) about (35%-40%), and the result is converted to a binary form that will be embedded in a color image.

High Imperceptible Data Hiding Method Based on Combination Theory for AMBTC Compressed Images

The rapid development of digital multimedia has resulted in a massive storm of large-scale data. The data compression method reduces data size and lowers transmission costs. As a result, data-hiding research in the compression domain poses significant challenges to researchers. This work proposes a highly imperceptible data-hiding scheme for a compressed absolute moment block truncation coding (AMBTC) image. The proposed method addresses the shortcomings of the current block-based AMBTC data-hiding scheme, with an incredibly complex block as non-embedding. This is because embedding in complex blocks causes substantial distortions in the image, resulting in low imperceptibility performance. Combination theory was implemented to ensure the high imperceptibility of the modified compressed images. The experimental findings proved that the proposed method achieved high imperceptibility and high hiding capacity by modifying one pixel in a block to embed confidential bits.

Generative Adversarial and Dual Layered Deep Classification Techniques for Improving Block Constructions in Public Cloud

This paper provides the Generative Adversarial and Dual Layered Deep Classification techniques to improve the drawbacks in the methods of Absolute Moment BTC (AMBTC) technique in reconstruction error rate of standard BTC model. The image blocks generation and compression are the main phases of BTC model. This can be applied for both colour images and grey scale images. However, the conventional BTC procedures lacks for edge reconstructions and noise reductions in the output images. The first technique GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of both GA models and BTC principles improve the quality of block constructions and reconstructions significantly. The second proposed work is adopted the Dual layered Deep Classification Technique. Handling the image database with minimal storage complexity, minimal computational complexity and optimal quality is a significant task. To obtain these solutions, many image processing techniques are evolved. In the domain, image compression and decompression are more needed at any cost for effectively handling the complex image databases. E-Learning resources are widely used around the internet based knowledge sharing environments. In the E-Learning environment, multiple types of data resources are managed. Particularly, organizing the images is more crucial task where multiple qualities of images are appeared inside the E-Learning network databases. This problem expects solutions from effective image compression techniques. Block Truncation Coding (BTC) and Absolute Moment BTC (AMBTC) are the techniques provide useful and easy implementations of E-Learning based image compression platform. At the same time, they are limited to image dissimilarity rate. To maintain the quality of images in both compression and decompression phases, multilevel image analysis models and training phases are required. In this regard, this proposed system develops a Dual Layered Deep Classification and Truncation (DLDCT) technique. DLDCT comprises the baseline benefits of BTC, multi-layered Support Vector Machine (SVM) units and Deep Layered Convolutional Neural Network (DLCNN) for producing classified range of image pixels and compressing the images under controlled circumstances. This proposed DLDCT makes the image compression and decompression with determined observations. This reduces real time errors occur during image reconstruction phases. This proposed system has been implemented and compared with existing works with respect to significant performance parameters.

An Authentication Method for AMBTC Compressed Images Using Dual Embedding Strategies

In this paper, we proposed an efficient authentication method with dual embedment strategies for absolute moment block truncation coding (AMBTC) compressed images. Prior authentication works did not take the smoothness of blocks into account and only used single embedding strategies for embedment, thereby limiting image quality. In the proposed method, blocks were classified as either smooth or complex ones, and dual embedding strategies used for embedment. Respectively, bitmaps and quantized values were embedded with authentication codes, while recognizing that embedment in bitmaps of complex blocks resulted in higher distortion than in smooth blocks. Therefore, authentication codes were embedded into bitmaps of smooth blocks and quantized values of complex blocks. In addition, our method exploited to-be-protected contents to generate authentication codes, thereby providing satisfactory detection results. Experimental results showed that some special tampering, undetected by prior works, were detected by the proposed method and the averaged image quality was significantly improved by at least 1.39 dB.

Robust secret image sharing scheme resistance to maliciously tampered shadows by AMBTC and quantization

For (k, n)-threshold secret image sharing (SIS) scheme, only k or more than k complete parts can recover the secret information, and the correct image cannot be obtained if the count of shadow images is not enough or the shadow images are damaged. The existing schemes are weak in resisting large-area shadow image tampering. In this paper, we propose a robust secret image sharing scheme resisting to maliciously tampered shadow images by Absolute Moment Block Truncation Coding (AMBTC) and quantization (RSIS-AQ). The secret image is successively compressed in two ways: AMBTC and quantization. The sharing shadow images contain the sharing results of both compressed image from different parts, so that even the shadow images are faced with large-scale area of malicious tampering, the secret image can be recovered with acceptable visual quality. Compared with related works, our scheme can resist larger area of tampering and yield better recovered image visual quality. The experimental results prove the effectiveness of our scheme.

Two-layers robust data hiding scheme for highly compressed image exploiting AMBTC with difference expansion

When the secret message is placed inside a highly compressed image, the transmission will be more secure and cost efficient. In this article, two-layer data embedding and extraction approaches are provided that use a combination of Absolute Moment Block Truncation Coding (AMBTC) and Difference Expansion (DE) to enhance hiding capacity on a large scale while producing minimum distortion. The first layer embedding strategy is classified into three situations according to the amount of bit patterns that are concatenated to improve performance. DE is used to build the second layer algorithm. Bitmap has been retaining its previous layer values through DE. The two-layer embedding technique is effective for concealing large volumes of data. In comparison to existing popular AMBTC data hiding strategies, this suggested design provides a low-cost, high-capacity, two-layer efficient data embedding procedure. The anticipated consequence highlighted certain outstanding magnificent aspects in the fields of tampered detection, recovery and authentication schemes, all of which are essential to the modern technological life. This system benefits a wide range of government and business sectors, including health care, commercial security, defence, and intellectual property rights.

AMBTC-based secret image sharing by simple modular arithmetic

A distortionless secret image sharing scheme using finite field GF(PG) (PG is the largest prime less than a given number) is investigated for sharing the absolute moment block truncation coding (AMBTC) images. Two adjusting operations are devised to modify the AMBTC trios (i.e., quantization pixels and bit maps) suitable for GF(PG) sharing. Polynomials under GF(PG) are constructed for encrypting the quantization pixels and bit maps. When sharing the trios under GF(PG), the AMBTC image is perfectly recovered. Moreover, the bit map sharing under GF(PG) can be extended to GF(PS) (PS is the smallest prime larger than a given number). Another scheme using GF(PS) is constituted by combining quantization pixel sharing under GF(PG) and bit map sharing under GF(PS). But the scheme using GF(PS) is not always lossless. Experimental results show that the proposed schemes are effective. Since GF(PG) and GF(PS) are simple modular arithmetic, improved computational efficiency is obtained.

High Capacity Reversible Data Hiding Scheme based on AMBTC for Encrypted Images

<p>Reversible data hiding for encrypted images is a technique that allows for exact extraction of embedded data and precise reconstruction of the encrypted image to its original version. In this paper, we propose a novel, reversible data hiding technique based on an absolute moment block truncation coding (AMBTC) algorithm for encrypted images. In the proposed scheme, each selected block is represented by an AMBTC code to vacate space to embed data after image encryption. Upon obtaining the encrypted image with the embedded data, the receiver can separately extract the embedded secret data and also recover the cover image losslessly. The experimental results for the proposed scheme achieved excellent performance. The proposed scheme is significantly superior to state-of-the-art schemes in terms of embedding capacity and image quality.</p> <p> </p>

Local feature based self-embedding fragile watermarking scheme for tampered detection and recovery utilizing AMBTC with fuzzy logic

The aim of designing self-embedding fragile watermarking is copy-right protection, tamper localization and image recovery. This paper proposes a self-embedding fragile watermarking scheme for tamper detection and recovery based on local image characteristics using Absolute Moment Block Truncation Coding (AMBTC) and fuzzy logic. The original image is divided into (4×4) non-overlapping blocks. The watermark of each block are generated from 6 MSBs of each pixels of the block. Using similarity matrix, the blocks are categorized into smooth and complex depending on similarity among neighboring pixels. In smooth block, mean is used as recovery data for their high similarity existence whereas complex block uses AMBTC image as it has a compressed representation of an image and preserves the features of the cover image. Recovery data are embedded into 2 LSBs of corresponding mapped block. The efficiency of the proposed scheme is analyzed in terms of Tamper Detection Rate (TDR) and Tamper Recovery Rate (TRR). The performance of the proposed scheme is analyzed against different attacks applied on different regions of the watermarked image. The experiment results show that the imperceptibility and visual quality of the watermarked image as well as recovered image is satisfactorily high even at 50% tampering rate.

Data Hiding in AMBTC Images Using Selective XOR Hiding Scheme

Nowadays since the Internet is ubiquitous, the frequency of data transfer through the public network is increasing. Hiding secure data in these transmitted data has emerged broad security issue, such as authentication and copyright protection. On the other hand, considering the transmission efficiency issue, image transmission usually involves image compression in Internet-based applications. To address both issues, this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding (AMBTC). First, an image is divided into non-overlapping blocks through AMBTC compression, the blocks are classified four types, namely smooth, semi-smooth, semi-complex, and complex. The secret data are embedded into the smooth blocks by using a simple replacement strategy. The proposed method respectively embeds nine bits (and five bits) of secret data into the bitmap of the semi-smooth blocks (and semi-complex blocks) through the exclusive-or (XOR) operation. The secret data are embedded into the complex blocks by using a hidden function. After the embedding phase, the direct binary search (DBS) method is performed to improve the image quality without damaging the secret data. The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.

Asymmetric Data Hiding for Compressed Images with High Payload and Reversibility

Hiding secret data in digital images is an attractive topic in the information security research area. Because the data-embedded stego image looks exactly the same as a regular image, transmitting secret data with stego images does not draw the attention of eavesdroppers, thus fulfilling the goal of information security. Many reversible data hiding (RDH) methods for absolute moment block truncation coding (AMBTC) compressed images have been proposed. These methods hide secret data in an AMBTC-compressed image to produce a stego image and transmit it to the recipient. Upon receiving the stego image, the recipient can extract the secret data and recover the AMBTC-compressed image. In this paper, we propose an RDH scheme for AMBTC-compressed images with an asymmetric embedding rule. Using the AMBTC-compressed version as the basis, the proposed embedding scheme always modifies a pixel value toward its original value with a step size (bitrate) proportional to the gap width. Therefore, the visual quality of the stego image is better than the referred AMBTC version. Additionally, as a result of the adaptive bitrate strategy, the data embedding capacity of the proposed scheme outperforms that of state-of-the-art methods. The security of the resulting stego images was also tested by RS-steganalysis. Experimental results show that the overall performance of the proposed scheme is satisfactory. We revised it, please confirm.

A High Fidelity Authentication Scheme for AMBTC Compressed Image Using Reference Table Encoding

In this paper, we propose a high-quality image authentication method based on absolute moment block truncation coding (AMBTC) compressed images. The existing AMBTC authentication methods may not be able to detect certain malicious tampering due to the way that the authentication codes are generated. In addition, these methods also suffer from their embedding technique, which limits the improvement of marked image quality. In our method, each block is classified as either a smooth block or a complex one based on its smoothness. To enhance the image quality, we toggle bits in bitmap of smooth block to generate a set of authentication codes. The pixel pair matching (PPM) technique is used to embed the code that causes the least error into the quantization values. To reduce the computation cost, we only use the original and flipped bitmaps to generate authentication codes for complex blocks, and select the one that causes the least error for embedment. The experimental results show that the proposed method not only obtains higher marked image quality but also achieves better detection performance compared with prior works.

Pixel-based fragile image watermarking based on absolute moment block truncation coding

Pixel-based fragile image watermarking based on absolute moment block truncation coding

Security for eHealth system: data hiding in AMBTC compressed images via gradient-based coding

Various eHealth applications based on the Internet of Things (IoT) contain a considerable number of medical images and visual electronic health records, which are transmitted through the Internet everyday. Information forensics thus becomes a critical issue. This paper presents a data hiding algorithm for absolute moment block truncation coding (AMBTC) images, wherein secret data, or the authentication code, can be embedded in images to enhance security. Moreover, in view of the importance of transmission efficiency in IoT, image compression is widely used in Internet-based applications. To cope with this challenge, we present a novel compression method named gradient-based (GB) compression, which is compatible with AMBTC compression. Therefore, after applying the block classification scheme, GB compression and data hiding can be performed jointly for blocks with strong gradient effects, and AMBTC compression and data hiding can be performed jointly for the remaining blocks. From the experimental results, we demonstrate that the proposed method outperforms other state-of-the-art methods.

A high payload reversible data hiding algorithm for homomorphic encrypted absolute moment block truncation coding compressed images

With the aim to ensure privacy and security of secret message, an enhanced reversible data hiding algorithm in encrypted as well as compressed domain is proposed here. After compression of grey-scale image using absolute moment block truncation coding (AMBTC) method, pixels of AMBTC compressed image is segmented into two types - seed pixels (2k…255) and remaining pixels are called non-seed pixels. Now, embedded k, (k ≥ 1) binary bits of secret message by changed over it into base10 numeral framework at seed pixels of AMBTC compressed image whereas seed pixel is divided into two individual units which are encrypted through Paillier cryptosytem separately. Highlight of proposed method is to embed variable size secret message at seed pixels of AMBTC compressed image without any occurrence of overflow problem. Experimental study revealed that for all type of test images, proposed method altogether beated all the compared methods in its ability to embed secret message and precisely recover it with a PSNR value of $\infty $ dB between cover image and reconstructed image too.

Pixel P Air-Wise Fragile Image Watermarking Based on HC-Based Absolute Moment Block Truncation Coding

In this paper, we first designed Huffman code (HC)-based absolute moment block truncation coding (AMBTC). Then, we applied Huffman code (HC)-based absolute moment block truncation coding (AMBTC) to design a pixel pair-wise fragile image watermarking method. Pixel pair-wise tampering detection and content recovery mechanisms were collaboratively applied in the proposed scheme to enhance readability even when images have been tampered with. Representative features are derived from our proposed HC-based AMBTC compression codes of the original image, and then serve as authentication code and recovery information at the same time during tamper detection and recovery operations. Recovery information is embedded into two LSB of the original image with a turtle shell-based data hiding method and a pre-determined matrix. Therefore, each non-overlapping pixel-pair carries four bits of recovery information. When the recipient suspects that the received image may have been tampered with, the compressed image can be used to locate tampered pixels, and then the recovery information can be used to restore the tampered pixels.

WITHDRAWN: Multi-classifiers and deep cnns for implementing image block truncation coding in e-learning platform

Abstract Handling the image database with minimal storage complexity, minimal computational complexity and optimal quality is a significant task. To obtain these solutions, many image processing techniques are evolved. In the domain, image compression and decompression are more needed at any cost for effectively handling the complex image databases. Now days, E-Learning resources are widely used around the internet based knowledge sharing environments. In the E-Learning environment, multiple types of data resources are managed. Particularly, organizing the images is more crucial task where multiple qualities of images are appeared inside the E-Learning network databases. This problem expects solutions from effective image compression techniques. Block Truncation Coding (BTC) and Absolute Moment BTC (AMBTC) are the techniques provide useful and easy implementations of E-Learning based image compression platform. At the same time, they are limited to image dissimilarity rate. To maintain the quality of images in both compression and decompression phases, multilevel image analysis models and training phases are required. In this regard, this proposed system develops a Dual Layered Deep Classification and Truncation (DLDCT) technique. DLDCT comprises the baseline benefits of BTC, multi-layered Support Vector Machine (SVM) units and Deep Layered Convolutional Neural Network (DLCNN) for producing classified range of image pixels and compressing the images under controlled circumstances. This proposed DLDCT makes the image compression and decompression with determined observations. This reduces real time errors occur during image reconstruction phases. This proposed system has been implemented and compared with existing works with respect to significant performance parameters.

WITHDRAWN: A novel generative adversarial block truncation coding schemes for high rated image compression on E-learning resource environment

Abstract Image compression is a useful technique that is inevitable to store any images in compressed pattern. The compressed image is reconstructed using image retrieval process for any applications. This technique ensures storage efficiency in various content management applications. Particularly, E-Learning resource environment deals with more space complexity. The E-Learning storage space complexity can be reduced with the help of image compression techniques. There are different types of image compression techniques invented for building compressed resources. Block Truncation Coding (BTC) is a type of lossy compression technique for reducing grey scale quantities using blocking and quantizing phases. This is an effective technique for compressing the images. In extend, Absolute Moment BTC (AMBTC) technique is proposed for minimizing the reconstruction error rate of standard BTC model. The image blocks generation and compression are the main phases of BTC model. This can be applied for both colour images and grey scale images. However, the conventional BTC procedures lacks for edge reconstructions and noise reductions in the output images. This problem creates BTC with lack of inaccuracy in complex image compression platforms such as E-Learning image sources. In the scope, the need for more active and generative image compression procedures is necessary to improve BTC principles. This work proposes highly complex and novel Generative Adversarial BTC (GA-BTC) compression models with multiple variances. GABTC is developed with multi-layered Deep Neural Network (DNN) structures with GA neural models. The integration of both GA models and BTC principles improve the quality of block constructions and reconstructions significantly. This work evaluates the model complexity and efficiency using various E-Learning images (Colour and Grey Scale) with different compression quality measurements.