Integer Wavelet Transform Research Articles

Reversible image hiding algorithm based on adaptive embedding mechanism

A reversible blind image hiding algorithm (ReBIHA) with help of an adaptive embedding mechanism and a compressive sensing technology is presented in this paper, of which can effectively hide the secret image through a natural meaningful carrier image. In the first phase, a new key generation model (KeyGM) is built by combining random numbers and the plaintext, followed by a newly designed four dimensional chaotic map (NewCM) with stronger pseudo-randomness. Then, the generated chaotic sequence is taken to produce a measurement matrix for compressive sensing. In the second phase, the plain image is sparsely processed, confused, encrypted and compressed to get measurements. Afterward, these measurements are subjected to diffusion and binary exchange to achieve a middle cipher image (MCI). In the third stage, a meaningful carrier image is randomly selected, and integer wavelet transform (IWT) is applied to it, getting one low-frequency and three high-frequency coefficients. Then, the pixels of MCI are subjected by splitting and symmetrically shifting transformation for three element matrices. Finally, these matrices are respectively embedded into the high-frequency coefficients through an adaptive mechanism to find replaceable elements, and a final carrier image hiding secrets (CiHS) can be achieved after performing an inverse IWT. The experimental results show that the proposed algorithm can avoid the extra transmission of original carrier image. Moreover, peak signal-to-noise ratio (PSNR) of reconstructed image can surpass 32 dB at compression ratio 0.5. Especially, the proposed adaptive embedding mechanism can not only achieve blind extraction of secrets well, but also ensure the visual quality for the original carrier image.

A New Face Image Manipulation Localization and Recovery Algorithm Using Image Watermarking and Integer Wavelet Transform

A New Face Image Manipulation Localization and Recovery Algorithm Using Image Watermarking and Integer Wavelet Transform

Frequency-domain image encryption based on IWT and 3D S-box

Most of the existing spatial domain image encryption techniques suffer from the difficulty of resisting cryptographic attacks. For this reason, this paper proposes a frequency-domain based digital image encryption scheme by combining Integer Wavelet Transform (IWT), three-dimensional S-box and chaotic system. First, the plaintext image is decomposed into different frequency subbands by IWT to map the digital image from spatial domain to frequency domain. Second, the plaintext hash value is selected as the dynamic key, and dynamic chaotic pseudo-random sequences with associations are generated, which are used for the encryption of each module respectively. Then, a three-dimensional S-box is designed to encrypt the information-rich low-frequency information using ‘bit-permutation three-dimensional S-box replace ciphertext interleaved diffusion’, while the high-frequency information is encrypted using a lightweight ‘XOR-row column permutation’ operation. Finally, the secure ciphertext for public channel transmission is obtained by the reconstruction method. The scheme of this paper, the frequency domain transformation is implemented through IWT, which enhances the ability to resist attacks. In addition, the diffusion encryption modules employ the introduction of ciphertext interleaved diffusion and parallel encryption mechanisms, thus the algorithm has the ability to resist plaintext attacks. Theoretical analysis and empirical results show that the algorithm has excellent numerical statistical analysis results, which corroborate that it has good confusion, diffusion and avalanche effects, and is able to resist various common cryptographic attacks. The frequency domain image encryption scheme proposed in this paper is a preferred high-security digital image privacy protection technique, so it has good application prospects.

Hybrid chaotic map with L-shaped fractal Tromino for image encryption and decryption

Insecure communication in digital image security and image storing are considered as important challenges. Moreover, the existing approaches face problems related to improper security at the time of image encryption and decryption. In this research work, a wavelet environment is obtained by transforming the cover image utilizing integer wavelet transform (IWT) and hybrid discrete cosine transform (DCT) to completely prevent false errors. Then the proposed hybrid chaotic map with L-shaped fractal Tromino offers better security to maintain image secrecy by means of encryption and decryption. The proposed work uses fractal encryption with the combination of L-shaped Tromino theorem for enhancement of information hiding. The regions of L-shaped fractal Tromino are sensitive to variations, thus are embedded in the watermark based on a visual watermarking technique known as reversible watermarking. The experimental results showed that the proposed method obtained peak signal-to-noise ratio (PSNR) value of 56.82dB which is comparatively higher than the existing methods that are, Beddington, free, and Lawton (BFL) map with PSNR value of 8.10 dB, permutation substitution, and Boolean operation with PSNR value of 21.19 dB and deoxyribonucleic acid (DNA) level permutation-based logistic map with PSNR value of 21.27 dB.

An image steganography using improved hyper-chaotic Henon map and fractal Tromino

<span lang="EN-US">Steganography is a vital security approach that hides any secret content within ordinary data, such as multimedia. First, the cover image is converted into a wavelet environment using the integer wavelet transform (IWT), which protects the cover images from false mistakes. The grey wolf optimizer (GWO) is used to choose the pixel’s image that would be utilized to insert the hidden image in the cover image. GWO effectively selects pixels by calculating entropy, pixel intensity, and fitness function using the cover images. Moreover, the secret image was encrypted by utilizing a proposed hyper-chaotic improved Henon map and fractal Tromino. The suggested method increases computational security and efficiency with increased embedding capacity. Following the embedding algorithm of the secret image and the alteration of the cover image, the least significant bit (LSB) is utilized to locate the tempered region and to provide self-recovery characteristics in the digital image. According to the findings, the proposed technique provides a more secure transmission network with lower complexity in terms of peak signal-to-noise ratio (PSNR), normalized cross correlation (NCC), structural similarity index (SSIM), entropy and mean square error (MSE). As compared to the current approaches, the proposed method performed better in terms of PSNR 70.58% Db and SSIM 0.999 respectively.</span>

Innovative chaotic dragon fractal (ChDrFr) shapes for efficient encryption applications: a new highly secure image encryption algorithm

In this paper, the generation of new dragon fractal shapes with chaotic iteration parameters is introduced as the main component of a new efficient approach for different cryptographic applications. This process involves applying a chaotic map, which is considered the initiator pattern, to generate different chaotic dragon fractal (ChDrFr) shapes in lieu of lines (which are classically used to generate dragon fractals). This is the new concept of this paper. The used chaotic maps are sensitive to their initial conditions and are characterized by randomness; hence, the resulting scheme is highly secure. As the resulting ChDrFr shapes have sparse structures, the spaces are packed with random values generated from another 5D hyper chaotic map. For encryption applications based on the substitution approach, one of the five generated ChFrDr shapes can be used to construct a chaotic fractal (ChFr) S-Box, while the other four ChDrFr shapes can be used for diffusion purposes. As an application to these new ChDrFr shapes and the ChFr S-Box, we introduce in this paper a new highly secure image encryption algorithm. A Henon chaotic map is used as the initiator of the ChDrFr shapes. The integer wavelet transform (IWT) is used to generate an approximation and three detail sub-bands for the original image. As the approximation sub-band contains a considerable amount of information about the original image, the above-described ChFr S-Box is used as a replacement for each pixel’s value in this sub-band. Then, the resultant substituted image is diffused with one of the generated ChFrDr shapes. The other three ChDrFr shapes are XORed with the details sub-images. Numerical simulation is applied to ensure the efficacy of encrypted images against different attacks. In particular, the correlation coefficient between the initial and the generated images is shown to be nearly zero. Moreover, tests reveal that the information entropy of the encrypted images and UACI were close to their optimum values. The properties of the newly proposed ChDrFr-based encryption algorithm are compared to the ones obtained by other encryption algorithms, and the results prove the superiority of this newly proposed algorithm to other types of encryption methods.

An Optimized Medical Image Watermarking Approach for E-Health Applications

Background: In recent years, information and communication technologies have been widely used in the healthcare sector. This development enables E-Health applications to transmit medical data, as well as their sharing and remote access by healthcare professionals. However, due to their sensitivity, medical data in general, and medical images in particular, are vulnerable to a variety of illegitimate attacks. Therefore, suitable security and effective protection are necessary during transmission. Method: In consideration of these challenges, we put forth a security system relying on digital watermarking with the aim of ensuring the integrity and authenticity of medical images. The proposed approach is based on Integer Wavelet Transform as an embedding algorithm; furthermore, Particles Swarm Optimization was employed to select the optimal scaling factor, which allows the system to be compatible with different medical imaging modalities. Results: The experimental results demonstrate that the method provides a high imperceptibility and robustness for both secret watermark and watermarked images. In addition, the proposed scheme performs better for medical images compared with similar watermarking algorithms. Conclusion: As it is suitable for a lossless-data application, IWT is the best choice for medical images integrity. Furthermore, using the PSO algorithm enables the algorithm to be compatible with different medical imaging modalities.

High performance image steganography integrating IWT and Hamming code within secret sharing

AbstractSteganography and secret sharing are schemes to increase the security of private information against attackers. Steganography emphasizes secrecy while secret sharing distributes the secret key in shares that are conditionally classified to reconstruct the original secret. This paper introduces a counting‐based secret sharing scheme that aims to reduce the computational complexity for longer keys, thereby providing a practical steganographic approach for efficient sharing. The scheme integrates counting‐based secret sharing with integer wavelet transform (IWT) and steganography. The subscriptions created using the Hamming code are embedded in the cover image. Using this method, IWT significantly reduces the occurrence of common rounding errors. As a result, secret key extraction becomes very accurate and eliminates the need to access the original images. This design, using secret sharing based on counting, not only has simplicity and efficiency, but also features such as flexibility, scalability, and lack of central authority. In addition, high‐quality steganography was obtained for keys with lengths of 64, 256, 512, 1024, and 3072, showing an average PSNR=80.11 in different color images. This outstanding performance makes it a highly efficient alternative to previous designs, representing a groundbreaking contribution with significant public interest in the field.

Stegano-Purge: An integer wavelet transformation based adaptive universal image sterilizer for steganography removal

Sterilization is the method of annihilating steganographic data hidden in a cover media. This paper proposes a visual quality preserving image sterilization method that effectively modifies stego-data embedded by various steganography algorithms on random greyscale images. The method applies one-level Integer Wavelet Transform (IWT) on a suspected stego image to produce HH, HL, LH and LL sub-bands. The value of a sub-band coefficient is modified according to its agitation quotient. The coefficient agitation is computed using the proposed metric Unnormalized Hellinger Distance (UHD) between a specific sub-band coefficient and its neighbours. To mitigate the issue of variable noise tolerance of IWT sub-bands, the proposed method performs parameter-controlled adaptive modification. Purification of up to 3 bits is done in the most noise-resilient HH channel and up to 2 bits in moderately noise-sensitive HL and LH channels. The highly noise-susceptible LL channel is sterilized by adaptive bit-flipping. A parameter control algorithm balances the amount of channel-wise bit modification to optimize the visual quality. The stego images for evaluation have been prepared by applying eleven state-of-the-art steganography methods including UNIWARD and HUGO on benchmark datasets of USC-SIPI and BossBase 1.01. The proposed method achieves improved performance in comparison with the state-of-the-art sterilization techniques in terms of the visual quality of sterilized image, embedding scheme-wise percentage of sterilization and the sterilization time.

Visualized Multiple Image Selection Encryption Based on Log Chaos System and Multilayer Cellular Automata Saliency Detection

For the security of multiple image regions of interest, this paper designs a multi-image visual encryption algorithm based on region of interest (ROI). Firstly, a new one-dimensional chaotic system (1-DLCP) is proposed. The performance analysis shows that the system has better dynamic behavior. Then, the salient parts of multiple images are extracted using multilayer cellular automata saliency detection. The measured matrix generated by the proposed chaotic system compresses the extracted plaintext images respectively. The compressed image and the extracted salient parts are recombined into one image, and a scrambling-diffusion synchronization operation is performed on the recombined image to obtain a secret image. The final embedding stage uses integer wavelet transform (IWT) to embed the secret image into a color carrier image. It is worth mentioning that this design scheme not only reconstructs the significant part without damage, but also reduces the storage space.

An Enhanced Compression Method for Medical Images Using SPIHT Encoder for Fog Computing

When it comes to filtering and compressing data before sending it to a cloud server, fog computing is a rummage sale. Fog computing enables an alternate method to reduce the complexity of medical image processing and steadily improve its dependability. Medical images are produced by imaging processing modalities using X-rays, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, and ultrasound (US). These medical images are large and have a huge amount of storage. This problem is being solved by making use of compression. In this area, lots of work is done. However, before adding more techniques to Fog, getting a high compression ratio (CR) in a shorter time is required, therefore consuming less network traffic. Le Gall5/3 integer wavelet transform (IWT) and a set partitioning in hierarchical trees (SPIHT) encoder were used in this study’s implementation of an image compression technique. MRI is used in the experiments. The suggested technique uses a modified CR and less compression time (CT) to compress the medical image. The proposed approach results in an average CR of 84.8895%. A 40.92% peak signal-to-noise ratio (PSNR) PNSR value is present. Using the Huffman coding, the proposed approach reduces the CT by 36.7434 s compared to the IWT. Regarding CR, the suggested technique outperforms IWTs with Huffman coding by 12%. The current approach has a 72.36% CR. The suggested work’s shortcoming is that the high CR caused a decline in the quality of the medical images. PSNR values can be raised, and more effort can be made to compress colored medical images and 3-dimensional medical images.

Image compression-hiding algorithm based on compressive sensing and integer wavelet transformation

In this paper, a three-dimensional chaotic system is proposed. Based on this chaotic system and two-dimensional compressive sensing, an asymmetric visually meaningful image compression-hiding algorithm is presented. Firstly, in the keystream generation stage, a novel parameter transformation model is constructed to pick up the feature information from the plain image as the plaintext key. Then, Rivest-Shamir-Adleman algorithm is employed to encrypt the plaintext key into the ciphertext key seen as public key. Before generating the initial values for the chaotic system, a new initial value getting model is designed to transform both the plaintext and the ciphertext keys. After solving the chaotic system, the keystream is produced which is then used in the image encryption process. Secondly, in the compression and encryption phase, a novel keystream pre-processing model is built to generate new sequences with a confusion performed on the plain image. Then, a newly constructed measurement matrix is designed to do two-dimensional compressive sensing on confusing the image to get measurements. Before obtaining the cipher image, a double diffusion operation is applied on these measurements. Thirdly, in the image hiding stage, the carrier image is performed by integer wavelet transformation to obtain coefficient matrices. Then, the cipher image is decomposed in decimal, getting the ones, tens and hundreds of pixels to form three bit matrices, of which are embedded into the three medium-high coefficient matrices of integer wavelet transformation, respectively. Finally, after performing inverse integer wavelet transformation, the carrier image containing secrets, i.e., visually meaningful encrypted image, is obtained. Experimental results also show that at a low compression ratio of 0.25, the normalized correlation coefficient between the original plain image and the recovered image is almost equal to one, while the peak signal-to-noise ratio between the carrier image containing secrets and its original carrier can reach as high as 42 dB. In addition, the proposed image compression-hiding algorithm performs good ability consideing the brute force attack and the cropping attack.

Privacy data protection scheme using memristive hyperchaos and multi-scale block compressive sensing

Recently, most meaningful image encryption schemes incorporated with various compressive sensing models have been developed to achieve dual protection of private image data and appearance. However, there exist performance constraints in these schemes in terms of anti-chosen-plaintext attack capability and key management. Aiming at the above issues, a new visually secure image encryption scheme is proposed using multi-scale block compressive sensing (MSB-CS) model and asymmetric integer wavelet transform (IWT) embedding. In this scheme, a memristor model with locally active tristable is first introduced into the oscillator to construct a new 5D memristive hyperchaotic system to generate cipher flows. Then, the non-linear MSB-CS model is designed to compress sparsely-represented plaintext coefficients. After a series of encryption operations, secret image without semantic features is asymmetrically embedded into the same-scale non-secret-involved carrier image. Additionally, both communicating parties, Alice and Bob, acquire shared secret key through the key sharing protocol based on matrix factorization problem. Finally, simulation experiments and comprehensive analysis indicate that the 5D memristive system has complicated hyperchaotic behaviours. In the meantime, the designed encryption scheme possesses better the anti-differential attack capability and reconstruction performance than other recently proposed schemes.

A Novel Fractional-Order Memristive Chaotic Circuit with Coexisting Double-Layout Four-Scroll Attractors and Its Application in Visually Meaningful Image Encryption

This paper proposes a fractional-order chaotic system using a tri-stable locally active memristor. The characteristics of the memristor, dynamic mechanism of oscillation, and behaviors of the proposed system were analyzed, and then a visually meaningful image encryption scheme was designed based on the chaotic system, DNA encoding, and integer wavelet transform (IWT). Firstly, the mathematical model of the memristor was designed, which was nonvolatile, locally active, and tri-stable. Secondly, the stability, dynamic mechanism of oscillation, bifurcation behaviors, and complexity of the fractional-order memristive chaotic system were investigated and the conditions of stability were obtained. Thirdly, the largest Lyapunov exponent, bifurcation diagram, and complexity of the novel system were calculated and the coexisting bifurcation, coexisting attractors, spectral entropy, and so on are shown. Finally, a visually meaningful image encryption scheme based on the proposed system was designed, and its security was assessed by statistical analysis and different attacks. Numerical simulation demonstrated the effectiveness of the theoretical analysis and high security of the proposed image encryption scheme.

Robust Medical Image Compression Method Based on Improved Integer Wavelet Transform for Speedy Transmission

Robust Medical Image Compression Method Based on Improved Integer Wavelet Transform for Speedy Transmission

Hybrid and Invisible Digital Image Watermarking Technique Using IWT-DCT and Hopfield Neural Network

According to the characteristic of HVS (Human Visual System) and the association memory ability of the neural network, an adaptive image watermarking algorithm based on a neural network is proposed invisible image watermarking is a secret embedding scheme for hiding secret images into a cover image file and the purpose of invisible watermarking is copyrights protection. Wavelet transformation-based image watermarking techniques provide better robustness for statistical attacks in comparison to Discrete Cosine Transform domain-based image watermarking. The joined method of IWT (Integer Wavelet Transform) and DCT (Discrete Cosine Transform) gives benefits to the two procedures. The IWT has an impediment of portion misfortune in embedding which increments the mean square estimate as SIM and results in diminishing PSNR. The capacity of drawing in is improved by pretreatment and re-treatment of image scrambling and Hopfield neural network. The proposed algorithm presents a hybrid integer wavelet transform and discrete cosine transform-based watermarking technique to obtain increased imperceptibility and robustness compared to the IWT-DCT-based watermarking technique. The proposed watermarking technique reduces the fractional loss compared to DWT-based watermarking.

Machine learning based multipurpose medical image watermarking.

Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing operations. It is essential because the changed/manipulated data may lead to erroneous judgment by medical experts and can negatively influence the human's heath. This work offers a blind and robust medical image watermarking framework using deep neural network to provide effective security solutions for medical images. During watermarking, the region of interest (ROI) data of the original image is preserved by employing the LZW (Lampel-Ziv-Welch) compression algorithm. Subsequently the robust watermark is inserted into the original image using IWT (integer wavelet transform) based embedding approach. Next, the SHA-256 algorithm-based hash keys are generated for ROI and RONI (region of non-interest) regions. The fragile watermark is then prepared by ROI recovery data and the hash keys. Further, the LSB replacement-based insertion mechanism is utilized to embed the fragile watermark into RONI embedding region of robust watermarked image. A deep neural network-based framework is used to perform robust watermark extraction for efficient results with less computational time. Simulation results verify that the scheme has significant imperceptibility, efficient robust watermark extraction, correct authentication and completely reversible nature for ROI recovery. The relative investigation with existing schemes confirms the dominance of the proposed work over already existing work.

A visually meaningful double-image encryption scheme using 2D compressive sensing and multi-rule DNA encoding

A visually meaningful double-image encryption scheme using 2D compressive sensing and multi-rule DNA encoding is presented. First, scrambling, diffusing and 2D compressive sensing are performed on the two plain images, and two privacy images are obtained, respectively. Then, the two privacy images are re-encrypted using DNA encoding theory to obtain two secret images. Finally, integer wavelet transform (IWT) is performed on the carrier image to obtain the wavelet coefficients, then the two secret images are embedded into the wavelet coefficients and 2k correction is performed, and the obtained result is processed by inverse IWT to obtain a visually meaningful encrypted image. DNA encoding rules selected for the pixel values of different positions in the two privacy images, and DNA operations performed between the two privacy images and the key streams at different positions are controlled by the chaotic system. The application of 2D compressive sensing reduces the amount of data, thus increasing the encryption capacity of the system. The introduction of DNA encoding theory and the double-image embedding process increases the security of the system. The simulation results demonstrate the feasibility of the scheme, and it has high data security and visual security.

Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques

Cybersecurity has been one of the interesting research fields that attract researchers to investigate new approaches. One of the recent research trends in this field is cancelable biometric template generation, which depends on the storage of a cipher (cancelable) template instead of the original biometric template. This trend ensures the confidential and secure storage of the biometrics of a certain individual. This paper presents a cancelable multi-biometric system based on deep fusion and wavelet transformations. The deep fusion part is based on convolution (Conv.), convolution transpose (Conv.Trans.), and additional layers. In addition, the deployed wavelet transformations are based on both integer wavelet transforms (IWT) and discrete wavelet transforms (DWT). Moreover, a random kernel generation subsystem is proposed in this work. The proposed kernel generation method is based on chaotic map modalities, including the Baker map and modified logistic map. The proposed system is implemented on four biometric images, namely fingerprint, iris, face, and palm images. Furthermore, it is validated by comparison with other works in the literature. The comparison reveals that the proposed system shows superior performance regarding the quality of encryption and confidentiality of generated cancelable templates from the original input biometrics.

A novel steganographic technique for medical image using SVM and IWT.

This study presents an efficient authentication scheme for digital image steganography on medical images benefiting from the combination of both techniques: Support Vector Machine (SVM) and Integer Wavelet Transform (IWT). We use two different strategies in this paper, where SVM is used first to separate the Region of Interest (ROI) from Non-Region of Interest (NROI) in the medical image. Then IWT is applied to embed secret information within the NROI part of the medical image (Cover Image). Moreover, we have applied a circular array and a shared secret key to enhance the robustness of the proposed scheme. The research looked into the various experimental analyses to establish the acceptability of the existing scheme. The simulation is performed to measure the imperceptibility using Peak Signal to Noise Ratio (PSNR) and to test the robustness using the Structural Similarity Index Measure (SSIM). The experimental result shows good imperceptibility with a PSNR of 64 dB and better robustness with a SSIM of 0.96 for the proposed steganographic scheme.