Discrete Wavelet Transform Domain Research Articles

A Watermark-Based Scheme for Authenticating JPEG 2000 Image Integrity That Complies with JPEG Privacy and Security

Network development has made it easier to access multimedia material and to change it by allowing the collection, modification, and transmission of digital data. Additionally, this has led to a rise in malicious use, including unauthorized data distribution and copying. As the quantity of evil activities increases, security issues such as unauthorized use and image forgery are rising. While security solutions for JPEG-1 images are widely available, there remains a significant gap in protection for JPEG 2000 images. In this paper, we propose a new watermark-based forgery detection method to comply with the JPEG Privacy and Security standards and to authenticate JPEG 2000 image integrity in the discrete wavelet transform (DWT) domain. The method proposed divides JPEG 2000 images into groups of blocks (GOBs) within the DWT domain. The watermark is generated by collaborating with the neighboring GOBs and is embedded in the GOBs. It enables you to respond to the collage attack. Experimental results using various sample images show the superiority of the proposed method, showing negligible visual differences between the original and watermarked JPEG 2000 images.

Coherent Feature Extraction with Swarm Intelligence Based Hybrid Adaboost Weighted ELM Classification for Snoring Sound Classification.

For patients suffering from obstructive sleep apnea and sleep-related breathing disorders, snoring is quite common, and it greatly interferes with the quality of life for them and for the people surrounding them. For diagnosing obstructive sleep apnea, snoring is used as a screening parameter, so the exact detection and classification of snoring sounds are quite important. Therefore, automated and very high precision snoring analysis and classification algorithms are required. In this work, initially the features are extracted from six different domains, such as time domain, frequency domain, Discrete Wavelet Transform (DWT) domain, sparse domain, eigen value domain, and cepstral domain. The extracted features are then selected using three efficient feature selection techniques, such as Golden Eagle Optimization (GEO), Salp Swarm Algorithm (SSA), and Refined SSA. The selected features are finally classified with the help of eight traditional machine learning classifiers and two proposed classifiers, such as the Firefly Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (FA-WELM-Adaboost) and the Capuchin Search Algorithm-Weighted Extreme Learning Machine hybrid with Adaboost model (CSA-WELM-Adaboost). The analysis is performed on the MPSSC Interspeech dataset, and the best results are obtained when the DWT features with the refined SSA feature selection technique and FA-WELM-Adaboost hybrid classifier are utilized, reporting an Unweighted Average Recall (UAR) of 74.23%. The second-best results are obtained when DWT features are selected with the GEO feature selection technique and a CSA-WELM-Adaboost hybrid classifier is utilized, reporting an UAR of 73.86%.

High invisibility image steganography with wavelet transform and generative adversarial network

Recently, extensive research is showing that deep learning has enormous potential in image steganography. However, most steganography methods based on deep learning are not sufficiently invisible. In this paper, a novel end-to-end deep neural network for image steganography based on generative adversarial network (GAN) and discrete wavelet transformation (DWT) is proposed, which can greatly improve the invisibility of the method. Firstly, the cover image is transformed from the RGB domain to the DWT domain to embed information in frequency. Furthermore, a multi-scale attention convolution to fuse different scales of feature information is proposed, which can help the model to find a better location to embed the information. Finally, the fusion module is used to embed the information into the cover image at different modification intensities. In addition, a new Discriminator that compares cover images and stego images in a patch-to-patch way to improve the ability of the Discriminator. We analyze our algorithm from multiple aspects (e.g., security and invisibility) to verify the superior performance of the proposed method. To demonstrate generalization ability, experiments are conducted on three widely used datasets and show the superior performance of our method compared to other steganography methods based on deep learning.

A visually secure image encryption method based on semi-tensor product compressed sensing and IWT-HD-SVD embedding

The conventional approach for images encryption entails transforming a regular image into an encrypted image that resembles noise. However, this noise-like encrypted image is susceptible to drawing the attention of an attacker when transmitted through a public channel. Hence, there has been a recent surge in the interest of academics towards visually secure image encryption techniques. In a broad sense, encryption methods that include visual significance should prioritize four key elements: the resemblance between the cypher picture and the carrier image, the capacity for embedding, the attainment of good recovery quality, and resilience against many forms of attacks. To address the issues pertaining to inadequate visual security, limited resistance against attacks, and subpar quality of reconstructed images observed in contemporary image encryption and compression methodologies. This paper proposes a visually secure image encryption method based on improved semi-tensor product compressed sensing, two-way cross zigzag obfuscation, and IWT-HD-SVD embedding. Firstly, the plain image is sparsely represented in the Discrete Wavelet Transform (DWT) domain, and a two-way cross zigzag mismatch strategy is proposed to disarrange the coefficient vectors. Then the plain image is encrypted as a secret image by the improved semi-tensor product compression sensing technique. After that, IWT-HD-SVD embedding technique is proposed to embed the secret image into the carrier image to generate the final meaningful cryptographic image. This dramatically improves the visual security of the cryptographic image. Simulation results show that the quality of the decrypted image is approximately 36 dB and up to 44 dB. In addition, the cryptographic image is highly robust against common noise attacks of 0.05 %.

Novel multiple color images encryption and decryption scheme based on a bit-level extension algorithm

Digital images play a crucial role in modern communication, but the accompanying privacy concerns have become increasingly prominent. In this context, image encryption technology has emerged as a vital means to safeguard the security of digital image information. Over the past few years, a number of effective image encryption schemes have been developed. However, the amalgamation of bit-level encryption techniques with compression algorithms remains underexplored. To leverage the advantages of these two technologies in terms of security and practicality, a novel multiple color images encryption and decryption scheme is devised based on a bit-level extension algorithm. Firstly, four color images are compressed by the discrete wavelet transform (DWT) to reduce transmission and storage consumption. Secondly, to carry out the bit-level scrambling and diffusion operations on pixels with fractional parts in the DWT domain and maintain the decryption image quality as much as possible, a bit-level extension algorithm is developed. Finally, three rounds of scrambling and diffusion operations based on the bit-level extension algorithm are conducted to enhance the randomness of the ciphertext image and conceal the plaintext information. To reinforce the resistance of the presented image encryption and decryption scheme towards the chosen-plaintext and the differential attacks, secret keys are produced with the hash function SHA-512. The presented scheme undergoes a comprehensive analysis including decryption quality, key security, and resistance to the statistical and the differential attacks. The results are: average PSNR = 31.2631 dB, key space reaches 2472, correlation coefficients are almost 0, average entropy = 7.9985 bits, average NPCR = 99.6089%, and average UACI = 33.4658%. Moreover, experimental simulations demonstrate that our solution exhibits a degree of resilience to noise interference and data loss. In summary, our presented multiple color images encryption and decryption scheme based on a bit-level extension algorithm is effective and reliable.

Robust medical image watermarking in frequency domain

Protecting patient information in medical image watermarking poses a significant challenge, especially when traditional methods like the Arnold transform prove inadequate in ensuring security. This paper introduces a novel approach within the Discrete Wavelet Transform (DWT) domain to address this issue effectively. By employing the Advanced Encryption Standard (AES), the security and robustness of the system are greatly enhanced through the encryption of both the medical image and patient data. The encrypted medical image undergoes a 2-level DWT process, allowing the concealment of encrypted patient information while maintaining its invisibility. This proposed scheme surpasses others in experimental evaluations, as evidenced by metrics such as PSNR and NC, solidifying its position as a more secure choice for medical image watermarking. The results validate the scheme’s robustness and imperceptibility.

Efficient Discrimination of Some Moss Species by Fourier Transform Infrared Spectroscopy and Chemometrics

Fourier-transform infrared (FT-IR) spectroscopy with the attenuated total reflectance (ATR) technique was used to classify 14 samples (11 species) from three moss families (Mielichhoferiaceae, Bryaceae, and Mniaceae). The FT-IR spectra ranging from 4000 cm−1 to 650 cm−1 of the 14 samples were obtained. To group the spectra according to their spectral similarity in a dendrogram, cluster analysis and principal component analysis (PCA) were performed. Cluster analysis combined with PCA was used to give a rough result of classification among the moss samples. However, some species belonging to the same genus exhibited very similar chemical components and similar FT-IR spectra. Discrete wavelet transform (DWT) was used to enhance the differences among species with similar chemical components and FT-IR spectra. Three scales were selected as the feature-extracting space in the DWT domain. Results showed that FT-IR spectroscopy combined with chemometrics was suitable for a systematic research classification of bryophytes.

Coronavirus disease identification using Multi-subband feature analysis in DWT domain

Coronavirus disease early identification and differentiating it with other lung infections is a complex and time-consuming task. At present RT-PCR and Antigen tests are used for diagnosis, but the whole process is tedious, time exhausting and sometimes gives inaccurate results. Radiological scans like CT scan and X-rays are often considered for confirmation of infection, as it contains vital information about region of infection, disease state and severity, texture, size and opacity of infection. Automated machine learning techniques along with CXR (Chest X-ray) images can serve as alternative approach for Covid-19 diagnosis and differentiating it with other health conditions. In this work, Covid-19 disease identification is performed based on multi-subband feature extraction using 2D Discrete Wavelet Transform (DWT) on CX-Ray images. The CX-ray images are decomposed into multi-subbands of frequencies using DWT. The quarter-sized decomposed low and high frequency components are concatenated into single feature vector. In order to find suitable wavelet filter for extracting features from CX-ray images, a rigorous experimentation is carried out among various wavelet families such as Haar, Daubechies, Symlets, Biorthogonal and their respective members that have different vanishing moment and regularity properties. The feature vector is then used for training machine learning model based on support vector machine classifier. Experimental result shows that the classification model based on Haar wavelet feature extraction performs better as compared to other wavelet families with classification accuracy of 100%.

Cross-scale feature fusion-based JND estimation for robust image watermarking in quaternion DWT domain

Owing to the rapid development of just-noticeable difference (JND) and the high robustness of discrete wavelet transform (DWT) based watermarking, robust watermarking methods based on JND models have been extensively adopted in the area of image copyright protection. In general, in existing JND model-based methods, transform domain-based features play an important role during the quantization strength measurement. However, most existing methods only use the subband-specific features from the final multi-scale wavelet transform domain, ignoring the feature relevance among subband-specific features with different scales among RGB channels. To address this issue, in this study, we put forward a JND estimation based on cross-scale feature fusion (CFJnd) mechanism in quaternionic DWT domain for robust image watermarking. For the proposed CFJnd, we first design different quaternionic wavelet scales for the JND modeling, which can be used to measure the minimum distortion level of the embedding-specific subband relevance. Furthermore, the cross-scale features among different subbands are adopted to jointly generate the pattern, texture and color measurement, making the final perceptual masking more representative. In addition, we further develop a CFJnd-based robust image watermarking, and the watermark is embedded by quantifying the candidate quaternionic DWT coefficients of the embedding-specific subband on a level which is adaptively defined according to the proposed CFJnd. Experimental results based on the publicly available image database show that the proposed method can produce reliable and promising results, compared to other state-of-the-art image watermarking methods.

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points.

Image Fusion by Using Bi-Orthogonal Wavelets in Discrete Wavelet Transform (DWT) Domain

Image fusion can be widely explained as merging more than one photograph or a few in their factors right into a single photo without the creation of distortion or lack of information. Image fusion pastimes mix complementary as properly as approximate statistics for greater than one photograph to create fused photo output. Hence, the output picture achieved must have to include an extra accurate representation of the position than any of the man or woman supply pix and is greater fantastic for human visible, and pc understanding or in addition picture processing and comparison duties. . The notably used fusion rule is the most willpower scheme. This handy scheme chooses the biggest absolute wavelet coefficient at each location from the given snapshots because of the coefficient on the area within the fused photo. After that, the fused photo is obtained with the aid of using the inverse DWT for the corresponding wavelet coefficient. The well-known significantly used pixel-primarily based fusion rule is the aforementioned maximum favored scheme. This approach can choose the salient elements from the furnished photographs; however, it's miles touchy with noise and artifacts as they have been supposed to have better contrast. When the 2 photographs are degraded with the aid of using the wavelet transform, the approximation picture (low–frequency band) and thing image (high-frequency band) may additionally have one-of-a-type bodily meaning. In this project, a new fusion rule is proposed to feature a wavelet coefficient that treats low-frequency and high-frequency bands with high-quality fusion schemes.

A Robust Color Image Watermarking Scheme Based on Discrete Wavelet Transform Domain and Discrete Slantlet Transform Technique

Watermarking is primarily used to prevent unauthorized copying of digital data by inserting a watermark as a symbol of ownership in the digital data. The purpose of this work is to increase the imperceptibility and robustness of watermarked image by utilizing the Discrete Wavelet Transform (DWT) domain and Discrete Slantlet Transform (DST) technique. In compared to existing methodologies, the strategies used in this work considerably increased the robustness, imperceptibility, and protection of the watermarked image against JPEG compression and other types of noise attacks. The proposed method's robustness was determined by comparing the image's Normalized Cross-Correlation (NCC) value before and after the watermarking operation. The proposed method increased the NCC value to greater than 0.7 and the Peak Signal-to-Noise Ratio (PSNR) value to greater than 59 decibels (dB). Additionally, the proposed approach does not require knowledge of the original image in order to extract the watermarks.

Optimal Classification of Atrial Fibrillation and Congestive Heart Failure Using Machine Learning.

Cardiovascular disorders, including atrial fibrillation (AF) and congestive heart failure (CHF), are the significant causes of mortality worldwide. The diagnosis of cardiovascular disorders is heavily reliant on ECG signals. Therefore, extracting significant features from ECG signals is the most challenging aspect of representing each condition of ECG signal. Earlier studies have claimed that the Hjorth descriptor is assigned as a simple feature extraction algorithm capable of class separation among AF, CHF, and normal sinus rhythm (NSR) conditions. However, due to noise interference, certain features do not represent the characteristics of the ECG signals. This study addressed this critical gap by applying the discrete wavelet transform (DWT) to decompose the ECG signals into sub-bands and extracting Hjorth descriptor features and entropy-based features in the DWT domain. Therefore, the calculation of Hjorth descriptor and entropy-based features performed on each sub-band will produce more detailed information of ECG signals. The optimization of various classifier algorithms, including k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), artificial neural network (ANN), and radial basis function network (RBFN), was investigated to provide the best system performance. This study obtained an accuracy of 100% for the k-NN, SVM, RF, and ANN classifiers, respectively, and 97% for the RBFN classifier. The results demonstrated that the optimization of the classifier algorithm could improve the classification accuracy of AF, CHF, and NSR conditions, compared to earlier studies.

Recognizing seizure using Poincaré plot of EEG signals and graphical features in DWT domain.

Electroencephalography (EEG) signals are considered one of the oldest techniques for detecting disorders in medical signal processing. However, brain complexity and the non-stationary nature of EEG signals represent a challenge when applying this technique. The current paper proposes new geometrical features for classification of seizure (S) and seizure-free (SF) EEG signals with respect to the Poincaré pattern of discrete wavelet transform (DWT) coefficients. DWT decomposes EEG signal to four levels, and thus Poincaré plot is shown for coefficients. Due to patterns of the Poincaré plot, novel geometrical features are computed from EEG signals. The computed features are involved in standard descriptors of 2‑D projection (STD), summation of triangle area using consecutive points (STA), as well as summation of shortest distance from each point relative to the 45-degree line (SSHD), and summation of distance from each point relative to the coordinate center (SDTC). The proposed procedure leads to discriminate features between S and SF EEG signals. Thereafter, a binary particle swarm optimization (BPSO) is developed as an appropriate technique for feature selection. Finally, k-nearest neighbor (KNN) and support vector machine (SVM) classifiers are used for classifying features in S and SF groups. By developing the proposed method, we have archived classification accuracy of 99.3 % with respect to the proposed geometrical features. Accordingly, S and SF EEG signals have been classified. Also, Poincaré plot of SF EEG signals has more regular geometrical shapes as compared to S group. As a final remark, we notice that the Poincaré plot of coefficients in S EEG signals has occupied more space as compared to SF EEG signals (Tab. 3, Fig. 11, Ref. 57). Text in PDF www.elis.sk Keywords: EEG signal, DWT, Poincaré plot, geometrical feature, BPSO, SVM, KNN.

Real-time FPGA implementation of a secure chaos-based digital crypto-watermarking system in the DWT domain using co-design approach

In this paper a new approach for designing invisible non-blind full crypto-watermarking system targeting images security on FPGA platform is presented. This new design is based on the Hardware-Software co-design approach using the High-Level Synthesis (HLS) tool of Xilinx which allows a good compromise between development time and performances. For a better authentication and robustness of the proposed system, the Discrete Wavelet Transform (DWT) is employed. To more enhance the security level, a new chaos-based generator proposed is integrated into a stream cipher algorithm in order to encrypt and decrypt the watermark during the insertion and extraction phases.This approach allows a better secure access at the positions of the watermark and to distribute the watermark evenly throughout the image. Three novel customized Intellectual Property (IP) cores designed under HLS tool, implementing Haar DWT and the new chaos-based key generator, have been generated, tested, and validated. The generated Register Transfer Level-IP (RTL-IP) cores are integrated into a Vivado library that achieves real-time secured watermarking operations for both embedding and extraction processes. The system has been evaluated using the main metrics in terms of imperceptibility of the produced watermarked images achieving a Peak Signal to Noise Ratio (PSNR) of 47 dB, robustness against most geometric and image processing attacks achieving a Normalized Cross-Correlation (NCC) of 0.99. The proposed crypt-watermarking system allows a good solution against brute force attack which produce a huge key-space of $$2^{768}$$ . Finally, the implementation offers a good efficiency value of 0.19 MHz/LUT in terms of FPGA resource consumption and speed, making the system a reliable choice for real sensitive embedded applications.

Efficient HEVC steganography approach based on audio compression and encryption in QFFT domain for secure multimedia communication

High-Efficiency Video Coding (HEVC) is the most recent video codec standard. It is substantial to analyze the HEVC steganography process due to its practical and academic significance. Thus, a secure HEVC steganography approach is introduced in this paper to study the possibility of hiding an encrypted secret audio message within a cover compressed video frame in a secure and complicated manner. In the preliminary stage, the secret audio message is compressed utilizing the Discrete Cosine Transform (DCT) to achieve a high capacity performance for the HEVC steganography process. After that, the suggested approach implies two-cascaded encryption layers to encrypt the compressed secret message before embedding it within a cover HEVC frame. In the first encryption layer, a novel encryption technique based on random projection and Legendre sequence in the Discrete Wavelet Transform (DWT) domain is introduced to cipher the compressed secret audio message. In the second encryption layer, the yielded encrypted audio message is represented in a form of quaternion numbers using the Quaternion Fast Fourier Transform (QFFT) technique. Each cover HEVC frame is also represented in a quaternion form. In the suggested approach, some straightforward quaternion mathematical operations are employed on the encrypted secret message and the cover HEVC frames to represent them in a quaternion form in the frequency domain, then the encrypted secret audio message is hidden within the cover HEVC frame. At the receiver, the secret message can be retrieved and extracted from the cover HEVC frame utilizing the same methodology of the employed quaternion mathematical operations. The major contributions of the suggested HEVC steganography scheme are: (1) it allows hiding of massive amount of secret information within cover video frames, and (2) it has higher robustness against multimedia attacks and steganalysis contrasted to the conventional and literature schemes. Furthermore, the proposed approach is evaluated utilizing different assessment metrics like Feature Similarity Index Measure (FSIM), Peak Signal-to-Noise Ratio (PSNR), correlation coefficient, and Structural Similarity Index Measure (SSIM) to evaluate the efficiency of the stego HEVC frames compared to the original ones. The achieved outcomes demonstrate that the suggested steganography scheme is straightforward to implement, more secure, and robust in the presence of steganalysis multimedia attacks compared to the literature approaches.

Digital Image Steganography Scheme for Speech Signals in the Discrete Wavelet Transform Domain

In recent years, the evolution and expansion of the multimedia world has allowed digital images to be widely used as a carrier medium for facilitating the sending and receiving of confidential digital content such as text, audio and images. However, there are few steganographic schemes that hide digital speech signals into digital images; also, the state-of-the-art methods have some disadvantages such as low embedding capacity, low quality in the modified image, and/or low audible quality in the recovered speech signal. In this paper, a new steganographic scheme that hides a digital speech signal into a color image is presented, where the embedding process is performed in the HL and LH channels of the first decomposition level of the discrete wavelet transform (DWT) over the YCbCr color space; additionally, a chaotic map method is implemented to encrypt the speech signal and to spread it throughout the whole image. Experimental results have demonstrated that the proposed scheme results in better performance against other state-of-the-art methods, due to their ability to embed and recover speech signals with duration up to 16.384 seconds using color images of 512×512 pixels, obtaining 32 dB and 0.92 in PSNR and SSIM, respectively for the stego-image, and 41 dB in SNR for the recovered speech signal.

A passive approach for the detection of splicing forgery in digital images

With the technology progress, a plethora of freely accessible software has questioned the authenticity of digital images. This field is continuously creating challenges for researchers to ascertain the integrity of images. Hence, there is a need to improve the performance of forgery detection algorithms from time to time. This paper is focused on the detection of splicing forgery because it is one of the most frequently used image manipulation techniques. In the proposed scheme, Markov features in both Discrete Wavelet Transform (DWT) and Local Binary Pattern (LBP) domains are extracted and combined for the detection of image splicing. Three-level DWT is applied to the source image by the means of discrete Haar wavelet. The image is split in to high and low-frequency sub-bands after applying one level DWT. Furthermore, low-frequency sub-band is decomposed twice to obtain three-level DWT, which leads to more information and less amount of noise. The efficacy of the proposed scheme has been appraised on six benchmark datasets i.e. CASIA v2.0, DVMM, IFS-TC, CASIA v1.0, Columbia, and DSO-1. Moreover, the SVM classifier is trained to classify the images as tampered or authentic. The effectiveness of the proposed scheme is evaluated based on various performance parameters such as accuracy, sensitivity, specificity, and informedness. The proposed results show improved accuracy i.e. 99.69%, 99.76%, 97.80%, 98.61%, 96.90%, and 92.50% on CASIA v1.0, CASIA v2.0, DVMM, Columbia, IFS-TC, and DSO-1, respectively, in comparison to other existing approaches.

Cancelable Fingerprint Recognition based on Encrypted Convolution Kernel in Different Domains

Peoples’ biometrics, such as fingerprints, are unique , as a result it can be used in many evidence security requests, such as employees’ registration gate, crime investigation, and revealing smart phones. The security of fingerprints is very critical in protecting the peoples’ identity. In this research, a cancelable fingerprint recognition system is proposed. The proposed system is based on comprising four biometrics in a unified biometric template for each person using Discrete Cosine Transform (DCT) compression. This unified biometric template is encrypted with different convolution kernels produced by chaotic Baker map in different domains. The Integer Wavelet Transform (IWT) and the Discrete Wavelet Transform (DWT) are used to create different transformations of the fingerprint. In case a transformed fingerprint is compromised, the biometric fingerprint transformation is reorganized with another transformation. A comparative study between different transform-domains in the occurrence of attacks shows the authority of encryption in the DWT domain with different keys. Both Equal Error Rate (EER) and Area under Receiver Operating Characteristic (AROC) curve are used for performance evaluation revealing high performance of the proposed system.

A reliable and secure image watermarking algorithm using homomorphic transform in DWT domain

Image watermarking is one of the reliable methods which easily retains integrity and confidentiality of images against illegitimate users thereby ensuring copyright protection. In this paper a new image watermarking technique is proposed which employs fusion of discrete wavelet transform (DWT), homomorphic transform (HT), singular value decomposition along with Arnold transform (AT). In this work first level DWT is performed on host image which results in various subbands (LL, HL, LH and HH). HL subband is chosen which is further transformed by HT to decompose it in illumination and reflectance components. The watermark is scrambled with AT in order to strengthen security towards malicious attacks. Scrambled watermark is now embedded into singular values of the reflectance component. As reflectance component contains prominent image features, hence embedding in this part provides better immunity against several attacks and imperceptibility is also attained as this component varies very rapidly. Thus, major aim for proposed watermarking technique is achieved. The proposed scheme is implemented with numerous wavelet families such as Haar, Coiflet, symlet and biorthogonal. The main idea for implementing with different wavelets is to judge which wavelet demonstrates good robustness against various attacks. Proposed scheme performance is also evaluated by examining comprehensively under several attacks such as rotation, sharpening. Results clearly prove high robustness for the proposed scheme. Superiority of proposed scheme is clearly indicated by comparing it with other commonly—used schemes as reported by other researchers. Benchmark attacks like “Checkmark” attacks are also verified for the proposed scheme.