DWT Domain Research Articles

Robust Hashing with Deep Features and Meixner Moments for Image Copy Detection

Copy detection is a key task of image copyright protection. Most robust hashing schemes do not make satisfied performance of image copy detection yet. To address this, a robust hashing scheme with deep features and Meixner moments is proposed for image copy detection. In the proposed hashing, global deep features are extracted by applying tensor Singular Value Decomposition (t-SVD) to the three-order tensor constructed in the DWT domain of the feature maps calculated by the pre-trained VGG16. Since the feature maps in the DWT domain are slightly disturbed by digital operations, the constructed three-order tensor is stable and thus the desirable robustness is guaranteed. Moreover, since t-SVD can decompose a three-order tensor into multiple low-dimensional matrices reflecting intrinsic structure, the global deep feature calculation from the low-dimensional matrices can provide good discrimination. Local features are calculated by the block-based Meixner moments. As the Meixner moments are resistant to geometric transformation and can efficiently discriminate various images, the use of the block-based Meixner moments can make discriminative and robust local features. Hash is ultimately determined by quantifying and combining global deep features and local features. The results of extensive experiments on open image datasets demonstrate that the proposed robust hashing outperforms some state-of-the-art robust hashing schemes in terms of classification and copy detection performances.

Robust image encryption algorithm in dwt domain

Robust image encryption algorithm in dwt domain

Machine tool fault classification diagnosis based on audio parameters

With the increasing precision, complexity, and diversification of CNC machine tools, the requirements for fault detection technology of CNC machine tools are getting essential.If problems such as tool wear and spindle locking can not be found in time, it will lead to the waste of a large number of raw materials and production time. Aiming at the fault problem of high-precision NC machine tools, this paper uses the method based on DWT and statistical domain to extract features from audio signals to form data sets, and proposes a lightweight convolution neural network to identify the fault types of machine tools.Firstly, features are extracted from MIMII public data sets, and evaluates the data sets by using three machine learning models (SVM, KNN, and GNB).The results show that when facing different materials and structures, the model trained by extracting features from mechanical audio signals based on wavelet transform and statistical domain has good prediction accuracy.Then, this method is used for audio data of CNC machine tools in a real factory environment.The data set includes six common machine tool faults, including normal operation, tool wear, spindle locking, tool idling, abnormal impact, and back cutting amount.A lightweight convolution neural network was used to identify fault types in machine tools.The experimental results show that the recognition accuracy of the trained model is 97.85%, AUC is 0.9984, and F1 is 0.9769. Compared with the DNN proposed by Jong-Yi (Kuo et al., 2022) [1] , it has been improved in all aspects. The accuracy of fault recognition is improved from 96.1% to 97.85% compared with the method of sound fusion feature and OCSVM (Ding et al., 2022) [2].

A novel reversible fragile watermarking method in DWT domain for tamper localization and digital image authentication

Medical images are extremely valuable and sensitive data because of their significance in research, diagnosis, and treatment. However, the transmission of medical images over an unsecured network can lead to security issues such as modification, removal, copying, and unauthorized access. For this reason, the protection of medical information is of great importance. This paper proposes a novel reversible fragile digital watermarking method for tamper localization and authenticity and integrity protection in medical images. In the proposed method, the host image is firstly divided into 8×8 non-overlapped blocks. Then the image is transformed to the frequency domain by applying a Discrete Wavelet Transform, and the watermark information is embedded. Finally, the image is converted from the frequency domain to the spatial domain using an inverse Discrete Wavelet Transform. During this transformation, an intelligent optimization technique is used to correct the rounding errors and obtain the watermarked image with the highest level of imperceptibility. The experimental results demonstrate that the proposed method offers a watermarked image with high imperceptibility, extracts the host image lossless during the watermark extraction phase, and performs tamper localization with high precision.

Linear Discriminate Analysis based Robust Watermarking in DWT and LWT Domain with PCA based Statistical Feature Reduction

Linear Discriminate Analysis based Robust Watermarking in DWT and LWT Domain with PCA based Statistical Feature Reduction

A Fragile Image Watermarking Scheme in DWT Domain Using Chaotic Sequences and Error-Correcting Codes.

With the rapid development of digital signal processing tools, image contents can be easily manipulated or maliciously tampered with. Fragile watermarking has been largely used for content authentication purposes. This article presents a new proposal for image fragile watermarking algorithms for tamper detection and image recovery. The watermarked bits are obtained from the parity bits of an error-correcting code whose message is formed from a binary chaotic sequence (generated from a secret key known to all legitimate users) and from bits of the original image. Part of the codeword (the chaotic bits) is perfectly known to these users during the extraction phase, adding security and robustness to the watermarking method. The watermarked bits are inserted at specific sub-bands of the discrete wavelet transform of the original image and are used as authentication bits for the tamper detection process. The imperceptibility, detection, and recovery of this algorithm are tested for various common attacks over digital images. The proposed algorithm is analyzed for both grayscale and colored images. Comparison results reveal that the proposed technique performs better than some existing methods.

A blind and robust color image watermarking scheme based on DCT and DWT domains

A blind and robust color image watermarking scheme based on DCT and DWT domains

FPGA Implementation of Image Watermarking Process using Xilinx System Generator

Watermarking is a tool of confidentiality that protects valuable information hidden in the set of digital media by modifying the particular graphic contents. Special purpose hardware designed systems can be mapped in watermarking through FPGA. In this paper, the process of watermarking is performed on image using Xilinx system generator. Watermarking is performed in DWT domain using MATLAB after scaling of watermark data in Simulink environment. The co-hardware simulation has been performed for developed model using VIRTEX 6, ML605 BOARD to see the capability of watermarking system. The Simulink and system Generator through FPGA model extracts the Watermark appropriately and thereafter device utility for embedding system is determined.

Correction to: An efficient and secured blind image watermarking using ABC optimization in DWT and DCT domain

Correction to: An efficient and secured blind image watermarking using ABC optimization in DWT and DCT domain

Medical Image Compression and Encryption Using Adaptive Arithmetic Coding, Quantization Technique and RSA in DWT Domain

Nowadays, the advances in information and communication technologies open the wide door to realize the digital world’s dream. Besides, within the clear scientific scope in all fields, especially the medical field, it has become necessary to harness all the scientific capabilities to serve people, especially in medical-related services. The medical images represent the basis of clinical diagnosis and the source of telehealth and teleconsultation processes. The exchange of these images can be subject to several challenges, such as transmission bandwidth, time delivery, fraud, tampering, modifying, privacy, and more. This paper will introduce an algorithm consisting a combination of compression and encryption techniques to meet such challenges in the medical image field. First, compression is done by applying the Adaptive Arithmetic Coding (AAC) technique and controllable frequency quantization process in the Discrete Wavelet Transform. After that, the encryption process is applied using RSA and SHA-256 algorithms to encrypt the compressed file and to create the digital signature. The performance analysis has shown that the algorithm can produce high compression ratio with good image quality, whereas range of PSNR near 45 dB and SIM is 0.88 as average values. For the security analysis, we have adopted data encryption and digital signature to guarantee the main data security services including integrity, authentication, and confidentiality, making the algorithm secure against passive or active attacks.

Identification of source social network of digital images using deep neural network

• A novel method is presented for the identification of image provenance. • It identifies if the image is taken with a camera or it has been downloaded from a specific social network. • It does not rely on hand-crafted fingerprints to learn the social network specific traces. • Transfer learning approach is used to learn the traces from the images transformed to the DCT or DWT domain. • The proposed method is robust in capturing the traces from the resized images. Identification of image provenance is one of the crucial tasks to be tackled in the field of image forensics . Due to the rapid growth in technology, a huge amount of digital images are shared among users through social networks which facilitates many malicious activities . Establishing the provenance of downloaded images is an important task that could help focus investigations in a specific direction. Such a task is based on the identification of unique fingerprints that are imprinted on images by the social network during the process of upload and download. Based on this, we propose a deep learning based approach that learns the unique traces from the images transformed to the discrete cosine and wavelet domains , to investigate whether the image under test originates directly from a camera or from a specific social network site . The proposed method is able to efficiently identify the specific social network of provenance of the downloaded images and outperforms the state-of-the-art techniques. The encouraging results on images represented in the wavelet domain obtained for the well-known datasets, namely, VISION, IPLAB, and FODB sheds the light into a new way of identifying the image provenance.

Efficient image tampering localization using semi-fragile watermarking and error control codes

In this paper, we propose an image tampering localization algorithm using semi-fragile watermarking and Error-Locating codes in the DWT domain. By introducing different families of codes, we show the benefit in terms of image tampering localization and complexity of using control code error localization as an authentication function. Indeed, we first experimentally show that error localization block codes is as precise as using classical error correcting codes (Reed-Solomon and BCH codes) to locate image tampering. However, their corresponding decoding algorithms complexity is at least quadratic which make them impractical for some real time applications. To solve this problem, we introduce error-control codes called Error-Locating codes where error localization is reduced to a single syndrome computation performed with low number of binary operations (detailed later in the paper). We provide comparisons of image quality and tampering localization performances using error-detection, error-localization and error-correction approaches with different error control codes.

An efficient and secured blind image watermarking using ABC optimization in DWT and DCT domain

An efficient and secured blind image watermarking using ABC optimization in DWT and DCT domain

A robust and secure image watermarking scheme using SURF and improved Artificial Bee Colony algorithm in DWT domain

To improve the security, robustness and imperceptibility of watermark scheme, a novel watermark scheme is devised by fusing multiple techniques, including DWT-SVD, Speed-Up Robust Feature (SURF), Random Sample Consensus (RANSAC) algorithm and improved Artificial Bee Colony algorithm named as IABC. To solve the false positive problem in various SVD-based watermark schemes, DWT is employed on the host image and then block wise singular components (maximum) are used for watermark’s principal component insertion. On the one hand, IABC algorithm is applied to optimize the scaling factors to balance invisibility and robustness and on the other hand to improve the convergence speed. Moreover, SURF and RANSAC algorithm are used to correct probable geometrical distortion to maintain watermark synchronization. Experimental outcomes demonstrated that compared with the related existing schemes, this scheme exhibits an overall superior performance in the aspects of security, imperceptibility and robustness.

Seismic ground roll attenuation in DWT domain by split Bregman iteration algorithm

Noise reduction of seismic data is one of the most substantial steps in seismic signal processing. Goudarzi and Riahi presented wavelet domain ground roll analysis (WDGA), a technique based on the data. The results of this method showed a good improvement of the signal-to-noise ratio, but the WDGA did not work well in the data with a high level of random noise because this method uses a comparison-based search algorithm. This problem requires additional research to find a way to overcome this limitation. The wavelet domain split Bregman (WDSB) method presented in this paper is based on the use of the split Bregman algorithm in a discrete wavelet domain. The goal is to simultaneously use the capabilities of the F-K filter and the split Bregman iteration algorithm in the discrete wavelet domain, to overcome the problem of high levels of random noise in ground roll attenuation. The results of using WDSB for synthetic and real seismic data show improvement in ground roll attenuation while protecting seismic signals. The new algorithm is studied in time and wavelet domains.

Use of SHDM in commutative watermarking encryption

SHDM stands for Sphere-Hardening Dither Modulation and is a watermarking algorithm based on quantizing the norm of a vector extracted from the cover work. We show how SHDM can be integrated into a fully commutative watermarking-encryption scheme and investigate implementations in the spatial, DCT, and DWT domain with respect to their fidelity, robustness, capacity, and security of encryption. The watermarking scheme, when applied in the DCT or DWT domain, proves to be very robust against JPEG/JPEG2000 compression. On the other hand, the spatial domain-based approach offers a large capacity. The increased robustness of the watermarking schemes, however, comes at the cost of rather weak encryption primitives, making the proposed CWE scheme suited for low to medium security applications with high robustness requirements.

Video watermarking using neural networks

Copyright protection for videos is important to prevent revenue loss for video generation companies by using video watermarking methods. Though many methods exists, still certain scope is noticed in robust video watermarking methods. Achieving features like trade-off between robustness and imperceptibility, speed, blind watermarking simultaneously is very challenging. The proposed work achieves the above said features using log-polar, DWT, and SVD techniques to embed watermark in a video and extract it when necessary. The objective is to protect the copyright and make the watermarking system blind, robust against frame drop attacks as well as achieving above features. This work also leverages scrambling, deep learning-based approach to generate secret sharing image from watermark to improve the speed compared to conventional tabular-based approach. We evaluated the method on our own dataset and proved that this method is outperforming compared to state-of-the-art methods in DWT and SVD domain.

Study of robustness of information embedding into digital images DWT domain using QIM method against destructive effects and steganalysis

Steganography is one of effective solutions to ensure information confidentiality. For this purpose, secret information is embedded into a cover object, for example, a digital image. However, embedded data can be detected using steganalysis techniques. As a result, an attacker can apply destructive effects to the stego-image and destroy the embedded data to prevent their hidden transmission. Therefore, this paper presents a study of robustness of information embedding into digital images DWT domain using QIM method against destructive effects (JPEG compression, brightness change, etc.) and statistical steganalysis at the same time. The results will help to increase the efficiency of information embedding into DWT domain of digital images.

An intelligent and blind dual color image watermarking for authentication and copyright protection

In this paper, a blind dual watermarking scheme for color images is proposed by embedding an invisible robust watermark to protect copyright, as well as a fragile watermark is embedded for image authentication. For the purpose of copyright protection, the robust watermark is embedded into the blue channel of RGB color space based on DWT, HVS and SVD domains using a specialized PSO optimization to balance the trade-off between robustness and imperceptibility. In addition, the robust watermarking capacity in SVD is doubled by inserting two robust watermark bits into each selected blocks and the robust watermark can be extracted blindly. For the purpose of authentication, a fragile watermark is embedded into all channels of RGB color space using a new way to manipulate the diagonal singular values. The authenticity of a suspected image can be verified in the absence of original watermark and host images. The combination of robust and fragile watermarking in the proposed scheme provides a suitable mechanism to protect valuable and original color images. According to the experimental and comparative results, the proposed scheme provides superior outcomes with high robustness, imperceptibility, and capacity along with a good accuracy rate in locating the tampered area of an image.

Low dimensional DCT and DWT feature based model for detection of image splicing and copy-move forgery

Digital images are being used as a prominent carrier of visual information in this age of digitization. Images become more and more omnipresent in everyday life. The images can be easily manipulated due to the accessibility of many internet tools and advanced software. Previously many techniques have been developed to authenticate the images. But all the previous techniques have high dimension of feature vectors. Here, a low dimensional DCT and DWT based features have been introduced to authenticate the images. In this work, we are dealing with both the passive forgery (splicing and copy-move) simultaneously. Features are extracted through image statistics and pixel correlation from DCT and DWT domain. Ensemble classifier has been selected for training and testing. The classifier classifies whether the given images are forged or authentic. Further, it also classifies the forgery in spliced or copy-move. If there is copy-move, the proposed work also perform the region detection using a novel key-point based method. The proposed model gives good detection accuracy and high generalization capability which is independent of image formats. Experimental results demonstrate the performance of proposed work against different post-processing operations like scaling, rotation, and Gaussian noise. Also, the comparative results against different existing methods show the effectiveness of the proposed model.