Efficient SVD-based audio watermarking technique in FRT domain

Abdelwahab, Khaled M.; El-atty, Saied M. Abd; El‐Shafai, Walid; El-Rabaie, El-Sayed M.; El-Samie, F. E. Abd

doi:10.1007/s11042-019-08023-z

Cited by 44 publications

(14 citation statements)

References 32 publications

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“…Different approaches have been adopted for forgery detection, such as passive and active approaches [8][9][10]. Active forgery detection techniques work on the principles of embedding external information in the image, such as a watermark [11][12][13] or a digital signature [14][15][16]. Verification is performed through the detection of the watermark or signature.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Forgery Detection Approaches for Digital Color Images

Baumy¹,

Algarni²,

Abdalla³

et al. 2022

Computers, Materials &Amp; Continua

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This paper is concerned with a vital topic in image processing: color image forgery detection. The development of computing capabilities has led to a breakthrough in hacking and forgery attacks on signal, image, and data communicated over networks. Hence, there is an urgent need for developing efficient image forgery detection algorithms. Two main types of forgery are considered in this paper: splicing and copy-move. Splicing is performed by inserting a part of an image into another image. On the other hand, copy-move forgery is performed by copying a part of the image into another position in the same image. The proposed approach for splicing detection is based on the assumption that illumination between the original and tampered images is different. To detect the difference between the original and tampered images, the homomorphic transform separates the illumination component from the reflectance component. The illumination histogram derivative is used for detecting the difference in illumination, and hence forgery detection is accomplished. Prior to performing the forgery detection process, some pre-processing techniques, including histogram equalization, histogram matching, high-pass filtering, homomorphic enhancement, and single image super-resolution, are introduced to reinforce the details and changes between the original and embedded sections. The proposed approach for copy-move forgery detection is performed with the Speeded Up Robust Features (SURF) algorithm, which extracts feature points and feature vectors. Searching for the copied partition is accomplished through matching with Euclidian distance and hierarchical clustering. In addition, some preprocessing methods are used with the SURF algorithm, such as histogram equalization and single-mage super-resolution. Simulation results proved the feasibility and the robustness of the pre-processing step in homomorphic detection and SURF detection algorithms for splicing and copy-move forgery detection, respectively.

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Section: Related Workmentioning

confidence: 99%

“…The algorithm in [12] decomposes the image into RGB components and applies 2-DWT for each component in the original image and mark image. The watermarked component is produced from the low-frequency components for the original image and mark image.…”

Section: Related Workmentioning

confidence: 99%

Efficient Forgery Detection Approaches for Digital Color Images

Baumy¹,

Algarni²,

Abdalla³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…An audio watermarking technique based on SVD, and fractional Fourier transform (FrFT) is suggested by. 12 This scheme inserted an image watermark into the audio signal or the modified version of the wave in the FrFT domain. This method provides a higher level of protection and attack resistance.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel watermarking scheme based on discrete wavelet transform‐singular value decomposition

Yasmeen

Uddin

2022

Security and Privacy

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Due to the rapid advancement in information technology, individuals as well as organizations frequently transfer or share their digital content via cyberspace. The security and integrity of this digital content become a big concern, as any middleman can misuse or temper this content. Digital watermarking is a blessing to protect it from any unwanted tampering. Even though many researchers have implemented various watermarking approaches, but still ownership protection and reliability concerns are not fully mitigated. This paper proposes an improved novel hybrid watermarking scheme by combining discrete wavelet transform (DWT) with singular value decomposition (SVD) to minimize these concerns. At first, the host image is decomposed by multi‐level DWT and a two‐level SVD is applied to the fourth lower‐level coefficient (LL5) of the host image. Then the watermark is inserted into the specified sub‐band of the host image. The watermark is extracted in a reverse manner. The hybridization of DWT and SVD ensures that the watermarking technique performs better in terms of visual distortion and robustness. The outcomes of the experiment show that our proposed method is more robust, imperceptible, and secure than the existing DWT‐SVD based methods, even with a broad watermark scale.

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“…The invention of MRI played an important role in extracting, analyzing, and categorizing brain tumors [11]. The MRI imaging is widely used for brain tumor classification [12]. The obtained MR images have high quality for future processing.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Brain MRI: AI-Assisted Healthcare Framework for the Smart Cities

El‐Shafai¹,

Ali²,

Sedik³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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The use of intelligent machines to work and react like humans is vital in emerging smart cities. Computer-aided analysis of complex and huge MRI (Magnetic Resonance Imaging) scans is very important in healthcare applications. Among AI (Artificial Intelligence) driven healthcare applications, tumor detection is one of the contemporary research fields that have become attractive to researchers. There are several modalities of imaging performed on the brain for the purpose of tumor detection. This paper offers a deep learning approach for detecting brain tumors from MR (Magnetic Resonance) images based on changes in the division of the training and testing data and the structure of the CNN (Convolutional Neural Network) layers. The proposed approach is carried out on a brain tumor dataset from the National Centre of Image-Guided Therapy, including about 4700 MRI images of ten brain tumor cases with both normal and abnormal states. The dataset is divided into test, and train subsets with a ratio of the training set to the validation set of 70:30. The main contribution of this paper is introducing an optimum deep learning structure of CNN layers. The simulation results are obtained for 50 epochs in the training phase. The simulation results reveal that the optimum CNN architecture consists of four layers.

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Efficient SVD-based audio watermarking technique in FRT domain

Cited by 44 publications

References 32 publications

Efficient Forgery Detection Approaches for Digital Color Images

Efficient Forgery Detection Approaches for Digital Color Images

A novel watermarking scheme based on discrete wavelet transform‐singular value decomposition

Analysis of Brain MRI: AI-Assisted Healthcare Framework for the Smart Cities

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