Inverse Lifting Wavelet Transform Research Articles

LWT-DCT based Image Watermarking Scheme using Normalized SVD

Background: Nowadays information security is one of the biggest issues of social networks. The multimedia data can be tampered with, and the attackers can then claim its ownership. Image watermarking is a technique that is used for copyright protection and authentication of multimedia. Objective: We aim to create a new and more robust image watermarking technique to prevent illegal copying, editing and distribution of media. Method : The watermarking technique proposed in this paper is non-blind and employs Lifting Wavelet Transform on the cover image to decompose the image into four coefficient matrices. Then Discrete Cosine Transform is applied which separates a selected coefficient matrix into different frequencies and later Singular Value Decomposition is applied. Singular Value Decomposition is also applied to the watermarking image and it is added to the singular matrix of the cover image which is then normalized followed by the inverse Singular Value Decomposition, inverse Discrete Cosine Transform and inverse Lifting Wavelet Transform respectively to obtain an embedded image. Normalization is proposed as an alternative to the traditional scaling factor. Results: Our technique is tested against attacks like rotation, resizing, cropping, noise addition and filtering. The performance comparison is evaluated based on Peak Signal to Noise Ratio, Structural Similarity Index Measure, and Normalized Cross-Correlation. Conclusion: The experimental results prove that the proposed method performs better than other state-of-the-art techniques and can be used to protect multimedia ownership.

FFT-Based Data Hiding on Audio in LWT-Domain Using Spread Spectrum Technique

Audio watermarking is a process to hide digital data without being seen or heard by the sense of sight or hearing. Watermaking is applied to insert the copyright on digital media, such as an image file, an audio file or a video file. In this paper, we propose watermarking procedure to embed spread spectrum watermark into frequency domain of adaptive selected subband from host audio. Lifting Wavelet Transform (LWT) is used to decompose the host audio into several subbands, and then Fast Fourier Transform (FFT) transforms selected several subbands with lowest energy. The watermark image is converted into one-dimensional signal, then it is modulated by imperceptible pseudo-noise (PN) code with controlled gain. Next, the frequency domain of audio is added by modulated and imperceptible watermark prior to transforming it to time domain by Inverse FFT (IFFT) obtaining watermarked subbands. Finally, the watermarked subbands are combined with other unused subbands by inverse LWT (ILWT) becoming the perfect version of watermarked audio. The result of this method has good robustness against most attacks from stirmark benchmark experiments, good imperceptibility with Signal to Noise Ratio (SNR) more than 30 dB and payload 172.66 bps.

An Improvement in Digital Image Watermarking Scheme Based on Singular Value Decomposition and Wavelet Transform

An enormous growth of multimedia information in the internet has given rise to varied unauthorized use and modification. This authenticity issue is equally a limitation both in the defense data transmission and secured transmission. Digital watermarking is one of the legitimate solutions to the above problem since it makes possible validation and secure transmission of secret data. This paper presents a robust and secure digital image watermarking scheme that can be used for copyright protection. The scheme involves Lifting Wavelet Transform (LWT) and Singular Value Decomposition (SVD). The latest approach to wavelet transform is the Lifting Wavelet Transform and the significant transform technique for robust digital image watermarking is Singular Value Decomposition. The results are cross-validated by using inverse LWT and SVD. The digital signature mechanism is used to generate and embed a digital signature after the watermark is embedded, then the ownership is authenticated before extracting watermarks. In order to check the robustness of the method against various common image processing attacks like Mean, Median, Gaussian, Shear, Rotation and Crop, certain performance metrics such as peak to signal noise ratio (PSNR)and mean square error (MSE) is computed. To check similarity, normal cross-correlation (NCC) is used.

Multiple-image encryption via lifting wavelet transform and XOR operation based on compressive ghost imaging scheme

A multiple-image encryption method via lifting wavelet transform (LWT) and XOR operation is proposed, which is based on a row scanning compressive ghost imaging scheme. In the encryption process, the scrambling operation is implemented for the sparse images transformed by LWT, then the XOR operation is performed on the scrambled images, and the resulting XOR images are compressed in the row scanning compressive ghost imaging, through which the ciphertext images can be detected by bucket detector arrays. During decryption, the participant who possesses his/her correct key-group, can successfully reconstruct the corresponding plaintext image by measurement key regeneration, compression algorithm reconstruction, XOR operation, sparse images recovery, and inverse LWT (iLWT). Theoretical analysis and numerical simulations validate the feasibility of the proposed method.