Analysis of a DFT-Based Watermarking Algorithm

Cedillo-Hernandez, Manuel; Miyatake, Mariko Nakano; Meana, Hector Manuel Perez

doi:10.1109/iceee.2005.1529569

Cited by 11 publications

(3 citation statements)

References 6 publications

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“…Traditionally, watermarks are embedded to the phase or magnitude (real values) of the host image in the DFT domain. However, embedding in phase only (Cheng & Cheng, 2005) or magnitude only (Hernandez et al, 2005) may lead to loss of information. With CVNN, both components can be used for embedding simultaneously without the loss of information.…”

Section: Artifi Cial Neural Network In Location Of Safe Regionmentioning

confidence: 99%

Damageless Digital Watermarking Using Complexvalued Artificial Neural Network

Olanweraju

2010

Journal of Information and Communication Technology

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Several high-ranking watermarking schemes using neural networks have been proposed in order to make the watermark stronger to resist attacks. However, the current system only deals with real value data. Once the data become complex, the current algorithms are not capable of handling complex data. In this paper, a distortion-free digital watermarking scheme based on Complex-Valued Neural Network (CVNN) in transform domain is proposed. Fast Fourier Transform (FFT) was used to obtain the complex number (real and imaginary part) of the host image. The complex values form the input data of the Complex Back-Propagation (CBP) algorithm. Because neural networks perform best on detection, classification, learning and adaption, these features are employed to simulate the Safe Region (SR) to embed the watermark, thus, watermark are appropriately mapped to the mid frequency of selected coeffi cients. The algorithm was appraised by Mean Squared Error MSE and Average Difference Indicator (ADI). Implementation results have shown that this watermarking algorithm has a high level of robustness and accuracy in recovery of the watermark.

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Section: Artifi Cial Neural Network In Location Of Safe Regionmentioning

confidence: 99%

Damageless Digital Watermarking Using Complexvalued Artificial Neural Network

Olanweraju

2010

Journal of Information and Communication Technology

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“…In literature, many watermarking algorithms have been proposed for video, video frames and images. In general, Digital watermarking techniques are classified into two categories: spatial domain watermarking, as in [4]- [6], and transform domain watermarking, such as in [7]- [10]. In spatial domain methods, watermarking is performed by modifying the pixels values in the host image.…”

Section: Introductionmentioning

confidence: 99%

A Blind DWT-SCHUR Based Digital Video Watermarking Technique

Rajab¹,

Al-Khatib²,

Al-Haj³

2015

JSEA

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Digital watermarking is one of the most powerful tools used in ownership and copyrights protection in digital media. This paper presents a blind digital video watermarking technique based on a combination scheme between the Discrete Wavelet transform in (DWT) and the real Schur Decomposition. The scheme starts with applying two-level DWT to the video scene followed by Schur decomposition in which the binary watermark bits are embedded in the resultant block upper triangular matrix. The proposed technique shows high efficiency due to the use of Schur decomposition which requires fewer computations compared to other transforms. The imperceptibility of the scheme is also very high due to the use of DWT transform; therefore, no visual distortion is noticed in the watermarked video after embedding. Furthermore, the technique proves to be robust against set of standard attacks like: Gaussian, salt and pepper and rotation and some video attacks such as: frame dropping, cropping and averaging. Both capacity and blindness features are also considered and achieved in this technique.

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“…Watermarking algorithms based on transform domain such as the DFT (Discrete Fourier Transform) [8], DCT (Discrete Cosine transform) and DWT (Discrete Wavelet Transform) [6] have been proposed to overcome the drawback of spatial domain watermarking.…”

Section: Introductionmentioning

confidence: 99%