In this paper, we first propose a new blind image watermarking scheme robust to geometric attacks and compressions. The scheme is based on contourlet transform (CT) and principal component analysis (PCA). The scheme uses the principal components of the largest contourlet coefficients of the last directional subband of the cover image to embed the watermark. Meanwhile, with the noise visibility function (NVF), the watermarking strength is adjusted adaptively to preserve the perceptual quality of the image. The watermark can be detected with high accuracy after various possible distortions. The normalized correlation (NC) between the original watermark and the watermark extracted from the distorted watermarked image is used as the robustness evaluation criterion. The simulation results demonstrate that the proposed scheme has good performance in terms of both quality and robustness against a variety of image-processing attacks, such as rotation, scaling and image compressions. Then we extend the scheme to blind video watermarking. The performance of the video watermarking scheme is evaluated against video attacks like rotation, frame averaging, noise additions and video compressions. The introduction of the CT produces robustness against image and video compressions, and the PCA yields resistance to geometric attacks.

Contourlet-based image and video watermarking robust to geometric attacks and compressions

Digital watermarking has been recognized as an effective technique to protect intellectual property by embedding secret information into the digital products. This paper presents a new watermarking technique for digital image applications by using the contourlet domain. The contourlet transform is a powerful tool to capture singularities along smooth object boundaries with different elongated shapes and directions that helps the watermarking technique to achieve high performance. Specifically, the host image is decomposed into subbands by using the contourlet transform. Then, the mid frequency subbands are chosen to embed watermark with suitable embedment factors. The peak signal-to-noise ratio (PSNR) and normalized correlation (NC) are used to evaluate the performance of the algorithm. Simulations on different images are carried out to evaluate the invisibility and robustness of the proposed scheme. The experimental results show that the proposed method outperforms the others in terms of invisibility and robustness for the lossy JPEG compression.

A new image watermarking scheme using contourlet transforms

Digital watermarking is one of the most widely used techniques for the protection of ownership rights of digital audio, images, and videos. One of the desirable properties of a digital watermarking scheme is its robustness against attacks aiming at removing or destroying the watermark from the host data. Different from the common watermarking techniques based on the spatial domain or transform domain, in this paper, a novel scheme of digital image blind watermarking based on the combination of the discrete wavelet transform (DWT) and the convolutional neural network (CNN) is proposed. Firstly, the host images are decomposed by the DWT with 4 levels and, then, the low frequency sub-bands of the first level and the high frequency sub-bands of the fourth level are used as the input data and the output target data to train the CNN model for embedding and extracting the watermark. Experimental results show that the proposed scheme has superior performance against common attacks of JPEG compression, mean and median filtering, salt and pepper noise, Gaussian noise, speckle noise, brightness modification, scaling, cropping, rotation, and shearing operations.

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An Efficient Robust Blind Watermarking Method Based on Convolution Neural Networks in Wavelet Transform Domain

Visual Cryptography Scheme (VCS) is Image Encryption technique Which Generate Share images. An image Steganography is a technique that hide secrete message in image. First selection of image based reCAPTCHA. After that, by applying Visual Cryptography on Black and White (BW) image and generate share1 and share2. In Progressive Visual Cryptography Scheme (PVCS) System share1 hide into reCAPTCHA image (Cover image). For Rotation Scale Transform (RST) based attack we are using transform based domain with Block DWT+SVD transform with modified Zernike Moment for rotational invariant pixel Selection. Also, find out some problems based on Dual Attack with different scheme attacks by the attackers. The easygoing of a powerful assault on the Stego picture by an aggressor is less because of utilization of this Modified Zernike strategy. The primary advantage of the framework is that the picture is moved before installing of the stego picture and de-moved subsequent to inserting.

A study of privacy preserving system based on progressive VCS and RST attacks

Watermarking inserts invisible data into content to protect copyright. The embedded information provides proof of authorship and facilitates the tracking of illegal distribution, etc. Current robust watermarking techniques have been proposed to preserve inserted copyright information from various attacks, such as content modification and watermark removal attacks. However, since the watermark is inserted in the form of noise, there is an inevitable effect of reducing content visual quality. In general, most robust watermarking techniques tend to have a greater effect on quality, and content creators and users are often reluctant to insert watermarks. Thus, there is a demand for a watermark that maintains maximum image quality, even if the watermark performance is slightly inferior. Therefore, we propose a watermarking technique that maximizes invisibility while maintaining sufficient robustness and data capacity to be applied in real situations. The proposed method minimizes watermarking energy by adopting curvelet domain multi-directional decomposition to maximize invisibility and maximizes robustness against signal processing attacks with a watermarking pattern suitable for curvelet transformation. The method is also robust against geometric attacks by employing the watermark detection method utilizing curvelet characteristics. The proposed method showed very good results of a 57.65 dB peak signal-to-noise ratio in fidelity tests, and the mean opinion score showed that images treated with the proposed method were hardly distinguishable from the originals. The proposed technique also showed good robustness against signal processing and geometric attacks compared with existing techniques.

Robust watermarking in curvelet domain for preserving cleanness of high-quality images

The paper proposes an image watermarking algorithm on curvelet transform domain. Different from other conventional methods which embed the watermark in spatial domain, discrete cosine transform (DCT), or discrete wavelet transform (DWT), the present paper uses curvelet transform domain for image watermarking since curvelet transform is effective when representing curves with fewer coefficients than other transforms such as wavelets for the same accuracy. The paper will investigate the different scales of curvelet transform domain for embedding the watermark to achieve both invisibility and robustness. The performance of image watermarking in curvelet transform domain is compared with the same algorithm but applied on DCT-DWT combined domain. Peak signal-to-noise ratio (PSNR) and normalized correlation (NC) are used to evaluate the invisibility and the robustness of the algorithms, respectively. Experimental results show that invisibility and robustness are ensured. Performance in terms of invisibility and robustness of the proposed algorithm is better than that applying on DCT-DWT combined domain for the lossy JPEG compression attacks, speckle and Gaussian noise.

Sy C. Nguyen

Papers

An improved image watermarking scheme using selective curvelet scales

A new image watermarking scheme using contourlet transforms