Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userWatermark Embedding and Detection
85
0
0.0
(
0
)
Added by
Jidong Zhong
Publication date
2007
fields
Informatics Engineering
and research's language is
English
Authors
Jidong Zhong
Ask ChatGPT about the research
No Arabic abstract
The embedder and the detector (or decoder) are the two most important components of the digital watermarking systems. Thus in this work, we discuss how to design a better embedder and detector (or decoder). I first give a summary of the prospective applications of watermarking technology and major watermarking schemes in the literature. My review on the literature closely centers upon how the side information is exploited at both embedders and detectors. In Chapter 3, I explore the optimum detector or decoder according to a particular probability distribution of the host signals. We found that the performance of both multiplicative and additive spread spectrum schemes depends on the shape parameter of the host signals. For spread spectrum schemes, the performance of the detector or the decoder is reduced by the host interference. Thus I present a new host-interference rejection technique for the multiplicative spread spectrum schemes. Its embedding rule is tailored to the optimum detection or decoding rule. Though the host interference rejection schemes enjoy a big performance gain over the traditional spread spectrum schemes, their drawbacks that it is difficult for them to be implemented with the perceptual analysis to achieve the maximum allowable embedding level discourage their use in real scenarios. Thus, in the last chapters of this work, I introduce a double-sided technique to tackle this drawback. It differs from the host interference rejection schemes in that it utilizes but does not reject the host interference at its embedder. The perceptual analysis can be easily implemented in our scheme to achieve the maximum allowable level of embedding strength.
rate research
Read More
In our previous work, we introduced a double-sided technique that utilizes but not reject the host interference. Due to its nice property of utilizing but not rejecting the host interference, it has a big advantage over the host interference schemes in that the perceptual analysis can be easily implemented for our scheme to achieve the locally bounded maximum embedding strength. Thus, in this work, we detail how to implement the perceptual analysis in our double-sided schemes since the perceptual analysis is very important for improving the fidelity of watermarked contents. Through the extensive performance comparisons, we can further validate the performance advantage of our double-sided schemes.
Digital rights management (DRM) of depth-image-based rendering (DIBR) 3D video is an emerging area of research. Existing schemes for DIBR 3D video cause video distortions, are vulnerable to severe signal and geometric attacks, cannot protect 2D frame and depth map independently or can hardly deal with large-scale videos. To address these issues, a novel zero-watermark scheme based on invariant feature and similarity-based retrieval for protecting DIBR 3D video (RZW-SR3D) is proposed in this study. In RZW-SR3D, invariant features are extracted to generate master and ownership shares for providing distortion-free, robust and discriminative copyright identification under various attacks. Different from traditional zero-watermark schemes, features and ownership shares are stored correlatively, and a similarity-based retrieval phase is designed to provide effective solutions for large-scale videos. In addition, flexible mechanisms based on attention-based fusion are designed to protect 2D frame and depth map independently and simultaneously. Experimental results demonstrate that RZW-SR3D have superior DRM performances than existing schemes. First, RZW-SR3D can extracted the ownership shares relevant to a particular 3D video precisely and reliably for effective copyright identification of large-scale videos. Second, RZW-SR3D ensures lossless, precise, reliable and flexible copyright identification for 2D frame and depth map of 3D videos.
Recently, a self-embedding fragile watermark scheme based on reference-bits interleaving and adaptive selection of embedding mode was proposed. Reference bits are derived from the scrambled MSB bits of a cover image, and then are combined with authentication bits to form the watermark bits for LSB embedding. We find this algorithm has a feature of block independence of embedding watermark such that it is vulnerable to a collage attack. In addition, because the generation of authentication bits via hash function operations is not related to secret keys, we analyze this algorithm by a multiple stego-image attack. We find that the cost of obtaining all the permutation relations of $lcdot b^2$ watermark bits of each block (i.e., equivalent permutation keys) is about $(lcdot b^2)!$ for the embedding mode $(m, l)$, where $m$ MSB layers of a cover image are used for generating reference bits and $l$ LSB layers for embedding watermark, and $btimes b$ is the size of image block. The simulation results and the statistical results demonstrate our analysis is effective.
A great challenge to steganography has arisen with the wide application of steganalysis methods based on convolutional neural networks (CNNs). To this end, embedding cost learning frameworks based on generative adversarial networks (GANs) have been proposed and achieved success for spatial steganography. However, the application of GAN to JPEG steganography is still in the prototype stage; its anti-detectability and training efficiency should be improved. In conventional steganography, research has shown that the side-information calculated from the precover can be used to enhance security. However, it is hard to calculate the side-information without the spatial domain image. In this work, an embedding cost learning framework for JPEG Steganography via a Generative Adversarial Network (JS-GAN) has been proposed, the learned embedding cost can be further adjusted asymmetrically according to the estimated side-information. Experimental results have demonstrated that the proposed method can automatically learn a content-adaptive embedding cost function, and use the estimated side-information properly can effectively improve the security performance. For example, under the attack of a classic steganalyzer GFR with quality factor 75 and 0.4 bpnzAC, the proposed JS-GAN can increase the detection error 2.58% over J-UNIWARD, and the estimated side-information aided version JS-GAN(ESI) can further increase the security performance by 11.25% over JS-GAN.
The recent advent in the field of multimedia proposed a many facilities in transport, transmission and manipulation of data. Along with this advancement of facilities there are larger threats in authentication of data, its licensed use and protection against illegal use of data. A lot of digital image watermarking techniques have been designed and implemented to stop the illegal use of the digital multimedia images. This paper compares the robustness of three different watermarking schemes against brightness and rotation attacks. The robustness of the watermarked images has been verified on the parameters of PSNR (Peak Signal to Noise Ratio), RMSE (Root Mean Square Error) and MAE (Mean Absolute Error).
suggested questions
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
