Comparison of "Spread-Quantization" Video Watermarking Techniques for Copyright Protection in the Spatial and Transform Domain

10.5772/37998 ◽  
2012 ◽  
Author(s):  
Radu Ovidiu ◽  
Nicolae Vizireanu
Keyword(s):  
Copyright Protection ◽  
Video Watermarking ◽  
Transform Domain

Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain

2016 ◽  
pp. 243-263
Author(s):  
V. Santhi ◽  
D. P. Acharjya
Keyword(s):  
Wavelet Transform ◽  
Literature Review ◽  
Digital Watermarking ◽  
Computational Intelligence ◽  
Copyright Protection ◽  
End Users ◽  
Multimedia Data ◽  
Transform Domain

Advances in technologies facilitate the end users to carry out unauthorized manipulation and duplication of multimedia data with less effort. Because of these advancements, the two most commonly encountered problems are (1) copyright protection and (2) unauthorized manipulation of multimedia data. Thus a scheme is required to protect multimedia data from those two above said problems. Digital Watermarking is considered as one of the security mechanisms to protect copyrights of multimedia data. The literature review reveals that the calculation of scaling and embedding parameters are not completely automated. In order to automate the procedure of calculating scaling and embedding parameters the computational intelligence need to be incorporated in the watermarking algorithm. Moreover the quality of the watermarked images could also be preserved by combining computational intelligence concepts. Thus watermarking schemes utilizing computational intelligence concepts could be called as intelligence based watermarking schemes and it is presented in this chapter in detail.

Hardware Implementations of Image/Video Watermarking Algorithms

2010 ◽  
pp. 425-454
Author(s):  
Fayez M. Idris
Keyword(s):  
Digital Media ◽  
Digital Watermarking ◽  
Copyright Protection ◽  
Video Watermarking ◽  
Patient Records ◽  
Electronic Patient Records ◽  
Hardware Implementations ◽  
Consumer Electronic ◽  
Software Implementations ◽  
Enhance Efficiency

Digital watermarking is a process in which a secondary pattern or signature, called a watermark, is hidden into a digital media (e.g., image and video) such that it can be detected or extracted later for different intentions. Digital watermarking has many applications including copyright protection, authentication, tamper detection, and embedding of electronic patient records in medical images. Various software implementations of digital watermarking algorithms can be built. While software implementations can address digital watermarking in off-line applications, they cannot meet the requirements of many applications. For example, in consumer electronic devices, a software solution would be very expensive. This has motivated the development of hardware implementations of digital watermarking. In this chapter, the authors present a detailed survey of existing hardware implementations of image and video watermarking algorithms. Fundamental design issues are discussed and special techniques exploited to enhance efficiency are identified. Future outlooks are also presented to address the challenges of hardware architecture design for image and video watermarking.

scholarly journals A Robust Image Watermarking Approach Using Cycle Variational Autoencoder

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Qiang Wei ◽  
Hu Wang ◽  
Gongxuan Zhang
Keyword(s):  
Copyright Protection ◽  
Cloud Storage ◽  
Image Watermarking ◽  
Rapid Development ◽  
Transform Domain ◽  
Watermark Image ◽  
Visual Effects ◽  
Variational Autoencoder ◽  
Robust Image ◽  
Traditional Approaches

With the rapid development of Internet and cloud storage, data security sharing and copyright protection are becoming more and more important. In this paper, we introduce a robust image watermarking algorithm for copyright protection based on variational autoencoder networks. The proposed image watermarking embedding and extracting network consists of three parts: encoder subnetwork, decoder subnetwork, and detector subnetwork. In the training process, the encoder and decoder subnetworks learn a robust image representation model and further implement the embedding of 1-bit watermark image to the cover image. Meanwhile, the detector subnetwork learns to extract the 1-bit watermark image from the embedding image. Experimental results demonstrate that the watermarked images generated by the proposed algorithm have better visual effects and are more robust against geometric and noise attacks than traditional approaches in the transform domain.

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