Tag: Watermarking

Free Download Digital Watermarking: Second International Workshop, IWDW 2003, Seoul, Korea, October 20-22, 2003. Revised Papers By Fernando Pérez-González (auth.), Ton Kalker, Ingemar Cox, Yong Man Ro (eds.)
2004 | 604 Pages | ISBN: 354021061X | PDF | 10 MB
We are happy to present to you the proceedings of the 2nd International Workshop on Digital Watermarking, IWDW 2003. Since its modern re-appearance in the academic community in the early 1990s, great progress has been made in understanding both the capabilities and the weaknesses of digital watermarking. On the theoretical side, we all are now well aware of the fact that digital waterma- ing is best viewed as a form of communication using side information. In the case of digital watermarking the side information in question is the document to be wat- marked. This insight has led to a better understanding of the limits of the capacity and robustness of digital watermarking algorithms. It has also led to new and improved watermarking algorithms, both in terms of capacity and imperceptibility. Similarly, the role of human perception, and models thereof, has been greatly enhanced in the study and design of digital watermarking algorithms and systems. On the practical side, applications of watermarking are not yet abundant. The original euphoria on the role of digital watermarking in copy protection and copyright prot- tion has not resulted in widespread usage in practical systems. With hindsight, a n- ber of reasons can be given for this lack of practical applications.

Free Download Digital Watermarking for Machine Learni by Lixin Fan, Chee Seng Chan, Qiang Yang
English | February 28, 2023 | ISBN: 9811975531 | 233 pages | MOBI | 14 Mb
Machine learning (ML) models, especially large pretrained deep learning (DL) models, are of high economic value and must be properly protected with regard to intellectual property rights (IPR). Model watermarking methods are proposed to embed watermarks into the target model, so that, in the event it is stolen, the model’s owner can extract the pre-defined watermarks to assert ownership. Model watermarking methods adopt frequently used techniques like backdoor training, multi-task learning, decision boundary analysis etc. to generate secret conditions that constitute model watermarks or fingerprints only known to model owners. These methods have little or no effect on model performance, which makes them applicable to a wide variety of contexts. In terms of robustness, embedded watermarks must be robustly detectable against varying adversarial attacks that attempt to remove the watermarks. The efficacy of model watermarking methods is showcased in diverse applications including image classification, image generation, image captions, natural language processing and reinforcement learning.

Free Download Image Watermarking Techniques
English | 2024 | ISBN: 3031409736 | 103 Pages | PDF EPUB (True) | 21 MB
This book investigates the image watermarking domain, analyzing and comparing image watermarking techniques that exist in current literature. The author’s goal is to aid researchers and students in their studies in the vast and important domain of image watermarking, including its advantages and risks. The book has three chapters: image watermarking using data compression; speech modulation for image watermarking; and secure image watermarking based on LWT and SVD.