Translator Disclaimer
17 September 2007 Adaptive model and neural network based watermark identification
Author Affiliations +
Abstract
Transform techniques generally are more robust than spatial techniques for watermark embedding. In this research, neural networks and adaptive models are utilized to estimate watermarks in the presence of noise as well as other common image processing attacks in the discrete cosine transform (DCT) and discrete wavelet transform (DWT) domains. The proposed method can be used to semi-blindly determine the estimated watermark. In this paper, a comparative study to a previous method, LMS correlation based detection, is performed and demonstrates the efficacy of the proposed adaptive neural network watermark embedding and detection scheme under different attacks. Finally, the proposed scheme in the DCT transform domain is compared to the proposed scheme in the DWT domain.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lifford McLauchlan and Mehrübe Mehrübeoğlu "Adaptive model and neural network based watermark identification", Proc. SPIE 6700, Mathematics of Data/Image Pattern Recognition, Compression, Coding, and Encryption X, with Applications, 670009 (17 September 2007); https://doi.org/10.1117/12.735351
PROCEEDINGS
11 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Neural network-based watermark embedding and identification
Proceedings of SPIE (September 02 2008)
Fusing multiple images with evidential reasoning
Proceedings of SPIE (March 31 2003)
Print protection using high-frequency fractal noise
Proceedings of SPIE (June 21 2004)
Neural network edge detector
Proceedings of SPIE (March 31 1991)
GLCM and neural network-based watermark identification
Proceedings of SPIE (September 02 2008)

Back to Top