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Título del libro: 2019 7th International Workshop On Biometrics And Forensics, Iwbf 2019
Título del capítulo: A robust image zero-watermarking using convolutional neural networks

Autores UNAM:
MANUEL CEDILLO HERNANDEZ;
Autores externos:
Idioma:
Inglés
Año de publicación:
2019
Palabras clave:

Biometrics; Convolution; Deep learning; Deep neural networks; Diagnosis; Learning algorithms; Medical imaging; Neural networks; Remote sensing; Watermarking; Convolutional neural network; Diagnostic errors; Minimum distortions; Remote sensing images; Robust Features; Watermark robustness; Watermark sequences; Zero-watermarking; Image watermarking

Resumen:

In the image zero-watermarking techniques, a watermark sequence is not physically embedded into the host image but has a logical linkage with the host image. This property of zero-watermarking is desirable for some kinds of images in which a minimum distortion may cause serious detection or diagnostic errors, such as medical images and remote sensing images. In this paper, we propose a robust zero-watermarking algorithm based on the Convolutional Neural Networks (CNN) and deep learning algorithm, in which robust inherent features of image is generated by the CNN, and it is combined with the owner's watermark sequence using XOR operation. The experimental results show the watermark robustness against several attacks and common image processing. © 2019 IEEE.

Entidades citadas de la UNAM:
Fuente:


ISBN:
9781728106229
Editorial:
Institute of Electrical and Electronics Engineers Inc.

Tipo de producto:
Conference Paper



DOI:
10.1109/IWBF.2019.8739245