Associative Watermarking for Image Authentication

Paper Title:

Performance Evaluation of Associative Watermarking Using Statistical Neurodynamics

Published on:

8 February 2024

Primary Category:

Multimedia

Paper Authors:

Ryoto Kanegae,

Masaki Kawamura

Bullets

Key Details

•

Proposes associative watermarking extending zero-watermarking

•

Uses hetero-associative memory for feature-to-watermark mapping

•

Uses auto-associative memory for watermark error correction

•

Demonstrates improved accuracy over zero-watermarking

•

Analyzes performance theoretically and empirically

Explore the topics in this paper

associative memory models

error correction

hetero-associative models

image processing

watermarking

AI generated summary

Associative Watermarking for Image Authentication

This paper proposes an associative watermarking method that extends zero-watermarking for images. It applies associative memory models, specifically a hetero-associative model for mapping image features to watermarks, and an auto-associative model for watermark error correction. Through theory and simulations, the proposed model is shown to outperform zero-watermarking in terms of watermark retrieval accuracy. Its performance is analyzed using state equations and bit error rate.