Convolutional Neural Network-Based Image Watermarking using Discrete Wavelet Transform

TL;DR

This paper introduces a CNN and wavelet transform-based watermarking method that embeds and extracts watermarks from digital images, ensuring security and ownership verification with high performance across various image resolutions.

Contribution

It presents a novel 11-layer CNN architecture combined with wavelet transforms for robust, resolution-independent image watermarking capable of handling all watermark types.

Findings

High similarity between original and extracted watermarks

Watermarked images maintain high visual quality

Method is effective across different image resolutions

Abstract

With the growing popularity of the Internet, digital images are used and transferred more frequently. Although this phenomenon facilitates easy access to information, it also creates security concerns and violates intellectual property rights by allowing illegal use, copying, and digital content theft. Using watermarks in digital images is one of the most common ways to maintain security. Watermarking is proving and declaring ownership of an image by adding a digital watermark to the original image. Watermarks can be either text or an image placed overtly or covertly in an image and are expected to be challenging to remove. This paper proposes a combination of convolutional neural networks (CNNs) and wavelet transforms to obtain a watermarking network for embedding and extracting watermarks. The network is independent of the host image resolution, can accept all kinds of watermarks, and has only 11 layers while keeping performance. Performance is measured by two terms; the similarity between the extracted watermark and the original one and the similarity between the host image and the watermarked one.