Deep Learning-based Text-in-Image Watermarking

TL;DR

This paper presents a novel deep learning approach using Transformer architectures for embedding and extracting text watermarks in images, achieving superior robustness and imperceptibility compared to traditional methods.

Contribution

It introduces the first deep learning-based method for text-in-image watermarking that adapts to image features and enhances security and robustness.

Findings

Achieved higher robustness than traditional watermarking techniques.

Demonstrated improved imperceptibility of watermarks.

Set new benchmarks in text-in-image watermarking performance.

Abstract

In this work, we introduce a novel deep learning-based approach to text-in-image watermarking, a method that embeds and extracts textual information within images to enhance data security and integrity. Leveraging the capabilities of deep learning, specifically through the use of Transformer-based architectures for text processing and Vision Transformers for image feature extraction, our method sets new benchmarks in the domain. The proposed method represents the first application of deep learning in text-in-image watermarking that improves adaptivity, allowing the model to intelligently adjust to specific image characteristics and emerging threats. Through testing and evaluation, our method has demonstrated superior robustness compared to traditional watermarking techniques, achieving enhanced imperceptibility that ensures the watermark remains undetectable across various image contents.