Deep Learning-based Text-in-Image Watermarking
arxiv(2024)
摘要
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.
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