Deep Learning-based Text-in-Image Watermarking

Bishwa Karki, Chun Hua Tsai, Pei Chi Huang, Xin Zhong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 en-hance 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.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 IEEE 7th International Conference on Multimedia Information Processing and Retrieval, MIPR 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages376-382
Number of pages7
ISBN (Electronic)9798350351422
DOIs
StatePublished - 2024
Event7th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2024 - San Jose, United States
Duration: Aug 7 2024Aug 9 2024

Conference

Conference7th IEEE International Conference on Multimedia Information Processing and Retrieval, MIPR 2024
Country/TerritoryUnited States
CitySan Jose
Period8/7/248/9/24

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Information Systems
  • Media Technology

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