Deep Learning-based Text-in-Image Watermarking
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866909176132796416 |
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| author | Karki, Bishwa Tsai, Chun-Hua Huang, Pei-Chi Zhong, Xin |
| author_facet | Karki, Bishwa Tsai, Chun-Hua Huang, Pei-Chi Zhong, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_13134 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning-based Text-in-Image Watermarking Karki, Bishwa Tsai, Chun-Hua Huang, Pei-Chi Zhong, Xin Multimedia Computer Vision and Pattern Recognition Machine Learning 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. |
| title | Deep Learning-based Text-in-Image Watermarking |
| topic | Multimedia Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2404.13134 |