SSDeN: Framework for Screen-Shooting Resilient Watermarking via Deep Networks in the Frequency Domain

Mobile devices have been increasingly used to take pictures without leaving a trace. However, the application system can lead to confidential information leaks. A framework for screen-shooting-resilient watermarking via deep networks (SSDeN) in the frequency domain is put forward in this study to so...

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Bibliographic Details
Published inApplied sciences Vol. 12; no. 19; p. 9780
Main Authors Bai, Rui, Li, Li, Zhang, Shanqing, Lu, Jianfeng, Chang, Chin-Chen
Format Journal Article
LanguageEnglish
Published MDPI AG 01.10.2022
Subjects
CNN
confidential information
DCT
robustly
screen-shooting
watermark
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Summary:Mobile devices have been increasingly used to take pictures without leaving a trace. However, the application system can lead to confidential information leaks. A framework for screen-shooting-resilient watermarking via deep networks (SSDeN) in the frequency domain is put forward in this study to solve this problem. The proposed framework can extract the watermark from the leaked photo for copyright protection. SSDeN is an end-to-end process that combines convolutional neural network (CNN) with residual block to embed and extract watermarks in the DCT domain. We simulate some screen-shooting attacks to ensure the networks embed the watermark robustly. Our framework achieves the state-of-the-art performance on existing learning architectures for screen-shooting-resilient watermarking.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12199780