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• W4297464597 abstract "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." @default.
• W4297464597 created "2022-09-29" @default.
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• W4297464597 date "2022-09-28" @default.
• W4297464597 modified "2023-09-30" @default.
• W4297464597 title "SSDeN: Framework for Screen-Shooting Resilient Watermarking via Deep Networks in the Frequency Domain" @default.
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• W4297464597 doi "https://doi.org/10.3390/app12199780" @default.
• W4297464597 hasPublicationYear "2022" @default.
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