论文标题
使用神经网络对图像水印中嵌入强度的自适应控制
Adaptive Control of Embedding Strength in Image Watermarking using Neural Networks
论文作者
论文摘要
数字图像水印已被广泛用于不同应用程序，例如数字媒体的版权保护，例如音频，图像和视频文件。鲁棒性和透明度的两个相反标准是水印方法的目标。在本文中，我们提出了一个框架来确定适当的嵌入强度因子。该框架可以使用大多数基于DWT和DCT的盲水印方法。我们在可可数据集上使用蒙版R-CNN来找到每个子块的良好强度因子。实验表明，此方法对不同的攻击具有鲁棒性，并且具有良好的透明度。
Digital image watermarking has been widely used in different applications such as copyright protection of digital media, such as audio, image, and video files. Two opposing criteria of robustness and transparency are the goals of watermarking methods. In this paper, we propose a framework for determining the appropriate embedding strength factor. The framework can use most DWT and DCT based blind watermarking approaches. We use Mask R-CNN on the COCO dataset to find a good strength factor for each sub-block. Experiments show that this method is robust against different attacks and has good transparency.
