论文标题
强大的空间扩展深神经图像水印
Robust Spatial-spread Deep Neural Image Watermarking
论文作者
论文摘要
水印是一种将信息嵌入到图像中的操作，尽管在上面施加了一些扭曲，但仍可以识别图像的所有权。在本文中，我们提出了一种新颖的端到端解决方案，用于使用卷积神经网络嵌入和恢复数字图像中的水印。该方法基于将消息传播到图像的空间域上，从而降低了“每个像素的本地位”容量。为了获得模型，我们使用了对抗性训练，并在编码器和解码器之间应用了噪音层。此外，我们扩大了通常考虑在水印上的攻击的范围，并根据其范围对攻击进行分组，我们达到了很高的一般鲁棒性，最著名的是针对JPEG压缩，高斯模糊，下采样或调整。为了帮助我们在培训模型中，我们还提出了JPEG的精确可区分近似值。
Watermarking is an operation of embedding an information into an image in a way that allows to identify ownership of the image despite applying some distortions on it. In this paper, we presented a novel end-to-end solution for embedding and recovering the watermark in the digital image using convolutional neural networks. The method is based on spreading the message over the spatial domain of the image, hence reducing the "local bits per pixel" capacity. To obtain the model we used adversarial training and applied noiser layers between the encoder and the decoder. Moreover, we broadened the spectrum of typically considered attacks on the watermark and by grouping the attacks according to their scope, we achieved high general robustness, most notably against JPEG compression, Gaussian blurring, subsampling or resizing. To help us in the models training we also proposed a precise differentiable approximation of JPEG.
