Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way. However, consideration from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a general deep neural network (DNN) based watermarking method to fulfill this goal. Instead of training a neural network for protecting a specific image, we train on an image set and use the trained model to protect a distinct test image set in a bulk manner. Respective evaluations both from the subjective and objective aspects confirm the supremacy and practicability of our proposed method. To demonstrate the robustness of this general neural watermarking mechanism, commonly used manipulations are applied to the watermarked image to examine the corresponding extracted watermark, which still retains sufficient recognizable traits. To the best of our knowledge, we are the first to propose a general way to perform watermarking using DNN. Considering its performance and economy, it is concluded that subsequent studies that generalize our work on utilizing DNN for intellectual content protection is a promising research trend.

中文翻译：

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使用深度神经网络进行数字水印的一般方法

物联网 (IoT) 技术促进了大量获取图像等数字内容。然而，从隐私或立法的角度考虑，仍然需要对知识内容进行保护。在本文中，我们提出了一种基于深度神经网络（DNN）的通用水印方法来实现这一目标。我们没有训练用于保护特定图像的神经网络，而是在图像集上进行训练，并使用经过训练的模型以批量方式保护不同的测试图像集。分别从主观和客观方面的评价证实了我们提出的方法的优越性和实用性。为了证明这种通用神经水印机制的鲁棒性，对水印图像应用常用的操作来检查相应的提取水印，该水印仍然保留了足够的可识别特征。据我们所知，我们是第一个提出使用 DNN 执行水印的通用方法的人。考虑到它的性能和经济性，可以得出结论，随后的研究将我们在利用 DNN 进行知识内容保护方面的工作进行概括是一个有前途的研究趋势。