A General Approach for Using Deep Neural Network for Digital Watermarking
This addresses the need for scalable and robust watermarking for privacy and legislation in IoT, though it appears incremental as it builds on existing DNN methods for a specific domain.
The paper tackles the problem of intellectual content protection for digital images in IoT by proposing a general deep neural network-based watermarking method that trains on an image set and applies to distinct test sets in bulk, with evaluations confirming its supremacy and robustness against manipulations.
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.