| Abstract |
Deep learning has become a promising model in the industry due to its superior learning accuracy and efficiency. In addition to conventional applications, such as fraud detection, natural language processing, image classification and reconstruction, object detection and segmentation this model can be widely used for data hiding, that is, watermarking. Existing transformed-domain-based watermarking provided better robustness toward attacks. In this article, an interesting autoencoder convolutional neural network (CNN)-based watermarking technique, AutoCRW, is proposed, which can prevent intellectual property theft of digital images. First, the autoencoder functionality of CNN generates two versions of the same image, namely positive and negative version of the images, which decompose by a transformed domain scheme. Then, watermark information is embedded into the output images, which can be extracted to realize copyright protection and ownership verification. Finally, a denoising convolutional neural network (DnCNN) is employed over the extracted mark to ensure the robustness of the watermarking system. Extensive experiments demonstrate that the proposed algorithm has high invisibility and good robustness against several attacks. |
