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Watermarking consists of embedding in, and later extracting from, a digital cover a design called a watermark to prove the image's copyright/ownership. In watermarking, the use of deep-learning approaches is extremely beneficial due to their strong learning ability with accurate and superior results. By taking advantage of deep-learning, we designed an autoencoder convolutional neural network (CNN)-based watermarking algorithm to maximize the robustness while ensuring the invisibility of the watermark. A two network model, including embedding and extraction, is introduced to comprehensively analyze the performance of the algorithm. The embedding network architecture is composed of convolutional autoencoders. Initially, CNN is considered to obtain the feature maps from the cover and mark images. Subsequently, the feature maps of the mark and cover are concatenated with the help of the concatenation principle. In the extraction model, block-level transposed convolution and the rectified linear unit algorithm is applied on the extracted features of watermarked and cover images to obtain the hidden mark. Extensive experiments demonstrate that the proposed algorithm has high invisibility and good robustness against several attacks at a low cost. Further, our proposed scheme outperforms other state-of-the-art schemes in terms of robustness with good invisibility.

Nowadays, security of medical records is an important issue for tele-health system. Motivated by importance of such critical issue, we propose a robust watermarking method in the DWT-SVD-optimization domain. To solve the security issue, traditional watermarking schemes uses manual scaling factor to manage balance between imperceptibility, robustness and capacity. However, selection of manual scaling factor loses their optimize trade-off between these important parameters of watermarking. The suggested scheme encourages protection of medical data via techniques of dual watermarking and optimization scheme. In our scheme, the data owner imperceptibly embeds the dual watermarks in the medical cover image for extra level of security. Here, appropriate optimization schemes are used to find the scaling factor for embedding purpose. Further, the performance of this scheme is examined and compared. Moreover, the patient text data is coded via hamming code before inserting in to the cover so that bit error rate can be avoided or eliminated, if any. We show that the suggested scheme not only offers the high imperceptible but also robust for various attacks. Compared with existing schemes, our work offers more robustness while imperceptible and good capacity at the same time.

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.

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