In this paper we propose an algorithmic approach for Convolutional Neural Network (CNN) for digital watermarking which outperforms the existing frequency domain techniques in all aspects including security along with the criteria in the neural networks such as conditions embedded, and types of watermarking attack. This research addresses digital watermarking in deep neural networks and with comprehensive experiments through computational modeling and algorithm design, we examine the performance of the built system to demonstrate the potential of watermarking neural networks. The inability of intruder towards the retrieval of data without the knowledge of architecture and keys is also discussed and results of the proposed method are compared with the state of the art methods at different noises and attacks.
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