You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper introduces a blind watermarking based on a convolutional neural network (CNN). We propose an iterative learning framework to secure robustness of watermarking. One loop of learning process consists of the following three stages: Watermark embedding, attack simulation, and weight update. We have learned a network that can detect a 1-bit message from a image sub-block. Experimental results show that this learned network is an extension of the frequency domain that is widely used in existing watermarking scheme. The proposed scheme achieved robustness against geometric and signal processing attacks with a learning time of one day.

Cited Abstract(s):
[1]: Existing techniques of watermarking make use of transform domain to have better robustness towards attacks. Here, we propose a novel learning based auto-encoder Convolutional Neural Network (CNN) for non-blind watermarking which outperforms the existing frequency domain techniques in terms of imperceptibility and robustness adding new dimension of usage of CNNs towards security. As these CNNs efficiently learn the features and represent the input at the output, they find applications in all the fields of science. Code book images of different size are generated using the proposed architecture and subjected to different attacks. Results of the proposed method are compared with state of the art methods at different noises and attacks such as Gaussian, speckle, compression effects, cropping, filtering, etc. The proposed scheme is validated against various possible attacks and its out-performance with state of the art methods is presented. Further, transfer learning capabilities of auto-encoder CNN for efficient way of learning new code book is presented. The inability of intruder towards retrieval of data without the knowledge of architecture and keys employed is also discussed.
The first CNN-based watermarking technique is proposed by [1] . Unlike the proposed technique, [1] uses CNN as an auto-encoder to present a new domain. However, [1] is a non-blind method. Therefore, although the robustness of these techniques is high, they are less useful in real scenarios. The proposed technique, on the other hand, is a blind watermarking.