Image Watermark Embedding and Extraction Method Based on BP Neural Network Compression Domain

Abstract

The invention discloses an image watermark embedding and extraction method based on BP neural network compression domain. The image watermark embedding and extraction method proposed by the present invention is based on the Arnold transformation, carries out BP neural network compression processing on the carrier image, and embeds the watermark information into the weights from the hidden layer to the output layer of the neural network, and the carrier image is almost No impact, very strong invisibility and higher security. In the compressed domain of this BP neural network, even after a variety of attacks, the watermarked carrier image can still remember the relationship between local pixels, so as to realize the correct detection of the watermark signal, making this embedding and extraction method It has strong robustness to various conventional image attacks, well balances the contradiction between robustness and imperceptibility of image watermarking, and can realize blind detection of image watermarking.

Description

Technical field [0001] The invention relates to a digital image watermarking technology in the field of information security, in particular to an image watermarking embedding and extraction method based on a BP neural network compression domain. Background technique [0002] As an effective supplement to traditional encryption methods, digital image watermarking uses data embedding methods to hide in digital image products to prove the creator’s ownership of his work, as a basis for identification and prosecution of illegal infringements, and to pass the watermark detection And analysis to ensure the integrity and reliability of digital information has become an effective means of intellectual property protection and digital multimedia anti-counterfeiting. In recent years, it has attracted people's attention and has become a hotspot in international academic research. For image watermarking to play its due role, it must possess two basic elements: robustness and imperceptibility....

AI Technical Summary

Problems solved by technology

Although the combination of machine learning and various image domain transformations has a good performance for the embedding and extraction of specific watermarks, there are still many problems. For example, image watermarking methods based on BP neural networks cannot achieve blind extraction. There are still some hidden dangers in the confidentiality, such as embedding and extraction methods based on space-frequency domain transformation, which generally have high computational complexity, and the ability to resist attacks needs to be strengthened.

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