Image watermark embedding and extracting method based on back propagation (BP) neural network compressed domain

Abstract

The invention discloses an image watermark embedding and extracting method based on a back propagation (BP) neural network compressed domain. The image watermark embedding and extraction method is based on Arnold transformation, to support BP neural network image compression processing, and the watermark is embedded into the neural network hidden layer to the output layer weight of the carrier image almost no effect, invisibility is very strong, and more secure. In the compressed domain that BP neural network, the carrier watermarked image even after multiple attack, is still able to remember the local relationship between pixels in order to achieve the correct detection of the watermark signal, making this embedding and extracting methods images of a variety of conventional attack has strong robustness, good balance contradictory and robust image watermarking imperceptibility between, and enables the blind to detect the watermark image.

Description

technical field [0001] The invention relates to a digital image watermark technology in the field of information security, in particular to an image watermark embedding and extraction method based on 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 works, and as a basis for identification and prosecution of illegal infringement. At the same time, through the detection of watermark And analysis to ensure the integrity and reliability of digital information, thus becoming an effective means of intellectual property protection and digital multimedia anti-counterfeiting, has aroused people's great attention in recent years, and has become a hot spot in international academic research. For image watermarking to play its due role, it must have two basic elements: robustness ...

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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