Watermark recognition online training, sample preparation and removal methods, watermark recognition online training device, equipment and medium

Abstract

The invention discloses watermark recognition online training, sample preparation and removal methods, a watermark recognition online training device, equipment and a medium. The training method comprises the steps: extracting a background image from a background data set, and randomly cutting the background image into a preset specification; extracting a watermark original image from the watermark data set, and performing image deformation processing on the watermark original image to form a watermark enhanced image; synthesizing the watermark enhancement image and the background image to obtain a corresponding synthesized image; and feeding the composite image as a training sample into a preset image segmentation model which has a coding and decoding structure and is suitable for capturing multi-scale features to implement sample training, so that the image segmentation model is suitable for removing the watermark original image from the to-be-identified image through the training. According to the invention, the watermark-containing training sample can be automatically synthesized on line and immediately used for training the image segmentation model, so that the model has the capability of identifying and removing the watermark in the picture.

Description

technical field [0001] This application belongs to image recognition technology, and specifically relates to a watermark recognition online training, sample preparation, removal method and their corresponding devices, electronic equipment, and non-volatile storage media. Background technique [0002] The relevant technology of using convolutional neural network for image recognition based on artificial intelligence has matured. As we all know, the realization of artificial intelligence depends on big data and its algorithms. In other words, an effective neural network depends on the multi-style of its training samples on the one hand, and on its network principle and structure on the other hand. [0003] For various purposes, major Internet platforms or applications add watermarks to pictures uploaded by users. These watermarks sometimes cause inconvenience, especially when the owner of the picture sometimes needs to reuse the original picture. The added watermark can only ...

AI Technical Summary

Problems solved by technology

[0004] In the existing technology, there is a technology to remove the watermark from the picture to be recognized, but due to the difference in the neural network it relies on and the difference in its training samples, its recognition effect is not ideal

Specifically, in terms of training samples, most of the data sets that the existing technology relies on are collected or based on simple processing, so the amount of data is limited, and its feature representation itself is inefficient, resulting in training effects Not good; in terms of the neural network it relies on, traditional neural networks are mostly used. Due to their design, these neural networks often rely on massive samples, and the demand for samples is particularly high

It can be seen that the type of network used and the number of samples used depend on each other, resulting in the generally poor effect of existing watermark recognition technologies and high implementation costs

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