Color Image Mosaic Detection Algorithm Based on Cascaded Multiscale Residual Neural Networks

Abstract

The appearance of a large number of image editing software packages allows people to easily tamper with image content information, resulting in a significant decrease in image credibility. A color image mosaic detection model based on CNN is proposed in this study. The cascade network structure of shallow thin neurons replaces the single network structure of deep multineurons in this study, and it compensates for the shortcomings of the previous image tampering detection algorithm using the single network structure of deep multineurons by relearning the characteristics of difficult samples. A multiscale convolution layer and a residual module are included in the model at the same time. Feature maps with different receptive fields can be fused with the multiscale convolution layer. By establishing a short connection between the input and output feature maps, the residual module can effectively reduce the risk of gradient disappearance in the model’s training process while also speeding up the network’s convergence speed. The simulation results show that this algorithm has an accuracy of 92.14% and an F1 value of 95.7%. This detection method outperforms other detection methods in terms of detection ability, reliability, and usability. This research gives users more information on which to base their judgments on when judging color mosaic images.