The progressive image copy-move forgery detection based on attention mechanism
To address the problem of information loss in the feature extraction stage of deep-learning-based methods for image copy-move forgery detection,a progressive image copy-move forgery detection model based on the attention mechanism is proposed.This model differs from the common structure of first downs-ampling to obtain strong semantic information,and then upsampling to restore high resolution and positional information in the feature extraction stage.Instead,it maintains parallel multi-resolution throughout the pro-cess,and enables information interaction between branches with different resolutions to achieve both strong semantic information and precise positional information at the same time.The key to feature extraction is to first generate feature maps with different resolutions,and then progressively connect features from low to high by using a combination of spatial attention and channel attention mechanisms to produce sub-masks with corresponding resolutions.Meanwhile,in image-level detection,the features are gradually connected from high to low resolution to enrich the information.Finally,focal loss is introduced to mitigate the influence of class imbalance on the model,and the mask under different resolutions is supervised with equal weight.The experimental results demonstrate that the proposed detection method outperforms the existing methods in both detection results of pixel level and image level on public datasets,thus validating the effectiveness of the attention mechanism and progressive feature connection.
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