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Controlling Neural Learning Network with Multiple Scales for Image Splicing Forgery Detection

The guarantee of social stability comes from many aspects of life, and image information security as one of them is being subjected to various malicious attacks. As a means of information attack, image splicing forgery refers to copying some areas of an image to another image to hide the traces of the original information and leads to grave consequences. Image splicing forgery is extremely complex since the attributes of the two images subjected to the pasting and copying operations are greatly different. In order to solve the issue mentioned above, we propose a method by applying a neural learning network controlled by multiple scales (MCNL-Net) based on U-Net to identify whether an image has been tampered and to locate the tampered regions. Firstly, the learning capacity of MCNL-Net is enhanced by the combination of a residual propagation module and a residual feedback module. An ingenious strategy is designed to control the size of local receptive field in each building block of MCNL-Net. The strategy makes MCNL-Net able to achieve properties and superiorities of multi-scale structure and learn specified features. For further improving the detection performance of MCNL-Net, a block attention mechanism is proposed to control the advanced degree of the input information in each building block. In addition, a MaxBlurPool method is applied into image splicing forgery detection for the first time, preserving the shift-equivariance of a convolutional neural network. Through experiments, we demonstrate that MCNL-Net can achieve more promising results and offer stronger robustness than the state-of-the-art splicing forgery detection methods.