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.

中文翻译：

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用于图像拼接伪造检测的多尺度控制神经学习网络

社会稳定的保障来自生活的方方面面，图像信息安全作为其中之一，正遭受着各种恶意攻击。图像拼接伪造作为一种信息攻击手段，是指将一张图像的某些区域复制到另一张图像上，以隐藏原始信息的痕迹，造成严重后果。图像拼接伪造极其复杂，因为经过粘贴和复制操作的两张图像的属性差异很大。为了解决上述问题，我们提出了一种基于U-Net应用多尺度控制的神经学习网络（MCNL-Net）来识别图像是否被篡改并定位被篡改区域的方法。第一，通过残差传播模块和残差反馈模块的组合增强了 MCNL-Net 的学习能力。设计了一种巧妙的策略来控制 MCNL-Net 的每个构建块中局部感受野的大小。该策略使 MCNL-Net 能够实现多尺度结构的特性和优势，并学习指定的特征。为了进一步提高 MCNL-Net 的检测性能，提出了一种块注意力机制来控制每个构建块中输入信息的高级程度。此外，MaxBlurPool 方法首次应用于图像拼接伪造检测，保留了卷积神经网络的移位等效性。通过实验，