基于高斯增强模块的相机模型辨别

Gaussian Enhancement Module for Reinforcing High-frequency Details in Camera Model Identification

在多媒体取证中,高通滤波器是卷积神经网络常用的预处理层之一,用于抑制图像内容的影响,只强调高频特征。但与此同时,其他一些包含取证痕迹的有用信息也将被不加区别地剔除。为了解决这一问题,文中提出了一个简单而高效的高斯增强模块(Gaussian Enhancement Module,GEM)来提取“扩展的”高频特征,即在维持原有特征强度的基础上增强高频细节信息。GEM由两个连续的一维低通高斯滤波器组成,以获得一个模糊版本的特征图,并进一步得到相应的扩展高频残差。通过以高频残差作为空间掩膜,它可以自适应地强化脆弱和细微的低级取证特征,并防止在特征传递过程中出现衰减现象。在相机模型辨别数据集上进行实验,通过将该模块插入多个主流骨干网络,GEM仅仅带来非常轻微的模型复杂度的增加,网络性能和鲁棒性却显著提高,表明该模块支持“即插即用”,与特定的网络架构无关。

In multimedia forensics,a high-pass filter is one of the commonly used pre-processing layers by convolutional neural network to depress the impact of image content and only highlight high-frequency features.However,some other useful information containing forgery traces would also be removed indiscriminately in the meantime.To address this issue,in this paper,a simple yet effective Gaussian enhancement module is proposed to extract“extended”high-frequency features,namely,reinforce high-frequency details while maintaining the original feature strength.The GEM comprises two successive low-pass Gaussian filters to acquire a blurry version of the feature map and further get the corresponding extended high-frequency residual.It can strengthen fragile and subtle low-level forgery features adaptively and prevent feature attenuation as well.Experiments are conducted on the camera-model identification dataset by plugging the module into several mainstream backbone networks,indicating that it supports“plug and play”and is non-related to the specific network architecture.The proposed GEM brings a significant improvement both in the performance and the robustness of networks with the slightly increased complexity of models.