Gaussian Enhancement Module for Reinforcing High-frequency Details in Camera Model Identification
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 informa-tion containing forgery traces would also be removed indiscriminately in the meantime.To address this issue,in this paper,a sim-ple 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 sup-ports"plug and play"and is non-related to the specific network architecture.The proposed GEM brings a significant improve-ment both in the performance and the robustness of networks with the slightly increased complexity of models.
Camera model identificationDeep learningImage forensicsHigh-pass filterGauss enhancement
NETL
NSTL
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