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Source Camera Identification with Multi-Scale Feature Fusion Network

Source camera identification (SCI) technology has attracted increasing attentions over the past few years. However, the existing methods suppress image content with denoising filters that are largely agnostic to the specific sensor pattern noise (SPN) signal of interest. Such practices may potentially degrade the performance of SPN-based SCI due to un-reliable SPNs, especially when forensic images are transmitted through social networking platforms. In this paper, we address the problem of SPN-based device identification and propose a multi-scale feature fusion network (MSFFN) to boost the sensor-based source camera identification attribution. Specifically, several image patches of different scales are selected and input into the MSFFN to extract the SPN. The MSFFN is a multi-scale encoder-decoder structure, which is used to suppress image content and improve source attribution. Subsequently, the content-independent SPN features of different scales are fused. At last, the fused features are used for image source identification. Experimental results compared with the state-of-the-art demonstrate that the proposed scheme achieves significant improvements, especially in the accuracy of social networking image source identification.

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