Dual-branch multi-scale densely connected network for image splicing detection and localization

In the last two decades, numerous methods have been proposed for image tampering forensics, especially when deep learning is leveraged as a powerful feature extraction technique. However, existing deep learning-based methods are not efficient to jointly explore robust representations of tampering traces from both low-level forensics patterns and high-level forensics semantics. Besides, they become unreliable for spliced regions with unknown scales in the evaluation stage. To overcome the above-mentioned limitations, a novel Dual-branch Multi-scale Densely Connected Network (DMDC-Net) is proposed in this work for image splicing detection. DMDC-Net is constructed based on an encoder-decoder framework, which equips a multi-scale dense (MD) module for feature reuse and pixel-wise feature fusion (PFF) module to leverage forensics clues from different data components adaptively. In DMDC-Net, the given input image will be fed into two individual subnets to obtain deep representation maps from RGB and high-frequency components, which contains both visual and noise inconsistencies for further analysis. Besides, the MD module is applied in Encoder of each subnet to make the model jointly learn low-level forensics patterns and high-level semantic representations, which incorporates local and global features by a multi-scale manner of dense connections. The outputs of each subnet are fused in the PFF module to take advantage of their complementarity by trainable weights and get the final detection score map. Experimental results demonstrate the effectiveness of our method and the superior performance than other state-of-the-art methods in CASIA v2.0 and Columbia dataset for different scenarios, such as post-processing operations.