# Image Splicing Location Based on Illumination Maps and Cluster Region Proposal Network  
[PERSON]  
1 School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
3 College of Computer Science and Technology, Jilin University, Changchun 130012, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (G.Y.) [PERSON]  
1 School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
3 College of Computer Science and Technology, Jilin University, Changchun 130012, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (G.Y.) [PERSON]  
1 School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China; [EMAIL_ADDRESS] (Y.Z. ); [EMAIL_ADDRESS] (S.L.) [PERSON]  
1 School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China; [EMAIL_ADDRESS] (Y.Z. ); 201932804013@stu. [PERSON] firstly proposed a two-stage deep learning detection approach, which included the stacked autoencoder model to learn the block features, and the fusion model to integrate the context information of each blocks [7]. Inspired by this, a first layer of CNN with the basic high-pass filter to calculate the residual maps is applied in a Spatial Rich Model (SRM), which can detect splicing and copy-move [8]. [PERSON] considered a CNN-based method to extract camera features of image patches and then to cluster to detect whether an image has been forged [9]. However, the above methods extract the features based on patches, where the location accuracy is patch-level. In constrained image splicing localization, the Deep Matching and Validation Network (DMVN) [10] and Deep Matching network based on Atrous Convolution (DMAC) [11] were proposed to generate probability estimates and locate constrained splicing regions. [PERSON] further introduced a novel attention-aware encoder-decoder deep matching network with atrous convolution, which had superior performance [12]. A combination of four features has been extracted and trained using a logistic regression classification model [13]. Ringed Residual U-net (RRU-Net) [14], Coarse to Refined Network [15] and Mask R-CNN [16] were directly applied for splicing location. Spatial Pyramid Attention Network (SPAN) is a self-attentive hierarchical structure, which constructs a pyramid of local self-attention blocks to locate tampered regions [17]. These blind forensics methods only extract the feature based on single-stream, where the tampered region location accuracy is relative low. In contrast with the single-stream, the dual-stream methods usually require image preprocessing to extract manipulating traces. [PERSON] proposed the Multi-Task Fully Convolutional Network (MFCN) with image-based and edge-based streams to localize the splicing regions, which explored the validity of semantic segmentation framework in forgery detection [18]. Further, the dualstream with different input were proposed, such as spatial and frequency domains [19], RGB and noise images [20], RGB and SRM features [21]. A Skip connection architecture based on Long-Short Term Memory-Encoder Decoder (LSTM-EnDec-Skip) [22] is an improvement of LSTM-EnDec [23], which applied a jump connection structure to the multi-task network. In summary, the input of dual-stream framework always includes the RGB image and the other tampered attribute image. Even though the above methods can locate splicing tampered regions, the location precision still needs improvement and the tampered attribute trace is always hidden through post-processing. The illumination maps are an inherent attribute of the image, which is difficult to modify but can help locate the splicing regions. We proposes a novel splicing blind forensics framework, where the image stream is used to extract semantic features, and the illumination stream is used to extract tampered features. The two stages are fused by Multiple Feature Pyramid Network (MFPN).