Abstract
Splicing is the most common operation in image forgery, where the tampered background regions are imported from different images. Illumination maps are inherent attribute of images and provide significant clues when searching for splicing locations. This paper proposes an end-to-end dual-stream network for splicing location, where the illumination stream, which includes Grey-Edge (GE) and Inverse-Intensity Chromaticity (IIC), extract the inconsistent features, and the image stream extracts the global unnatural tampered features. The dual-stream feature in our network is fused through Multiple Feature Pyramid Network (MFPN), which contains richer context information. Finally, a Cluster Region Proposal Network (C-RPN) with spatial attention and an adaptive cluster anchor are proposed to generate potential tampered regions with greater retention of location information. Extensive experiments, which were evaluated on the NIST16 and CASIA standard datasets, show that our proposed algorithm is superior to some state-of-the-art algorithms, because it achieves accurate tampered locations at the pixel level, and has great robustness in post-processing operations, such as noise, blur and JPEG recompression.
Highlights
With the increasing popularity of editing tools, image content can be and discretely edited
The methods based on image equipment inconsistency use the discrepancy of the image device information, such as the Color Filter Array (CFA) [1], Error Level Analysis (ELA) [2], Noise Inconsistency (NI) [3]
The illumination map is an inherent attribute of the image and difficult to process uniformly, which can be considered as a major indicator for splicing forgery detection
Summary
With the increasing popularity of editing tools, image content can be and discretely edited. The methods based on the image attributes look for the inconsistency features between the original and tampered regions, such as the gray world [4], 3-D lighting environment [5], the two-color reflection model [6]. 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. Salloum proposed the Multi-Task Fully Convolutional Network (MFCN) with imagebased and edge-based streams to localize the splicing regions, which explored the validity of semantic segmentation framework in forgery detection [18].
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