BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization

By Yue Wu, Wael Abd-Almageed, Premkumar Natarajan. European Conference on Computer Vision, 2018

306 citations recorded by the source corpus.

How researchers see this paper

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ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries With Anomalous Features (2019, methodology): “These clues include JPEG compression artifacts [25, 30, 5], edge inconsistencies [39, 53], noise pattern [33, 49, 20], color consistency [21], visual similarity [44, 45, 46], EXIF consistency [28], and camera model [14, 13].”
Media Forensics and DeepFakes: An Overview (2020, background): “To address this problem, both [140] and [141] propose a two-branch architecture followed by a fusion”
SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization (2020, methodology): “– Synthetic Dataset[37] is composed of four subsets: the removal and enhancements datasets from [37], the splicing dataset from [34] and the copy-move dataset from [35].”
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos (2019, background): “Digital image manipulation such as copy-move and splicing [44, 43] are examples of deliberate manipulation, while image repurposing [17, 35, 18] is an example of misleading context.”