Image Manipulation Detection

Image manipulation detection aims to identify and locate forgeries in digital images, a crucial task given the proliferation of manipulated media. Current research focuses on developing robust and generalizable deep learning models, often employing two-stream architectures, transformer networks, or graph convolutional networks to analyze both global image features and local manipulation artifacts. These advancements are driven by the need to counter increasingly sophisticated image editing techniques and improve the reliability of visual information across various applications, including combating misinformation and enhancing digital forensics. The field is also actively addressing challenges like limited training data and the need for more standardized evaluation benchmarks.