The PS-Battles Dataset - an Image Collection for Image Manipulation Detection
This addresses the need for reliable tampering detection in digital media, which is crucial for security and authenticity, but it is incremental as it provides a new dataset rather than a novel detection method.
The authors tackled the problem of detecting manipulated images by creating the PS-Battles dataset, which includes 102,028 images grouped into 11,142 subsets with originals and manipulated derivatives to support automated detection methods.
The boost of available digital media has led to a significant increase in derivative work. With tools for manipulating objects becoming more and more mature, it can be very difficult to determine whether one piece of media was derived from another one or tampered with. As derivations can be done with malicious intent, there is an urgent need for reliable and easily usable tampering detection methods. However, even media considered semantically untampered by humans might have already undergone compression steps or light post-processing, making automated detection of tampering susceptible to false positives. In this paper, we present the PS-Battles dataset which is gathered from a large community of image manipulation enthusiasts and provides a basis for media derivation and manipulation detection in the visual domain. The dataset consists of 102'028 images grouped into 11'142 subsets, each containing the original image as well as a varying number of manipulated derivatives.