A Diverse Large-Scale Dataset for Evaluating Rebroadcast Attacks

We describe the acquisition of a large, diverse set of rebroadcast images captured by a screen-grab, scanning a printed photo, or rephotographing a displayed or a printed photo. This dataset consists of 14,500 rebroadcast images captured from a diverse set of devices: 234 displays, 173 scanners, 282 printers, and 180 recapture cameras. The diversity of this dataset-across devices and types of rebroadcast-poses significant challenges to detecting rebroadcast attacks. We evaluate the efficacy of four different classifiers trained to simultaneously detect all types of rebroadcast images.