Many methods for verifying the integrity of digital images employ various fingerprints associated with acquisition devices. Data on an acquisition device and fingerprints are extracted from an image and confronted with a reference data set that includes all possible fingerprints of the acquisition device. This allows us to draw a conclusion whether the digital image has been modified or not. Thus it is critical to have a sufficiently large, reliable, and true reference data set, otherwise critical miscalculations can arise. Reference data sets are extracted from image data sets that in turn are collected from unknown and nonguaranteed environments (mostly from the Internet). Since often software modifications leave no obvious traces in the image file (e.g., in metadata), it is not easy to recognize original images, from which fingerprints of acquisition devices can be extracted to form true reference data sets. This is the problem addressed in this paper. Given a database consisting of “unguaranteed” images, we introduce a statistical approach for assessing image originality by using the image file's header information (e.g., JPEG compression parameters). First a general framework is introduced. Then the framework is applied to several fingerprint types selected for image integrity verification.