  In the past years, numerous methods have been introduced to reliably detect
digital face image manipulations. Lately, the generalizability of these schemes
has been questioned in particular with respect to image post-processing. Image
compression represents a post-processing which is frequently applied in diverse
biometric application scenarios. Severe compression might erase digital traces
of face image manipulation and hence hamper a reliable detection thereof. In
this work, the effects of image compression on face image manipulation
detection are analyzed. In particular, a case study on facial retouching
detection under the influence of image compression is presented. To this end,
ICAO-compliant subsets of two public face databases are used to automatically
create a database containing more than 9,000 retouched reference images
together with unconstrained probe images. Subsequently, reference images are
compressed applying JPEG and JPEG 2000 at compression levels recommended for
face image storage in electronic travel documents. Novel detection algorithms
utilizing texture descriptors and deep face representations are proposed and
evaluated in a single image and differential scenario. Results obtained from
challenging cross-database experiments in which the analyzed retouching
technique is unknown during training yield interesting findings: (1) most
competitive detection performance is achieved for differential scenarios
employing deep face representations; (2) image compression severely impacts the
performance of face image manipulation detection schemes based on texture
descriptors while methods utilizing deep face representations are found to be
highly robust; (3) in some cases, the application of image compression might as
well improve detection performance.
