  Most pictures shared online are accompanied by temporal metadata (i.e., the
day and time they were taken), which makes it possible to associate an image
content with real-world events. Maliciously manipulating this metadata can
convey a distorted version of reality. In this work, we present the emerging
problem of detecting timestamp manipulation. We propose an end-to-end approach
to verify whether the purported time of capture of an outdoor image is
consistent with its content and geographic location. We consider manipulations
done in the hour and/or month of capture of a photograph. The central idea is
the use of supervised consistency verification, in which we predict the
probability that the image content, capture time, and geographical location are
consistent. We also include a pair of auxiliary tasks, which can be used to
explain the network decision. Our approach improves upon previous work on a
large benchmark dataset, increasing the classification accuracy from 59.0% to
81.1%. We perform an ablation study that highlights the importance of various
components of the method, showing what types of tampering are detectable using
our approach. Finally, we demonstrate how the proposed method can be employed
to estimate a possible time-of-capture in scenarios in which the timestamp is
missing from the metadata.
