  Most pictures shared online are accompanied by a temporal context (i.e., the
moment they were taken) that aids their understanding and the history behind
them. Claiming that these images were captured in a different moment can be
misleading and help to convey a distorted version of reality. In this work, we
present the nascent problem of detecting timestamp manipulation. We propose an
end-to-end approach to verify whether the purported time of capture of an image
is consistent with its content and geographic location. 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 59.03% to 81.07%. Finally,
an ablation study highlights the importance of various components of the
method, showing what types of tampering are detectable using our approach.
