  With the increasing influence of social media, online misinformation has
grown to become a societal issue. The motivation for our work comes from the
threat caused by cheapfakes, where an unaltered image is described using a news
caption in a new but false-context. The main challenge in detecting such
out-of-context multimedia is the unavailability of large-scale datasets.
Several detection methods employ randomly selected captions to generate
out-of-context training inputs. However, these randomly matched captions are
not truly representative of out-of-context scenarios due to inconsistencies
between the image description and the matched caption. We aim to address these
limitations by introducing a novel task of out-of-context caption generation.
In this work, we propose a new method that generates a realistic out-of-context
caption given visual and textual context. We also demonstrate that the
semantics of the generated captions can be controlled using the textual
context. We also evaluate our method against several baselines and our method
improves over the image captioning baseline by 6.2% BLUE-4, 2.96% CiDEr, 11.5%
ROUGE, and 7.3% METEOR
