  Real world multimedia data is often composed of multiple modalities such as
an image or a video with associated text (e.g. captions, user comments, etc.)
and metadata. Such multimodal data packages are prone to manipulations, where a
subset of these modalities can be altered to misrepresent or repurpose data
packages, with possible malicious intent. It is, therefore, important to
develop methods to assess or verify the integrity of these multimedia packages.
Using computer vision and natural language processing methods to directly
compare the image (or video) and the associated caption to verify the integrity
of a media package is only possible for a limited set of objects and scenes. In
this paper, we present a novel deep learning-based approach for assessing the
semantic integrity of multimedia packages containing images and captions, using
a reference set of multimedia packages. We construct a joint embedding of
images and captions with deep multimodal representation learning on the
reference dataset in a framework that also provides image-caption consistency
scores (ICCSs). The integrity of query media packages is assessed as the
inlierness of the query ICCSs with respect to the reference dataset. We present
the MultimodAl Information Manipulation dataset (MAIM), a new dataset of media
packages from Flickr, which we make available to the research community. We use
both the newly created dataset as well as Flickr30K and MS COCO datasets to
quantitatively evaluate our proposed approach. The reference dataset does not
contain unmanipulated versions of tampered query packages. Our method is able
to achieve F1 scores of 0.75, 0.89 and 0.94 on MAIM, Flickr30K and MS COCO,
respectively, for detecting semantically incoherent media packages.
