  With the rapid development of deep learning techniques, the generation and
counterfeiting of multimedia material are becoming increasingly straightforward
to perform. At the same time, sharing fake content on the web has become so
simple that malicious users can create unpleasant situations with minimal
effort. Also, forged media are getting more and more complex, with manipulated
videos that are taking the scene over still images. The multimedia forensic
community has addressed the possible threats that this situation could imply by
developing detectors that verify the authenticity of multimedia objects.
However, the vast majority of these tools only analyze one modality at a time.
This was not a problem as long as still images were considered the most widely
edited media, but now, since manipulated videos are becoming customary,
performing monomodal analyses could be reductive. Nonetheless, there is a lack
in the literature regarding multimodal detectors, mainly due to the scarsity of
datasets containing forged multimodal data to train and test the designed
algorithms. In this paper we focus on the generation of an audio-visual
deepfake dataset. First, we present a general pipeline for synthesizing speech
deepfake content from a given real or fake video, facilitating the creation of
counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS)
and Dynamic Time Warping techniques to achieve realistic speech tracks. Then,
we use the pipeline to generate and release TIMIT-TTS, a synthetic speech
dataset containing the most cutting-edge methods in the TTS field. This can be
used as a standalone audio dataset, or combined with other state-of-the-art
sets to perform multimodal research. Finally, we present numerous experiments
to benchmark the proposed dataset in both mono and multimodal conditions,
showing the need for multimodal forensic detectors and more suitable data.
