  Generative techniques continue to evolve at an impressively high rate, driven
by the hype about these technologies. This rapid advancement severely limits
the application of deepfake detectors, which, despite numerous efforts by the
scientific community, struggle to achieve sufficiently robust performance
against the ever-changing content. To address these limitations, in this paper,
we propose an analysis of two continuous learning techniques on a Short and a
Long sequence of fake media. Both sequences include a complex and heterogeneous
range of deepfakes generated from GANs, computer graphics techniques, and
unknown sources. Our study shows that continual learning could be important in
mitigating the need for generalizability. In fact, we show that, although with
some limitations, continual learning methods help to maintain good performance
across the entire training sequence. For these techniques to work in a
sufficiently robust way, however, it is necessary that the tasks in the
sequence share similarities. In fact, according to our experiments, the order
and similarity of the tasks can affect the performance of the models over time.
To address this problem, we show that it is possible to group tasks based on
their similarity. This small measure allows for a significant improvement even
in longer sequences. This result suggests that continual techniques can be
combined with the most promising detection methods, allowing them to catch up
with the latest generative techniques. In addition to this, we propose an
overview of how this learning approach can be integrated into a deepfake
detection pipeline for continuous integration and continuous deployment
(CI/CD). This allows you to keep track of different funds, such as social
networks, new generative tools, or third-party datasets, and through the
integration of continuous learning, allows constant maintenance of the
detectors.
