  Multimodal generative models are rapidly evolving, leading to a surge in the
generation of realistic video and audio that offers exciting possibilities but
also serious risks. Deepfake videos, which can convincingly impersonate
individuals, have particularly garnered attention due to their potential misuse
in spreading misinformation and creating fraudulent content. This survey paper
examines the dual landscape of deepfake video generation and detection,
emphasizing the need for effective countermeasures against potential abuses. We
provide a comprehensive overview of current deepfake generation techniques,
including face swapping, reenactment, and audio-driven animation, which
leverage cutting-edge technologies like GANs and diffusion models to produce
highly realistic fake videos. Additionally, we analyze various detection
approaches designed to differentiate authentic from altered videos, from
detecting visual artifacts to deploying advanced algorithms that pinpoint
inconsistencies across video and audio signals.
  The effectiveness of these detection methods heavily relies on the diversity
and quality of datasets used for training and evaluation. We discuss the
evolution of deepfake datasets, highlighting the importance of robust, diverse,
and frequently updated collections to enhance the detection accuracy and
generalizability. As deepfakes become increasingly indistinguishable from
authentic content, developing advanced detection techniques that can keep pace
with generation technologies is crucial. We advocate for a proactive approach
in the "tug-of-war" between deepfake creators and detectors, emphasizing the
need for continuous research collaboration, standardization of evaluation
metrics, and the creation of comprehensive benchmarks.
