01651nas a2200241   4500000000100000000000100001008004100002260001200043100002100055700001800076700002200094700002700116700002200143700002200165700001800187700001900205245004700224856005800271300001200329490000600341520104800347022001401395       2024                            d  c12/20241 aHessen Bougueffa1 aMamadou Keita1 aWassim Hamidouche1 aAbdelmalik Taleb-Ahmed1 aHelena Liz-López1 aAlejandro Martín1 aDavid Camacho1 aAbdenour Hadid00aAdvances in AI-Generated Images and Videos  uhttps://www.ijimai.org/journal/bibcite/reference/3512  a173-2080 v93 aIn recent years generative AI models and tools have experienced a significant increase, especially techniques to generate synthetic multimedia content, such as images or videos. These methodologies present a wide range of possibilities; however, they can also present several risks that should be taken into account. In this survey we describe in detail different techniques for generating synthetic multimedia content, and we also analyse the most recent techniques for their detection. In order to achieve these objectives, a key aspect is the availability of datasets, so we have also described the main datasets available in the state of the art. Finally, from our analysis we have extracted the main trends for the future, such as transparency and interpretability, the generation of multimodal multimedia content, the robustness of models and the increased use of diffusion models. We find a roadmap of deep challenges, including temporal consistency, computation requirements, generalizability, ethical aspects, and constant adaptation.  a1989-1660