Over the last few decades, rapid progress in AI, machine learning, and deep learning has
resulted in new techniques and various tools for manipulating multimedia. Though the …

Easy access to audio-visual content on social media, combined with the availability of
modern tools such as Tensorflow or Keras, and open-source trained models, along with …

The increasing fluency and widespread usage of large language models (LLMs) highlight
the desirability of corresponding tools aiding detection of LLM-generated text. In this paper …

Generative image modeling enables a wide range of applications but raises ethical
concerns about responsible deployment. This paper introduces an active strategy combining …

Every major technical invention resurfaces the dual-use dilemma—the new technology has
the potential to be used for good as well as for harm. Generative AI (GenAI) techniques, such …

Seismic advances in generative AI algorithms for imagery, text, and other data types has led
to the temptation to use synthetic data to train next-generation models. Repeating this …

The free access to large-scale public databases, together with the fast progress of deep
learning techniques, in particular Generative Adversarial Networks, have led to the …

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based
architecture for talking portrait synthesis that can concurrently achieve fast convergence, real …

Deep Learning (DL) is the most efficient technique to handle a wide range of challenging
problems such as data analytics, diagnosing diseases, detecting anomalies, etc. The …

Deepfakes are the result of digital manipulation to forge realistic yet fake imagery. With the
astonishing advances in deep generative models, fake images or videos are nowadays …