A comprehensive overview of Deepfake: Generation, detection, datasets, and opportunities
When used maliciously, deepfake can pose detrimental implications to political and social
forces including reducing public trust in institutions, damaging the reputation of prominent …
forces including reducing public trust in institutions, damaging the reputation of prominent …
Image processing using multi-code gan prior
Despite the success of Generative Adversarial Networks (GANs) in image synthesis,
applying trained GAN models to real image processing remains challenging. Previous …
applying trained GAN models to real image processing remains challenging. Previous …
Unsupervised multi-target domain adaptation: An information theoretic approach
Unsupervised domain adaptation (uDA) models focus on pairwise adaptation settings where
there is a single, labeled, source and a single target domain. However, in many real-world …
there is a single, labeled, source and a single target domain. However, in many real-world …
Style transfer for co-speech gesture animation: A multi-speaker conditional-mixture approach
How can we teach robots or virtual assistants to gesture naturally? Can we go further and
adapt the gesturing style to follow a specific speaker? Gestures that are naturally timed with …
adapt the gesturing style to follow a specific speaker? Gestures that are naturally timed with …
No gestures left behind: Learning relationships between spoken language and freeform gestures
We study relationships between spoken language and co-speech gestures in context of two
key challenges. First, distributions of text and gestures are inherently skewed making it …
key challenges. First, distributions of text and gestures are inherently skewed making it …
[PDF][PDF] IRC-GAN: Introspective Recurrent Convolutional GAN for Text-to-video Generation.
Automatically generating videos according to the given text is a highly challenging task,
where visual quality and semantic consistency with text are two critical issues. In existing …
where visual quality and semantic consistency with text are two critical issues. In existing …
Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects
NUR Ahmed, A Badshah, H Adeel, A Tajammul… - IEEE …, 2024 - ieeexplore.ieee.org
In recent years, rapid advancements in deepfakes (incorporating Artificial Intelligence (AI),
machine, and deep learning) have updated tools and techniques for manipulating …
machine, and deep learning) have updated tools and techniques for manipulating …
Atgan: Adversarial training-based gan for improving adversarial robustness generalization on image classification
D Wang, W **, Y Wu, A Khan - Applied Intelligence, 2023 - Springer
Deep neural networks are vulnerable to adversarial examples, which are well-designed
examples aiming to cause models to produce wrong outputs with high confidence. Although …
examples aiming to cause models to produce wrong outputs with high confidence. Although …
[PDF][PDF] Real-Time Deepfake Image Generation Based on Stylegan2-ADA.
DA Talib, AA Abed - Revue d'Intelligence Artificielle, 2023 - researchgate.net
Accepted: 28 March 2023 Training Generative Adversarial Networks (GAN) usually leads to
hyper-specialization due to few data and this causes training to diverge. This paper …
hyper-specialization due to few data and this causes training to diverge. This paper …
[CITATION][C] A Style-Based Generator Architecture for Generative Adversarial Networks
T Karras - arxiv preprint arxiv:1812.04948, 2019