Lie detection is the process of determining whether someone is being truthful or deceptive
Micro-expressions are very brief & tiny, facial expressions that occur in response to emotions. They typically last for only a fraction of a second, making them difficult to detect without careful observation. These expressions can reveal genuine emotions that a person might be trying to conceal or manage consciously.The brain’s limbic system, which is involved in emotional processing, plays a key role in generating micro-expressions. When a person experiences an emotion, the brain sends signals to facial muscles to express that emotion. Sometimes, these signals are so rapid and sensitive that they are not consciously controlled.Detecting micro-expressions requires careful observation and often specialized training.

The Real to #Ghibli Image Dataset is a high-quality collection of 5,000 images designed for AI-driven style transfer and artistic transformations. This dataset is ideal for training GANs, CycleGAN, diffusion models, and other deep learning applications in image-to-image translation.It consists of two separate subsets:trainA (2,500 Real-World Images) → A diverse collection of human faces, landscapes, rivers, mountains, forests, buildings, vehicles, and more.
trainB_ghibli (2,500 Ghibli-Style Images) → Stylized images inspired by Studio Ghibli movies, including animated characters, landscapes, and artistic compositions.
Unlike paired datasets, this collection contains independent images in each subset, making it suitable for unsupervised learning approaches.

The Ghibli Art Image Dataset is a prototype designed for generating Ghibli-style images using machine learning. It includes three subsets—training, testing, and validation—each containing directories with two PNG images: one original (o.png) and one generated in Ghibli style (g.png). This dataset supports tasks like image classification and Ghibli-style image generation. As a small-scale version of a larger dataset, it provides essential resources like model code and a pre-trained Generator.pth model. The images were collected from platforms such as Meta, Google, and Instagram for research and experimentation purposes.

The dataset consists of authentic images sampled from the Shutterstock platform across various categories, including a balanced selection where one-third of the images feature humans. These authentic images are paired with their equivalents generated using state-of-the-art generative models. This structured pairing enables a direct comparison between real and AI-generated content, providing a robust foundation for developing and evaluating image authenticity detection systems.