Deepfake refers to data generated by a deep learning model based on a fictitious scenario. The data can be of any form ranging from image, audio, to even video. With the current state-of-the-art deepfake models, it is possible to synthesize highly deceitful content that can be indistinguishable from authentic content. Such content is always tied to ethical issues or concerns because they are widely used for blackmailing, creating fake news, or fake pornography videos. Generative models are hazardous in the wrong hands, but they still have some highly sought-after positive applications. One such application is synthetic data generation. Generative models can synthesize different types of data that can assist in providing insights into experiments that may never occur in real-life.

FINS’s thrust on this topic focuses on imbuing the deep learning models with the ability to better understand/interpret the exemplary data and gain finer control over the data generation process.