Shivangi Aneja is a PhD student in Visual Computing Lab advised by Prof. Matthias Nießner. Prior to that, she completed her Master’s degree in Informatics from Technical University of Munich. She joined the lab during her master thesis, where she worked on “Generalized Zero and Few-Shot Transfer for Facial Forgery Detection”. She holds a Bachelor’s degree in Computer Science from the National Institute of Technology, India, where she graduated with a Gold Medal for academic excellence.
|COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning|
|Shivangi Aneja, Chris Bregler, Matthias Nießner|
|Despite the recent attention to DeepFakes, one of the most prevalent ways to mislead audiences on social media is the use of unaltered images in a new but false context. To address these challenges and support fact-checkers, we propose a new method that automatically detects out-of-context image and text pairs. Our key insight is to leverage grounding of image with text to distinguish out-of-context scenarios that cannot be disambiguated with language alone. Check out the paper for more details.|
|TAFIM: Targeted Adversarial Attacks against Facial Image Manipulations|
|Shivangi Aneja, Lev Markhasin, Matthias Nießner|
|We introduce a novel data-driven approach that produces image-specific perturbations which are embedded in the original images to prevent face manipulation by causing the manipulation model to produce a predefined manipulation target. Compared to traditional adversarial attack baselines that optimize noise patterns for each image individually, our generalized model only needs a single forward pass, thus running orders of magnitude faster and allowing for easy integration in image processing stacks, even on resource-constrained devices like smartphones. Check out the paper for more details.|