4 papers
Kurtosis-Guided Denoising Score Matching for Tabular Anomaly Detection
Victor Livernoche, Jie Zan, Reihaneh Rabbany
Denoising score matching (DSM) provides a way to learn data distributions by training a neural network to recover the score function, defined as the gradient of the log density, fr…
Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics
Victor Livernoche, Andreea Musulan, Zachary Yang +2
Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes actually appear and circulate across social platforms during maj…
OpenFake: An Open Dataset and Platform Toward Real-World Deepfake Detection
Victor Livernoche, Akshatha Arodi, Andreea Musulan +5
Deepfakes, synthetic media created using advanced AI techniques, pose a growing threat to information integrity, particularly in politically sensitive contexts. This challenge is a…
On Diffusion Modeling for Anomaly Detection
Victor Livernoche, Vineet Jain, Yashar Hezaveh +1
Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different var…