Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes appear and circulate across social platforms during major events in democratic countries. We analyze the 2025 Canadian federal election across X, Bluesky, and Reddit using a high-accuracy detector trained on diverse modern generative models, covering 187,778 posts. We find that 5.9% of election-related images were deepfakes. Right-leaning accounts shared them more often (9.2% of images flagged) than left-leaning users (3.9%), with flagged content more frequently defamatory or conspiratorial. Yet, most detected deepfakes were benign or non-political, and harmful ones drew little attention, accounting for only 0.1% of all views on X. Overall, deepfakes were present in the election conversation, but their reach was modest, and realistic fabricated images, although less common, drew higher engagement, highlighting growing concerns about their misuses.

Known for their impressive performance in generative modeling, diffusion models are attractive candidates for density-based anomaly detection. This paper investigates different variations of diffusion modeling for unsupervised and semi-supervised anomaly detection. In particular, we find that Denoising Diffusion Probability Models (DDPM) are performant on anomaly detection benchmarks yet computationally expensive. By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE). DTE estimates the distribution over diffusion time for a given input and uses the mode or mean of this distribution as the anomaly score. We derive an analytical form for this density and leverage a deep neural network to improve inference efficiency. Through empirical evaluations on the ADBench benchmark, we demonstrate that all diffusion-based anomaly detection methods perform competitively for both semi-supervised and unsupervised settings. Notably, DTE achieves orders of magnitude faster inference time than DDPM, while outperforming it on this benchmark. These results establish diffusion-based anomaly detection as a scalable alternative to traditional methods and recent deep-learning techniques for standard unsupervised and semi-supervised anomaly detection settings.