research
New research combines StyleGAN and diffusion models to generate high-quality visual counterfactual explanations, advancing explainable AI while revealing techniques applicable to synthetic media generation.
voice AI
Researchers introduce a hierarchical end-of-turn detection model with primary speaker segmentation, advancing real-time conversational AI systems for more natural voice interactions.
AI ethics
New research investigates how people attribute causality to AI across scenarios of agency, misuse, and misalignment, with implications for accountability in synthetic media and deepfake governance.
deepfake detection
York University's forensic speech science team earns recognition at major Deepfake Detection Challenge, advancing techniques for identifying synthetic audio and voice cloning fraud.
deepfake detection
Toronto Metropolitan University researchers are developing technical safeguards to combat deepfake deception, addressing the growing challenge of synthetic media authentication in digital spaces.
blockchain
New research explores how combining AI and blockchain creates robust systems for digital authenticity, content provenance, and decentralized verification in an era of synthetic media.
AI Safety
Researchers develop framework to measure how well AI agents can execute complex, multi-step cyber attacks, revealing critical insights for AI safety and security.
AI agents
New research proposes frameworks for identifying and counting AI agents—a critical question as autonomous systems create content and take actions with real-world consequences.
explainable AI
New research introduces FAME, a framework using formal methods to generate mathematically guaranteed minimal explanations for neural network decisions, advancing AI interpretability.
super-resolution
New research combines rank-factorized implicit neural bias with FlashAttention to scale super-resolution transformers efficiently, advancing high-quality image synthesis for AI-generated content.
AI Safety
New research reveals LLM-based safety evaluators fail to reliably measure adversarial robustness, raising critical questions about automated AI safety testing methodologies.
AI Safety
New research exposes a critical flaw in AI safety systems: models tasked with monitoring AI outputs show systematic bias when evaluating content they generated themselves.