4 papers
VETO: Towards Protecting Images From Frontier AI Editing
Jonas Grebe, Hossein Shakibania, Tobias Braun +2
The rise of powerful, accessible image-editing models such as FLUX.2 has brought high-fidelity editing within broad reach. Their capabilities now extend beyond localized modificati…
Obliviate: Erasing Concepts from Autoregressive Image Generation Models
Hossein Shakibania, Jonas Henry Grebe, Tobias Braun +4
The widespread adoption of generative AI models has intensified concerns about misuse, including the creation of unsafe or disturbing imagery. To mitigate such issues, several conc…
Token by Token, Compromised: Backdoor Vulnerabilities in Unified Autoregressive Models
Tobias Braun, Jonas Henry Grebe, Hossein Shakibania +2
Unified autoregressive models (UAMs) are transformer models that generate text as well as image tokens within a single autoregressive pass. Shared parameters and a multimodal vocab…
No Trust Issues Here: A Technical Report on the Winning Solutions for the Rayan AI Contest
Ali Nafisi, Sina Asghari, Mohammad Saeed Arvenaghi +1
This report presents solutions to three machine learning challenges developed as part of the Rayan AI Contest: compositional image retrieval, zero-shot anomaly detection, and backd…