From the 1 of 8 linked papers with an AI index.
8 papers
Fighting Fire with Fire: On the Feasibility of Protecting Exercises Against AI Cheating
Tobias Braun, Jonas Grebe, Louis Rethfeld +1
The widespread adoption of generative AI enables students to outsource cognitive effort to increasingly capable assistants, creating an illusion of competence while undermining the…
VETO: Towards Protecting Images From Frontier AI Editing
Jonas Grebe, Hossein Shakibania, Tobias Braun +2
The paper presents VETO, a subtle anti-edit cloak that disrupts how modern diffusion-based image editors read source images, and introduces VetoBench, a benchmark for evaluating pr…
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…
GEM: Geometric Erasure by Contrastive Velocity Matching in Rectified Flows
Jonas Henry Grebe, Tobias Braun, Anna Rohrbach +1
While the rapid adoption of multimodal generative models offers immense potential, it has also increased the risks of harmful content synthesis, deepfakes, and copyright infringeme…
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
Tobias Braun, Jonas Henry Grebe, Marcus Rohrbach +1
The expansion of text-to-image diffusion models has raised concerns about harmful outputs, from fabricated depictions of public figures to sexually explicit imagery. To mitigate su…
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…