33 citations · 49 across the 42 of their papers we have counts for
14 papers · 1 filter
Concept Removal for Frontier Image Generative Models
Aditya Kumar, Pierre Joly, Adam Dziedzic +1
Image generative models are trained on massive, largely uncurated internet-scale datasets that contain undesirable visual concepts. Efficiently removing such concepts from the mode…
MultiMem: Measuring and Mitigating Memorization in Multi-Modal Contrastive Learning
Wenhao Wang, Franziska Boenisch, Michael Backes +1
Memorization in machine learning models enables high performance on rare in-distribution samples by capturing their atypical patterns. However, it also causes harmful retention of…
Data Provenance for Image Auto-Regressive Generation
Bihe Zhao, Louis Kerner, Michel Meintz +3
Image autoregressive models (IARs) have recently demonstrated remarkable capabilities in visual content generation, achieving photorealistic quality and rapid synthesis through the…
SERUM: Simple, Efficient, Robust, and Unifying Marking for Diffusion-based Image Generation
Jan Kociszewski, Hubert Jastrzębski, Tymoteusz Stępkowski +4
We propose SERUM: an intriguingly simple yet highly effective method for marking images generated by diffusion models (DMs). We only add a unique watermark noise to the initial dif…
Conditioned Activation Transport for T2I Safety Steering
Maciej Chrabąszcz, Aleksander Szymczyk, Jan Dubiński +3
Despite their impressive capabilities, current Text-to-Image (T2I) models remain prone to generating unsafe and toxic content. While activation steering offers a promising inferenc…
Demystifying Foreground-Background Memorization in Diffusion Models
Jimmy Z. Di, Yiwei Lu, Yaoliang Yu +3
Diffusion models (DMs) memorize training images and can reproduce near-duplicates during generation. Current detection methods identify verbatim memorization but fail to capture tw…
Implementing Adaptations for Vision AutoRegressive Model
Kaif Shaikh, Franziska Boenisch, Adam Dziedzic
Vision AutoRegressive model (VAR) was recently introduced as an alternative to Diffusion Models (DMs) in image generation domain. In this work we focus on its adaptations, which ai…
Finding DoRI: Discovery of Retained Images in Diffusion Models
Antoni Kowalczuk, Dominik Hintersdorf, Lukas Struppek +3
Text-to-image diffusion models (DMs) have achieved remarkable success in image generation. However, concerns about data privacy and intellectual property remain due to their potent…
BitMark: Watermarking Bitwise Autoregressive Image Generative Models
Louis Kerner, Michel Meintz, Bihe Zhao +2
State-of-the-art text-to-image models generate photorealistic images at an unprecedented speed. This work focuses on models that operate in a bitwise autoregressive manner over a d…
Precise Parameter Localization for Textual Generation in Diffusion Models
Łukasz Staniszewski, Bartosz Cywiński, Franziska Boenisch +2
Novel diffusion models can synthesize photo-realistic images with integrated high-quality text. Surprisingly, we demonstrate through attention activation patching that only less th…
Captured by Captions: On Memorization and its Mitigation in CLIP Models
Wenhao Wang, Adam Dziedzic, Grace C. Kim +2
Multi-modal models, such as CLIP, have demonstrated strong performance in aligning visual and textual representations, excelling in tasks like image retrieval and zero-shot classif…
Beautiful Images, Toxic Words: Understanding and Addressing Offensive Text in Generated Images
Aditya Kumar, Tom Blanchard, Adam Dziedzic +1
State-of-the-art Diffusion Models (DMs) produce highly realistic images. While prior work has successfully mitigated Not Safe For Work (NSFW) content in the visual domain, we ident…
Privacy Attacks on Image AutoRegressive Models
Antoni Kowalczuk, Jan Dubiński, Franziska Boenisch +1
Image AutoRegressive generation has emerged as a new powerful paradigm with image autoregressive models (IARs) matching state-of-the-art diffusion models (DMs) in image quality (FI…
Benchmarking Robust Self-Supervised Learning Across Diverse Downstream Tasks
Antoni Kowalczuk, Jan Dubiński, Atiyeh Ashari Ghomi +6
Large-scale vision models have become integral in many applications due to their unprecedented performance and versatility across downstream tasks. However, the robustness of these…