3 citations · 3 across the 16 of their papers we have counts for
19 papers
BabelFake: A Multilingual Audio-Visual DeepFake Benchmark
Carlotta Segna, Joel Tschesche, Anna Rohrbach
Reliable and practical audio-visual DeepFake detection requires benchmarks that reflect diverse linguistic contexts and modern data synthesis pipelines for visual as well as audio…
The Poisoned Conversation: Privacy-Leaking Watermarks in Unified Multimodal Models
Tobias Braun, Jonas Henry Grebe, Emil Sivic +4
Multimodal models are increasingly shifting toward unified architectures that understand and generate text, images, and other modalities within a shared conversational context. Thi…
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…
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…
Multi-axis Analysis of Image Manipulation Localization
Keanu Nichols, Divya Appapogu, Giscard Biamby +3
Advanced image editing software enables easy creation of highly convincing image manipulations, which has been made even more accessible in recent years due to advances in generati…
EMO-BOOST: Emotion-Augmented Audio-Visual Features for Improved Generalization in Deepfake Detection
Aritra Marik, Marcel Klemt, Anna Rohrbach
With every advancement in generative AI models, forensics is under increasing pressure. The constant emergence of new generation techniques makes it impossible to collect data for…
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…
Think Twice, Act Once: Verifier-Guided Action Selection For Embodied Agents
Nishad Singhi, Christian Bialas, Snehal Jauhri +4
Building generalist embodied agents capable of solving complex real-world tasks remains a fundamental challenge in AI. Multimodal Large Language Models (MLLMs) have significantly a…
HaloProbe: Bayesian Detection and Mitigation of Object Hallucinations in Vision-Language Models
Reihaneh Zohrabi, Hosein Hasani, Akshita Gupta +3
Large vision-language models can produce object hallucinations in image descriptions, highlighting the need for effective detection and mitigation strategies. Prior work commonly r…
Tuning Just Enough: Lightweight Backdoor Attacks on Multi-Encoder Diffusion Models
Ziyuan Chen, Yujin Jeong, Tobias Braun +1
As text-to-image diffusion models become increasingly deployed in real-world applications, concerns about backdoor attacks have gained significant attention. Prior work on text-bas…
VeriTaS: The First Dynamic Benchmark for Multimodal Automated Fact-Checking
Mark Rothermel, Marcus Kornmann, Marcus Rohrbach +1
The growing scale of online misinformation urgently demands Automated Fact-Checking (AFC). Existing benchmarks for evaluating AFC systems, however, are largely limited in terms of…
DeepFake Doctor: Diagnosing and Treating Audio-Video Fake Detection
Marcel Klemt, Carlotta Segna, Anna Rohrbach
Generative AI advances rapidly, allowing the creation of very realistic manipulated video and audio. This progress presents a significant security and ethical threat, as malicious…
Spurious-Aware Prototype Refinement for Reliable Out-of-Distribution Detection
Reihaneh Zohrabi, Hosein Hasani, Mahdieh Soleymani Baghshah +3
Out-of-distribution (OOD) detection is crucial for ensuring the reliability and safety of machine learning models in real-world applications, where they frequently face data distri…
Diffusion Classifiers Understand Compositionality, but Conditions Apply
Yujin Jeong, Arnas Uselis, Seong Joon Oh +1
Understanding visual scenes is fundamental to human intelligence. While discriminative models have significantly advanced computer vision, they often struggle with compositional un…
Erased but Not Forgotten: How Backdoors Compromise Concept Erasure
Tobias Braun, Jonas Henry Grebe, Marcus Rohrbach +2
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…
When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning
Nishad Singhi, Hritik Bansal, Arian Hosseini +4
Scaling test-time compute has emerged as a key strategy for enhancing the reasoning capabilities of large language models (LLMs), particularly in tasks like mathematical problem-so…
VDial: Unification of Video and Visual Dialog via Multimodal Experts
Adnen Abdessaied, Anna Rohrbach, Marcus Rohrbach +1
We present VDial - a novel expert-based model specifically geared towards simultaneously handling image and video input data for multimodal conversational tasks. Current multim…
DEFAME: Dynamic Evidence-based FAct-checking with Multimodal Experts
Tobias Braun, Mark Rothermel, Marcus Rohrbach +1
The proliferation of disinformation demands reliable and scalable fact-checking solutions. We present Dynamic Evidence-based FAct-checking with Multimodal Experts (DEFAME), a modul…
Chrono: A Simple Blueprint for Representing Time in MLLMs
Hector Rodriguez, Boris Meinardus, Anil Batra +2
The recent success of Large Language Models (LLMs) has prompted the extension to the multimodal domain, developing image-text Multimodal LLMs (MLLMs) and then video-text models. In…