Papers

Field

Robust adversarial detection with theoretical guarantees3 papers raise it · 3 address it
Current detectors rely on heuristics and fail under adaptive attacks, leaving models vulnerable to adversarial inputs.
Emergent misalignment in multimodal models1 paper raises it · 1 addresses it
Fine-tuning on narrow tasks can induce broadly harmful behaviors in vision-language models, but its mechanisms and mitigation are poorly understood.
- Narrow Multimodal Fine-Tuning Can Induce Emergent Misalignmentraises · addresses
Safe and high-quality generation trade-off1 paper raises it · 1 addresses it
Restricting training data for safety reduces model quality, while post-hoc unlearning risks catastrophic forgetting.
- Pristine Modelsraises · addresses
Robustness of decision models under adversarial manipulation1 paper raises it · 1 addresses it
Decision models are increasingly used for automated tasks, but their reliability under attacks is untested and unstandardized.
- DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Visionraises · addresses
Watermark removal resilience1 paper raises it · 2 address it
Invisible watermarks for ownership verification are vulnerable to simple, training-free attacks that preserve image quality.
- BrushOff: Stroke-Based Re-rendering as a Watermark Removal Attack on Paintingraises · addresses
- SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verificationaddresses

Named by an AI from these papers' abstracts.

arXiv cs.CVSep 18
Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Models
Yining Wang, Xi Li, Mi Zhang, Xiaohan Zhang, Xiaoyu You +2
Multimodal large reasoning models (MLRMs) have demonstrated remarkable capabilities in complex visual understanding. However, this very power introduces a critical yet underexplored privacy threat: adversaries can exploit MLRMs to precisely infer users' geographic locations from casually shared photographs, by performing structured reasoning over subtle visual cues such as architectural styles, vegetation, and lighting conditions. In this work, we present a systematic study of MLRM-driven geolocation privacy leakage. We first reveal that refusal-based safeguards are critically insufficient, as carefully crafted jailbreak prompts can raise model response rates to 100%. We further identify that existing defenses, which inject imperceptible perturbations into shared images, suffer from structural limitations intrinsic to their pixel-space optimization, resulting in degraded black-box transferability and pronounced visual artifacts. Motivated by these findings, we propose a diffusion-based framework that provides targeted, proactive defense against geolocation privacy leakage. By injecting perturbations into the latent space of a diffusion model during reverse sampling, our method…
PDF arXiv

arXiv cs.CVSep 18
Hiding in Plain Sight: A Diffusion-based Mitigation of Geolocation Privacy Leakage in Vision-Language Models
Yining Wang, Xi Li, Mi Zhang, Xiaohan Zhang, Xiaoyu You +2
Multimodal large reasoning models (MLRMs) have demonstrated remarkable capabilities in complex visual understanding. However, this very power introduces a critical yet underexplored privacy threat: adversaries can exploit MLRMs to precisely infer users' geographic locations from casually shared photographs, by performing structured reasoning over subtle visual cues such as architectural styles, vegetation, and lighting conditions. In this work, we present a systematic study of MLRM-driven geolocation privacy leakage. We first reveal that refusal-based safeguards are critically insufficient, as carefully crafted jailbreak prompts can raise model response rates to 100%. We further identify that existing defenses, which inject imperceptible perturbations into shared images, suffer from structural limitations intrinsic to their pixel-space optimization, resulting in degraded black-box transferability and pronounced visual artifacts. Motivated by these findings, we propose a diffusion-based framework that provides targeted, proactive defense against geolocation privacy leakage. By injecting perturbations into the latent space of a diffusion model during reverse sampling, our method…
PDF arXiv

Robust adversarial detection with theoretical guarantees3 papers raise it · 3 address it
Current detectors rely on heuristics and fail under adaptive attacks, leaving models vulnerable to adversarial inputs.
Emergent misalignment in multimodal models1 paper raises it · 1 addresses it
Fine-tuning on narrow tasks can induce broadly harmful behaviors in vision-language models, but its mechanisms and mitigation are poorly understood.
- Narrow Multimodal Fine-Tuning Can Induce Emergent Misalignmentraises · addresses
Safe and high-quality generation trade-off1 paper raises it · 1 addresses it
Restricting training data for safety reduces model quality, while post-hoc unlearning risks catastrophic forgetting.
- Pristine Modelsraises · addresses
Robustness of decision models under adversarial manipulation1 paper raises it · 1 addresses it
Decision models are increasingly used for automated tasks, but their reliability under attacks is untested and unstandardized.
- DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Visionraises · addresses
Watermark removal resilience1 paper raises it · 2 address it
Invisible watermarks for ownership verification are vulnerable to simple, training-free attacks that preserve image quality.
- BrushOff: Stroke-Based Re-rendering as a Watermark Removal Attack on Paintingraises · addresses
- SkillMarker: A KGW-Based Watermarking Method for Agent Skill Ownership Verificationaddresses

Named by an AI from these papers' abstracts.

Fields · AI · updated 1h ago

Found by the topic model: adversarial training, deepfake detection, watermarking schemes.

43papers this week−21.8% vs last week

270 papers this month