VLMs for synthetic medical image detection overweight text metadata, flipping authenticity judgments on the same image and dropping accuracy on authentic images by 61.1% on average when an explicit AI-origin tag is present.
Spot the fake: Large multimodal model-based synthetic image detection with artifact explanation
8 Pith papers cite this work. Polarity classification is still indexing.
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Fake-HR1 is a hybrid-reasoning VLM that adaptively selects reasoning modes for synthetic image detection via two-stage HFT and HGRPO training, improving both accuracy and efficiency over standard LLMs.
Skyra is an MLLM that detects AI-generated videos by identifying and reasoning over grounded visual artifacts, supported by a new annotated dataset and benchmark.
GAPL learns a compact set of canonical forgery prototypes and applies two-stage LoRA training to build a low-variance feature space that improves generalization across GAN and diffusion generators.
Omni-Fake delivers a unified multimodal deepfake benchmark dataset and RL-driven detector that reports gains in accuracy, cross-modal generalization, and explainability over prior baselines.
LOGER ensembles heterogeneous global vision models with selective local patch aggregation via multiple instance learning to achieve robust deepfake detection across varied manipulations and degradations.
FakeVLM-R1 combines GRPO reinforcement learning with critical-thinking CoT and a physics-annotated FakeClue++ dataset to reach claimed SOTA synthetic image detection while reducing over-rejection of real images.
A 3B-parameter vision-language model trained on continuously curated social media data detects AI-generated content with state-of-the-art accuracy on benchmarks and shows positive engagement effects in production deployment.
citing papers explorer
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Text Over Image: Auditing Multimodal Robustness in Synthetic Medical Image Detection
VLMs for synthetic medical image detection overweight text metadata, flipping authenticity judgments on the same image and dropping accuracy on authentic images by 61.1% on average when an explicit AI-origin tag is present.
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Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection
Fake-HR1 is a hybrid-reasoning VLM that adaptively selects reasoning modes for synthetic image detection via two-stage HFT and HGRPO training, improving both accuracy and efficiency over standard LLMs.
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Skyra: AI-Generated Video Detection via Grounded Artifact Reasoning
Skyra is an MLLM that detects AI-generated videos by identifying and reasoning over grounded visual artifacts, supported by a new annotated dataset and benchmark.
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Scaling Up AI-Generated Image Detection with Generator-Aware Prototypes
GAPL learns a compact set of canonical forgery prototypes and applies two-stage LoRA training to build a low-variance feature space that improves generalization across GAN and diffusion generators.
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Omni-Fake: Benchmarking Unified Multimodal Social Media Deepfake Detection
Omni-Fake delivers a unified multimodal deepfake benchmark dataset and RL-driven detector that reports gains in accuracy, cross-modal generalization, and explainability over prior baselines.
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LOGER: Local--Global Ensemble for Robust Deepfake Detection in the Wild
LOGER ensembles heterogeneous global vision models with selective local patch aggregation via multiple instance learning to achieve robust deepfake detection across varied manipulations and degradations.
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FakeVLM-R1: Internalizing Physical Laws via CoT for Synthetic Image Detection
FakeVLM-R1 combines GRPO reinforcement learning with critical-thinking CoT and a physics-annotated FakeClue++ dataset to reach claimed SOTA synthetic image detection while reducing over-rejection of real images.
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Detecting AI-Generated Content on Social Media with Multi-modal Language Models
A 3B-parameter vision-language model trained on continuously curated social media data detects AI-generated content with state-of-the-art accuracy on benchmarks and shows positive engagement effects in production deployment.