VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection
Deepfake image detection is currently served by three fundamentally different paradigms: commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors. Despite their widespread use, these paradigms are rarely evaluated under a common protocol, making direct comparison difficult. We introduce VendorBench-100, a cross-paradigm benchmark that evaluates 36 representative models using a single adversarial 100-image corpus, a unified output schema, and a common evaluation framework. To ensure reliable assessment under the corpus's intentional class imbalance, models are ranked
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- PossiblePossibly related (embedding) · 57%darkdevil3610/100-AI-Machine-learning-Deep-learning-Computer-vision-NLP →
- PossiblePossibly related (embedding) · 51%Somnusochi/VLM-AutoYOLO →
- PossiblePossibly related (embedding) · 50%AdilShamim8/100-AI-Machine-Learning-Deep-Learnin-Projects →
- PossiblePossibly related (embedding) · 50%NVIDIA-ISAAC-ROS/isaac_ros_object_detection →
- PossiblePossibly related (embedding) · 47%Atomic-man007/Awesome_Multimodel_LLM →
- FuzzySimilar title/name (fuzzy) · 59%Tongyi-MAI/Z-Image-Turbo →
“Fuzzy title match (0.73): “VendorBench-100: A Unified Cross-Paradigm Benchmark for Deep” ≈ “Tongyi-MAI/Z-Image-Turbo””
- LinkedLinked via arxiv author · 85%Sharayu N. Deshmukh →
“VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection”
- LinkedLinked via arxiv author · 85%Md Rashidunnabi →
“VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection”