3 papers
cs.CR2026
Benchmarking Neural Defend ARCAS 1B: A Foundational Multimodal Deepfake Detection Model
Sivashankar Selvarajan, Piyush Verma, Sumit Kumar +1
AI-generated imagery evolves faster than benchmark-specific detector evaluations, making a single score an incomplete account of generalization. This paper evaluates Neural Defend…
cs.CV2026
VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection
Sharayu N. Deshmukh, Md Rashidunnabi, Nelton Tiago Gemo +3
Deepfake image detection is served by three fundamentally different paradigms - commercial APIs, zero-shot vision-language models (LLMs), and open-source detectors - that are rarel…
cs.CV2026
Are DeepFakes Realistic Enough? Exploring Semantic Mismatch as a Novel Challenge
Sharayu Nilesh Deshmukh, Kailash A. Hambarde, Joana C. Costa +2
Current DeepFake detection scenarios are mostly binary, yet data manipulation can vary across audio, video, or both, whose variability is not captured in binary settings. Four-clas…