Modern T2V/I2V generators synthesize people increasingly hard to distinguish from authentic footage, while current evaluation suites lag: legacy benchmarks target manipulation-based forgeries, and recent synthetic-video benchmarks prioritize scale over realistic human depiction. We introduce SynthForensics, a people-centric benchmark of 20,445 videos from 8 T2V and 7 I2V open-source generators, paired-source from FF++/DFD reals, two-stage human-validated, in four compression versions with full metadata. In our paired-comparison human study, raters prefer SynthForensics in 71--77% of head-to-head comparisons against each of nine existing synthetic-video benchmarks, while facial-quality metrics fall within the FF++/DFD baseline range. Across 15 detectors and three protocols, face-based methods drop 13--55 AUC points (mean 27) from FF++ to SynthForensics and a further 23 under aggressive compression; fine-tuning closes the gap at a backward cost on legacy benchmarks; training from scratch shows synthetic and manipulation features largely disjoint for most detectors. We release dataset, pipeline, and code.
@article{arxiv.2602.04939,
title = {SynthForensics: Benchmarking and Evaluating People-Centric Synthetic Video Deepfakes},
author = {Roberto Leotta and Salvatore Alfio Sambataro and Claudio Vittorio Ragaglia and Mirko Casu and Yuri Petralia and Francesco Guarnera and Luca Guarnera and Sebastiano Battiato},
journal= {arXiv preprint arXiv:2602.04939},
year = {2026}
}