publications

2026

1. NeurIPS

   

   SynthForensics: Benchmarking and Evaluating People-Centric Synthetic Video Deepfakes

   Roberto Leotta^† Salvatore Alfio Sambataro^†, and 6 more authors

   In Advances in Neural Information Processing Systems (NeurIPS), Evaluations & Datasets Track, 2026

   ⭐ Poster · † Joint first authorship

   Abs arXiv

   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.

2. ACM MM

   

   Adversarial Attacks on Deepfake Detectors: A Challenge in the Era of AI-Generated Media

   Francesco Guarnera, Luca Guarnera, Andrea Montibeller, Salvatore Alfio Sambataro, and 6 more authors

   In Proceedings of the 34th ACM International Conference on Multimedia (MM ’26), 2026

   Abs DOI PDF Website

   The increasing realism of AI-generated media and its widespread dissemination through social media demand deepfake detectors that remain reliable under adversarial attacks and realistic post-processing. We present AADD-2026, a competitive benchmark for evaluating adversarial attacks against deepfake detectors under realistic deployment conditions. The challenge considers synthetic images from five generative families, social-media-inspired processing, JPEG and JPEG-AI compression, and eight heterogeneous detectors operating across spatial, frequency, and foundation-model representations. Ten teams submitted valid solutions, exploring latent-space, frequency-aware, ensemble, manifold-projection, and compression-aware attacks. Results show that explicitly accounting for compression and post-processing substantially improves transferability to unseen detectors. The winning approach achieves the highest attack success through a gradient-free real-face manifold projection strategy. Meanwhile, DINOv2- and SigLIP2-based detectors show substantially greater resilience than conventional architectures, highlighting a clear trade-off between attack transferability and perceptual fidelity.