Benchmarking and Improving Perceptual Straightening for Robust AI-Generated Video Detection

Xenofon Papougiannakis, Rubén Tous

The proliferation of text-to-video generative models—including commercial systems such as Sora, Veo, Runway Gen-3, and Kling—makes automated detection of AI-generated videos an urgent problem in multimedia forensics. We present a study with two interleaved contributions: a cross-family benchmark and two improvements to the detection pipeline. For the benchmark, we evaluate three methodologically distinct detector families on three public datasets (VidProM, DeepAction, and DeCoF_T2V), spanning diverse open-source and commercial generators: ReStraV, a geometry-supervised detector grounded in the perceptual straightening hypothesis; D3, a training-free detector based on second-order XCLIP temporal statistics; and DeMamba, a supervised Bidirectional Mamba module on frozen XCLIP features. ReStraV and DeMamba achieve broadly comparable global performance on large datasets (VidProM AUC 0.985/0.989; DeCoF_T2V AUC 0.990/0.992), while D3 remains weak as a stand-alone detector under a fixed detection threshold calibrated on VidProM and transferred unchanged to the other benchmarks (AUC 0.534/0.410/0.454 on VidProM/DeepAction/DeCoF_T2V). Per-generator analysis reveals complementary failure modes: DeMamba recovers several hard commercial generators where ReStraV struggles, whereas ReStraV remains competitive in low-data settings at substantially lower computational cost. To bridge these weaknesses, we propose two improvements. First, Rich384 enriches ReStraV’s compact geometric descriptor with DINOv2 temporal embeddings, strengthening ranking quality on large benchmarks and recovering generators that geometry alone misses (AUC 0.995 on VidProM and 0.998 on DeCoF_T2V, with DeepAction AUC decreasing to 0.805). Second, we propose GurAI, our transparent logistic late-fusion method that combines Rich384 and DeMamba logits, raising threshold-free AUC to 0.998/0.999 on VidProM and DeCoF_T2V while preserving interpretability. On the small DeepAction cohort, fusion improves selected per-generator fake recalls at the cost of elevated REAL false positives (AUC 0.769; REAL accuracy 0.460).Our analysis suggests that transparent late fusion can exploit complementary detector strengths more effectively than architectural redesign alone when facing generator diversity.