The paper investigates whether detailed articulated human pose provides more discriminative power than coarse spatial relationships for early violence detection. By fixing the downstream pipeline and comparing five interaction representations—including bounding‑box geometry, handcrafted pose analogues, enriched pose descriptors, and a learned joint encoder—the study finds that pose‑based representations do not outperform coarse geometry. When visual encoders are frozen and evaluated on larger datasets, person‑crop appearance and whole‑frame context outperform geometry, but cropping to interacting people offers no advantage over encoding the entire frame. The authors further demonstrate that pre‑onset frames contain source‑related artifacts (e.g., title cards, watermarks) that contribute significantly to discrimination, suggesting that benchmark performance may reflect these artifacts rather than true event evidence.
arXiv:2608.11985v3 Announce Type: replace Abstract: Frame-level area under the ROC curve (AUC) is the dominant evaluation metric for weakly supervised video anomaly detection (WSVAD). Its standard fo...
The paper introduces Motion‑Aware Deepfake (MAD) videos, a new class of deepfakes generated by pose‑guided diffusion models that can animate entire human figures. To study this threat, the authors build the first MAD‑specific benchmark with 1.5 million frames from 1,363 real and 30,122 synthetic videos, and analyze the subtle high‑frequency artifacts and spectral fingerprints that arise from limited input frames. Leveraging these insights, they propose MoDA, a defense framework that combines spatial semantics with steganalysis‑rich frequency features, achieving high detection and attribution accuracy across in‑distribution, cross‑dataset, and zero‑shot scenarios, and demonstrating robustness against adaptive attacks.
The paper investigates how weakly supervised video anomaly detectors, trained with only video‑level labels, are evaluated using frame‑level metrics such as Micro‑AUROC and AP. It shows that these metrics largely measure a detector’s ability to separate different videos rather than correctly ordering anomalous moments within a single video, a phenomenon termed temporal dilution. Experiments demonstrate that a detector can achieve high pooled scores even when it assigns the same score to every frame in a video, indicating that current evaluation practices may overstate temporal localization performance.
This paper presents an interpretable deepfake detection framework that explicitly encodes physically grounded forensic cues to analyze spatially and temporally coherent facial features in video sequences. The method transforms videos into identity-consistent facial trajectories, segments them into fixed-length temporal windows, and represents each frame with 68 structured descriptors across photometric, textural, geometric, and compression domains. These descriptors are processed by an LSTM to capture temporal dependencies, achieving strong F1-scores on four benchmark datasets and demonstrating robust cross-dataset generalization.
arXiv:2609.40014v1 Announce Type: new Abstract: Detecting violence after it begins is important from recognizing behavioral cues that appear immediately beforehand. This work studies short-horizon pr...
arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...
arXiv:2608. 19987v1 Announce Type: new Abstract: Skeleton-based Video Anomaly Detection (VAD) offers a robust, privacy-preserving solution for identifying abnormal behaviors.
arXiv:2610.09830v1 Announce Type: new Abstract: Video deepfakes targeting a specific individual, the Person-of-Interest (POI), are the most harmful ones, and, since a public figure is abundantly reco...
arXiv:2607. 17342v1 Announce Type: cross Abstract: Understanding physical human-robot and human-human interactions is a challenging yet emerging topic in 3D vision.
ManiVid introduces a unified forensic analysis framework for manipulated videos, combining forgery detection, artifact grounding, and anomaly explanation. The authors release ManiVid-38K, a large dataset of 19K real‑fake video pairs with authenticity labels, forgery masks, and explanations, and a benchmark ManiVidBench with 1K balanced pairs. ManiVidLens, the proposed model, outperforms existing methods in artifact grounding and anomaly explanation while matching state‑of‑the‑art detection accuracy.
arXiv:2607. 03558v1 Announce Type: cross Abstract: Continuous video anomaly detection is dominated by reactive Multiple Instance Learning (MIL) that collapses spatiotemporal features into scalar scores.
arXiv:2410. 19553v2 Announce Type: replace-cross Abstract: This paper explores the impact of occlusions in video action detection.