arXiv:2608.29212v1 Announce Type: cross
Abstract: Existing video watermarking systems are symmetric: the party that can verify a mark holds the extractor weights or generator secret and can therefore...
By Guang Yang, Fengchen Liu
The paper introduces RIFT, a forensic framework for detecting AI-generated videos by exploiting a cross‑scale coupling mismatch between macro‑level temporal dynamics and micro‑level pixel residuals. RIFT comprises a macro stream that models expected temporal evolution, a micro stream that probes residual patterns, and a coupling divergence module that quantifies their conditional dependency. Experiments on VidProM and GenVidBench show near‑perfect F1‑scores and robust performance across different encoders.
By Siyu Li, Jin Yang, Weiheng Liang
arXiv:2608. 03008v1 Announce Type: cross Abstract: As generated videos become increasingly realistic, reliable video forgery detection is increasingly important.
By Shichao Kan, Chengpeng Hong, Jingtong Dou, Chuancheng Shi, Yuhan Liu, Linrui Xu, Yixiong Liang, Yigang Cen, Yanpeng Sun, Fei Shen, Tat-Seng Chua
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.
By Fazhong Liu, Yan Meng, Tian Dong, Guoxing Chen, Haojin Zhu
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.
By Hengrui Kang, Zhonghao Yan, Yuxuan Yang, Ruoyan Jing, Yuncheng Guo, Hao Chen, Kongming Liang, Zhanyu Ma, Conghui He, Weijia Li
The paper investigates whether passive motion traces recorded during selfie capture can serve as an auxiliary signal for detecting spoofing and verifying users in mobile remote identity verification systems. It introduces the CanSelfie dataset, comprising 375 multi‑sensor sequences from 30 participants, and evaluates seven time‑series classifiers and eight anomaly detectors across various sensor configurations. Results show that accelerometer‑only classifiers achieve very low false rejection rates, while certain models achieve low false acceptance rates and high verification accuracy, indicating that selfie‑capture motion is a promising low‑friction evidence channel.
By Erkka Rantahalvari, Olli Silv\'en, Zinelabidine Boulkenafet, Constantino \'Alvarez Casado