arXiv:2607. 03581v1 Announce Type: cross Abstract: The widespread adoption of facial masks, accelerated by COVID-19 and mandated in security-sensitive settings, has exposed limitations of conventional face recognition systems.
By Dana A Abdullah
The paper introduces IMFD, an end‑to‑end multi‑face forgery detector that uses instruction‑based Large Vision‑Language Models (LVLMs). IMFD jointly localizes faces and predicts forgery labels in a single stage, explicitly incorporating predicted face bounding boxes into the textual instruction to improve grounding and detection. Experiments on converted multi‑face forgery datasets show that IMFD outperforms several state‑of‑the‑art methods.
By Dasom Choi, Sangjun Moon, Hyeongchan Im, Jaeeon Park, Jingun Kwon, Hidetaka Kamigaito, Taro Watanabe, Manabu Okumura
arXiv:2609.14437v1 Announce Type: cross
Abstract: Deepfake detection systems often exhibit significant performance degradation when deployed on unseen manipulation methods, limiting their reliability...
By Arya Pulkit, Aditya Ruhela, Akarshan Kapoor, Arnav Bhavsar
The study evaluates whether adding a camera‑noise fingerprint (Noiseprint++) to an RGB Xception backbone improves face‑swap detection on FaceForensics++. A three‑model ablation shows that fusion does not outperform RGB alone and that the residual branch alone performs near chance. Diagnostic analysis reveals that the noise maps contain a statistical signal in their per‑sample moments, but an InstanceNorm layer standardizes these away, rendering the residual redundant rather than complementary to RGB features.
By Danil Davydov, Bader Rasheed, Dmitriy Vatolin
arXiv:2609.12668v1 Announce Type: new
Abstract: Recent deepfake detection studies increasingly suggest remote photoplethysmography (rPPG) signals as an authenticity cue. However, existing benchmarks...
By Chenxi Yang, Yassine Ouzar, Larbi Boubchir
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.
By Chahira Benhama, Mohand Sa\"id Allili, Assia Hamadene