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arXiv Computer Vision By Dasom Choi, Sangjun Moon, Hyeongchan Im, Jaeeon Park, Jingun Kwon, Hidetaka Kamigaito, Taro Watanabe, Manabu Okumura

IMFD: End-to-end Multi-Face Forgery Detection through Instruction-based Large Vision-Language Models

Read the original on arXiv Computer Vision →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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