arXiv (All)
open access
IMFD: End-to-end Multi-Face Forgery Detection through Instruction-based Large Vision-Language Models
The rapid increase of deepfakes has raised significant concerns due to their spread on social media. Traditional multi-face forgery detectors crop and verify each face independently, ignoring background context and inter-face relationships, which often yields suboptimal performance. To overcome thes
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Official Review · NEURIPS 2024#787081
- Deepfake face detection using hybrid bag-of-visual-words and multi-CNN feature fusion.#388883
- AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection#31309
- Swin Meets EfficientNet: Lightweight Architectures for GAN-Based Face Forensics#822178
- Benchmarking the Explanatory Quality of Open-Weight Vision-Language Models in Face Recognition#1036268
Record · ID 1014281
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive