arXiv (All)
open access
Forensic Twins: Self-Supervised Residual Learning for AI-Generated Image Forensics
Detectors of AI-generated images are typically trained using samples from all Generative AI architectures they must catch, and struggle as soon as a new architecture emerges. Recent approaches have explored self-supervised pre-training as an alternative solution, yet standard frameworks work against
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
- RED: Reconstruction Evolution Dynamics for Generalizable AI-Generated Image Detection#1132023
- Revisiting Cross-Reconstruction for Generalizable Deepfake Detection#1156816
- An Empirical Study into Clustering of Unseen Datasets with Self-Supervised Encoders#971659
- Discovering Transferable Forensic Features for CNN-generated Images Detection#273687
- TIGA: Trajectory-Injected Generative Attack against Black-box AIGC Detectors#924628
Record · ID 1106213
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