arXiv (All)
open access
ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images
Generative image models can produce highly realistic images, raising concerns about potential misuse in creating deceptive content. Current deepfake approaches face several challenges, including limited generalizability, lack of interpretability, and poor actionability. To help address these, we pre
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
- Smoothing the Semantic Landscape: Generalizable AI-Generated Image Detection via Text-Induced Flatness#1116218
- Exploring Deepfakes And Effective Prevention Strategies: A Critical Review#715476
- DailyBench: A Unified Benchmark for AI-Generated and Manipulated Images from Modern Generative Models#821962
- REALIS: A Curated Dataset for Studying the Challenges of AI Image Detection#1117065
- Risk, Regulation, and Reward: Navigating AI Adoption in Finance#119521
Record · ID 1063582
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