A curated collection of the latest research and resources on AI-Generated Image and Video Detection.

[CVPR 2026] UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection

Code for the LOTA: Bit-Planes Guided AI-Generated Image Detection

「ICML 2026」Official PyTorch Code for "OmniAID: Decoupling Semantics and Artifacts for Universal AI-Generated Image Detection in the Wild".

[CVPR 2026] Towards Generalizable AI-Generated Image Detection via Image-Adaptive Prompt Learning

SpecXNet is a robust AI-generated image and deepfake detection model that fuses spatial and frequency-domain features using Fourier-based attention for improved cross-generator generalization.

[ICCV 2025] D^3QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for Autoregressive-Generated Image Detection

Zero-shot AI-generated image detection using forensic self-descriptions. Trained only on real images, detects deepfakes from any generator. Official implementation. CVPR 2025.

Official repository of the paper: Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics

Micro-Defects expose Macro-Fakes: Detecting AI-Generated Images via Local Distributional Shifts

[CVPR Findings 2026] PRADA: Probability-Ratio-Based Attribution and Detection of Autoregressive-Generated Images

DeForge-AI: A robust framework for AI-generated image detection, combining a DINOv3 Vision Transformer with high-frequency forensic noise analysis.

Multi-agent AI image forensics pipeline that detects AI-generated and manipulated images using parallel vision agents (Gemini + Groq) with a context-aware pre-processing layer and Cerebras-based arbitration to reduce false positives.