DeeCLIP

DeeCLIP: A Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated Images ☀️ If you find this work useful for your research, please kindly star our repo and cite our paper! ☀️ TODO We are working hard on the following items: Release ArXiv paper Release training scripts...

Agentic-CLIP-Benchmark

Agentic-CLIP-Benchmark: Zero-Shot Evaluation on CIFAR-10 -green.svg This project implements a robust, automated pipeline for evaluating OpenAI's CLIP ViT-B/32 model on the full CIFAR-10 test set 10,000 images. Developed using an AI Native workflow Trae IDE , it achieves a high-precision zero-shot...

DeeCLIP: a Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated Images

This paper introduces DeeCLIP, a novel framework for detecting AI-generated images using CLIP-ViT and fusion learning. Despite significant advancements in generative models capable of creating highly photorealistic images, existing detection methods often struggle to generalize across different...