DeeCLIP

DeeCLIP：检测 AI 生成图像的鲁棒且可泛化的 Transformer 框架 ☀️ 如果您觉得这项工作对您的研究有帮助，请为我们的仓库点个 Star 并引用我们的论文！☀️ TODO 我们正在努力完成以下事项： 发布 ArXiv 论文 发布训练脚本 发布推理脚本 发布模型检查点 DeeCLIP DeeCLIP 是一个用于检测 AI 生成图像 的新颖框架，结合了 CLIP-ViT 与融合学习 。随着生成模型的不断改进，由于泛化能力差且易受常见图像操作的影响，检测合成图像变得更具挑战性。DeeCLIP 通过鲁棒且适应性强的架构解决了这些问题。 🔍 概述 DeeCLIP...

A Joint Reconstruction-Triplet Loss Autoencoder Approach Towards Unseen Attack Detection in IoV Networks

Internet of Vehicles IoV systems, while offering significant advancements in transportation efficiency and safety, introduce substantial security vulnerabilities due to their highly interconnected nature. These dynamic systems produce massive amounts of data between vehicles, infrastructure, and...

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...