This tool will parse the inputted text into many different existing AI writing detection tools and return the results to you. This is to be able to scan text with as many tools as possible without having to do it manually.
An awesome list of the best AI content detectors.
Syncing OpenAI's AI detector to your email to determine the likelihood that someone is sending you an email authored by ChatGPT
Applying Evolutionary Computing to Diffusion Models
An AI message detector written in python using tensorflow.
intelligent AI System to detect AI-generated content using state-of-the-art Technolgies. Our deep learning technology distinguishes between AI and human-authored media in images and text, we deployed it in a web application to make it easy for anyone
Copyleaks Web Report is an easy-to-use Angular module from Copyleaks for quick plagiarism and AI detection. It offers a simple and adaptable way to show how original submitted texts or files are through clear reports.
pihole blocklist for ai-generated websites. community-fed -> add to it!
An awesome list of the best AI content detectors.
Easily collect and compare results of various AI "detectors" in one place
This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features. For more details follow the documentaion.
This repository contains the code and scripts used in my thesis project, which aims to build various BERT and LSTM models to detect AI generated texts in both English and Arabic languages.
Code and data to evaluate adversarial attacks on neural text detectors
Python tool for simple comparison check on generated code vs suspected generated code.
An awesome list of the best AI detectors for images, videos & audio. Yes, even for deepfakes.
a deep learning project that uses ResNet and Inception architectures to classify real vs AI-generated face images. The project includes two models trained on a custom dataset, achieving validation accuracies of 52.45% (ResNet) and 52.94% (Inception). Built with TensorFlow and Flask, and a web interface for real time face classification
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