Drop your image here
PNG, JPG, or WEBP · Max 10 MB
or drag and drop
Upload any image and our AI analyzes visual patterns, metadata, and forensic signals to estimate whether it was AI-generated or authentic.
Drop your image here
PNG, JPG, or WEBP · Max 10 MB
or drag and drop
Three simple steps to check any image in seconds.
PNG, JPG, or WEBP. Your image is processed instantly in memory and never stored on our servers.
We run multiple deep learning models and forensic checks on visual textures, metadata, and pixel patterns.
Receive a clear authenticity score with simple explanations of why the result was reached.
ImgAuth AI is an academic AI and cybersecurity project developed to detect AI-generated or manipulated images using deep learning and forensic analysis techniques.
The platform combines multiple state-of-the-art AI models and forensic signals to estimate whether an image may be synthetic or authentic — making cutting-edge detection accessible to everyone.
Built as a college major project focused on AI safety, digital trust, and media authenticity in an age of rapidly advancing generative AI.
What makes ImgAuth AI powerful under the hood.
Three independent deep learning models vote on whether an image is AI-generated, increasing accuracy and reducing bias.
Noise kurtosis, FFT spectral analysis, and deep feature inconsistency mapping expose subtle AI generation artifacts.
Heatmaps and attention maps highlight exactly which regions of the image influenced the detection result.
All analysis happens in-memory. Images are never written to disk, stored in databases, or shared with third parties.
Fast pipeline design minimizes model load time. Results are typically delivered within 10–30 seconds.
A weighted ensemble scoring engine combines all signals into a single, calibrated authenticity confidence score.
Modern tools and frameworks powering the platform.
Four students building at the intersection of AI, cybersecurity, and design.
Designed overall architecture, coordinated module integration, defined the scoring strategy, and supervised technical direction and core detector logic.
Implemented FastAPI routes, file validation, request handling, backend orchestration, and API-result communication between all system layers.
Managed QA workflows, testing strategies, bug tracking, documentation, and security review of file handling and user interactions.