Tusk

Tusk is an advanced AI-powered image validation platform designed to analyze, detect, and verify the authenticity and safety of images in real time. Leveraging cutting-edge machine learning models, Tusk can identify fake, manipulated, or inappropriate content with high accuracy. It ensures that only genuine and safe visuals are used across digital platforms.

Tusk’s intelligent validation engine scans images for indicators of AI generation, deepfakes, explicit content, and authenticity inconsistencies, providing users with clear insights and confidence in the media they encounter.

With its simple yet robust interface, users can upload an image and instantly receive a detailed validation report powered by AI. The system is designed for creators, developers, and organizations that prioritize content integrity and trust in their digital ecosystem.

✨ Key Features

🔍 AI Authenticity Check: Detects AI-generated or manipulated images (deepfakes, synthetic visuals, etc.)

🧠 Smart Classification: Classifies images into safe, questionable, or unsafe categories

🧩 Real-Time Analysis: Delivers instant feedback using AI models deployed on the backend

🛡️ Content Moderation: Flags images containing NSFW or harmful content

⚙️ Developer-Friendly API: Easily integrate Tusk’s validation engine into other apps or workflows

Tusk was built using a modern full-stack AI architecture combining Next.js, TailwindCSS, and Python-based machine learning models to deliver real-time image validation and content safety analysis.

On the frontend, Tusk uses Next.js (App Router) for a smooth, responsive user experience. The UI is crafted with TailwindCSS and subtle motion effects to make the validation process intuitive and engaging. Users can easily upload images via a clean drag-and-drop interface that instantly previews their selection.

Once an image is uploaded, it’s sent to the backend API, where the core AI validation happens. The backend integrates a custom-trained model (using TensorFlow and OpenCV) that analyzes multiple factors — including pixel artifacts, texture anomalies, and embedding inconsistencies — to determine if an image is AI-generated, fake, or explicit.

The model outputs confidence scores for authenticity and safety, which are formatted and returned to the frontend through a RESTful API built with Next.js API routes (or Express.js during local testing).

For efficient image handling, uploads are processed using Cloudinary, which optimizes and securely stores the images before analysis. This allowed us to manage large image files seamlessly and focus on AI inference rather than storage.

We also implemented a smart caching mechanism to reduce redundant re-analysis of the same image hashes, improving overall performance and API response time.

During development, a few hacky but clever tweaks were used — like augmenting image metadata before AI evaluation and fine-tuning the deepfake detection model using a mix of publicly available datasets and synthetic examples generated by open-source diffusion models. This significantly improved model robustness on real-world data.

🧩 Tech Stack

Frontend: Next.js (App Router), TypeScript, TailwindCSS

Backend: Next.js API Routes / Express.js

AI/ML: Python, TensorFlow, OpenCV, Pillow

Storage & Optimization: Cloudinary

Utilities: Git, Vercel, Node.js