Khalil Anis Zabat
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AIGC Detection Model

AIGC Detection Model

A sophisticated machine learning system designed to identify AI-generated images with high accuracy, built on Vision Transformer architecture and trained on a large dataset of authentic and synthetic images.

Project Overview

As AI-generated imagery becomes increasingly realistic, the ability to distinguish between human-created and AI-generated content grows more critical. This project addresses this challenge through:

  • Vision Transformer (ViT-16) architecture for visual pattern recognition
  • Extensive training on 50,000+ diverse images
  • Robust evaluation methodology
  • Practical applications for content moderation and verification

Technical Implementation

Model Architecture

  • Vision Transformer: ViT-16 backbone optimized for image classification tasks
  • Transfer Learning: Pre-trained weights fine-tuned for AIGC detection
  • Custom Classification Head: Specialized final layers for binary classification
  • Attention Visualization: Heat maps showing regions of interest in detection

Data Engineering

  • Diverse Dataset: 50,000+ images spanning multiple AI generators and authentic sources
  • Data Augmentation: Random crops, flips, rotations, and color adjustments to improve robustness
  • Balanced Training: Equal representation of AI-generated and authentic images
  • Cross-validation: K-fold validation ensuring reliable performance metrics

Optimization Techniques

  • Hyperparameter Tuning: Grid search to identify optimal learning rate, batch size, and model configuration
  • Mixed Precision Training: Float16/32 training for faster processing
  • Gradient Accumulation: Effective training with limited GPU resources
  • Early Stopping: Prevention of overfitting while maximizing accuracy

Competition Results

The model achieved remarkable results in a competitive evaluation:

  • 3rd Place Ranking: Among numerous competing approaches
  • 92% Average Accuracy: Across diverse test sets
  • Low False Positive Rate: Minimizing incorrect flagging of authentic content
  • Robust to Adversarial Examples: Resistant to common evasion techniques

Applications & Future Development

This technology has applications in:

  • Content moderation for social media platforms
  • Digital forensics and evidence verification
  • Copyright protection systems
  • Academic integrity verification

Future development will focus on adapting to new generation techniques, reducing computational requirements, and extending detection to video content.