Deep Visual Similarity for Content Moderation: Detecting Plagiarized Images at Scale

The proliferation of visual content on social media platforms has intensified plagiarism detection and copyright protection challenges. This article presents a deep learning-based content moderation system to identify near-duplicate and manipulated images at scale. The system integrates a fine-tuned ResNet-50 architecture with hierarchical navigable small-world graphs to enable efficient similarity searches across massive image repositories. By extracting high-dimensional feature embeddings and implementing multi-stage filtering approaches, the technology can detect visual similarities despite common evasion techniques, including cropping, scaling, rotation, and color adjustments. Training with triplet loss functions and augmented datasets significantly enhances the robustness against transformation attempts. Production implementation on a major social platform accurately identifies duplicate content while substantially reducing manual moderation requirements. Beyond operational efficiencies, deployment results reveal meaningful improvements in content originality, reduced copyright violations, and enhanced creator satisfaction. The architecture balances computational resources through hybrid indexing strategies prioritizing recently uploaded content. This comprehensive solution addresses critical challenges in maintaining content integrity at scale while offering insights into effective implementation strategies for automated visual similarity detection in large-scale content ecosystems.