AI-based image recognition for brand protection: A platform comparative analysis

Recent advances in artificial intelligence have revolutionized brand protection in digital spaces, yet a systematic comparison of platform-specific approaches remains understudied. This article evaluates AI-based image recognition systems across major digital platforms, revealing key differences in their technological architectures and detection capabilities. Our analysis demonstrates that while convolutional neural networks form the backbone of these systems, platform-specific adaptations significantly impact their effectiveness. We found that e-commerce platforms achieve 94% accuracy in counterfeit detection through specialized deep learning models, while social media platforms prioritize real-time processing with lightweight architectures at 87% accuracy. These findings provide crucial insights for organizations implementing brand protection strategies and highlight emerging opportunities for cross-platform standardization.