Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms

Image filtering systems have become essential in large-scale content platforms to prevent the dissemination of unauthorized data. While extensive research has focused on identifying images based on categories or visual similarity, the filtering problem addressed in this study presents distinct challenges. Specifically, it involves a predefined set of filtering images and requires real-time detection of whether a distributed image is derived from an unauthorized source. Although three major approaches—bitmap-based, image processing-based, and deep learning-based techniques—have been explored, no comprehensive comparison has been conducted. To bridge this gap, we formalize the concept of image equivalence and introduce performance metrics tailored for fair evaluation. Through extensive experiments, we derive the following key findings. First, bitmap-based methods are practically viable in real-world scenarios, offering reasonable detection rates and fast search speeds even under resource constraints. Second, despite their success in tasks such as image classification, deep learning-based methods underperform in our problem domain, highlighting the need for customized models and architectures. Third, image processing-based techniques demonstrate superior performance across all key metrics, including execution time and detection rates. These findings provide valuable insights into designing efficient image filtering systems for diverse content platforms, particularly for detecting unauthorized images and their transformations effectively.