# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 1 Introduction  
Instagram users upload over 95 million photos and videos every day, with an average of 65,000 images posted per minute [1]. Pinterest hosts over 240 billion pins across 5 billion boards, providing a vast repository of user-generated and shared images [2]. These massive volumes of digital content are easily copied, modified, and shared through high-speed internet and mobile networks, leading to a rapid increase in unauthorized content, from copyright violations to the non-consensual distribution of intimate recordings captured through covert cameras [3, 4].  
To mitigate the spread of such unlawful digital content, social media platforms require filtering mechanisms at various stages involving media creators, service providers, andconsumers. Automated filtering techniques leveraging AI are widely adopted to identify and block illegal content, including pornography [5,6]. However, the filtering systems discussed in this study specifically target predefined unauthorized or unlawful data, such as content violating personal portrait rights or non-consensual imagery. Failure to effectively filter such content can lead to its rapid spread online, causing significant harm to individuals [7,8]. Although traditional research in digital rights management (DRM) has tackled similar issues [9], the growing sophistication of data manipulation techniques to evade filtering presents new challenges.  
Figure 1 illustrates the structure of the filtering system discussed in this paper. Service providers manage a database of known unauthorized data, periodically updated by regulatory authorities. When media data are uploaded by creators or downloaded by consumers, the system verifies whether the content is unauthorized and blocks it if necessary. The effectiveness of a filtering system depends on how accurately and quickly it can identify illegal data while avoiding excessive false positives. High false positive rates require manual intervention by administrators, increasing operational costs and excessively restricting users from uploading legitimate images.  
Due to the inherent nature of digital data, it is trivial to modify original content, such as changing file formats or altering image properties. Consequently, filtering systems must not only detect exact matches but also identify derivative data generated through transformations. This study focuses on filtering techniques for images, aiming to provide a quantitative analysis of methods for detecting modified or derivative images based on the original. Video data, being a temporal sequence of multiple images, could be extended using the techniques discussed in this paper.  
Traditionally, fingerprint techniques, a specific method within ICD (Image Copy Detection), have been used to uniquely identify digital data, generating unique identifiers akin to human fingerprints [10]. Recently, these techniques have expanded into the concept of image DNA, which extracts features in more diverse ways. This study adopts the term \"image DNA\", which ensures that transformed images produce similar DNA values while distinct images yield dissimilar DNA. Thus, the extent to which DNA similarity reflects image transformation becomes the key metric for evaluating the effectiveness of image DNA techniques.