# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 1 Introduction
### Problem Definition  
Filtering systems for image services must distinguish between an image derived from an original and a visually similar but non-derivative image, as incorrectly filtering out unrelated yet similar-looking images can lead to frequent false positives. To address this, we introduce the concept of _image equivalence_, which distinguishes transformation-derived images from those that are merely similar in appearance.  
Figure 1: Unlawful media filtering by a media service provider.  
**Definition 1**.: _Two images, \(A_{1}\) and \(A_{2}\), are equivalent if and only if \(A_{2}\) can be generated by applying a transformation to \(A_{1}\)._  
Equivalent images encompass not only exact copies of the original but also images that have undergone various transformations, such as format conversion, caption addition, resizing, color adjustment, facial mosaicking, and other modifications. Most Image Copy Detection (ICD) techniques focus on identifying equivalent images with high precision and recall.  
In contrast, images that may appear visually similar but are not transformation-derived are classified as similar images rather than equivalent images. To formalize this distinction, we define image DNA as a unique descriptor, ensuring that equivalent images exhibit similar DNA values, whereas visually similar but non-equivalent images possess distinct DNA characteristics.  
**Definition 2**.: _The image DNA of an image must satisfy the following two properties:_  
1. _If images_ \(A\) _and_ \(B\) _are equivalent images, then their DNA representations, DNA(_\(A\)_) and DNA(_\(B\)_), should have a small distance under a predefined similarity metric dist, i.e.,_ \[\text{dist(DNA(A), DNA(B))}\leq\tau_{1}\] (1) _where_ \(\tau_{1}\) _is a threshold ensuring that small variations from transformations do not disrupt equivalence._
2. _If images_ \(A\) _and_ \(B\) _are not equivalent images, then their DNA representations should have a large distance under the same metric, i.e.,_ \[\text{dist(DNA(A), DNA(B))}>\tau_{2}\] (2) _where_ \(\tau_{2}\) _is a threshold ensuring that visually similar but non-equivalent images remain distinguishable._  
A robust image DNA technique, in this context, is one that consistently upholds these properties, ensuring clear differentiation between equivalent and non-equivalent images. The core challenge addressed in this study is the real-time verification of uploaded images in large-scale social media or content service platforms. This process requires computing the image DNA upon upload and instantly comparing it against a database of stored images to determine equivalence.  
The process of filtering unauthorized images involves the steps illustrated in Figure 2. First, DNA is extracted from the filtering target images and stored in a searchable DNA database. This database can either be prepopulated with unauthorized images or updated incrementally as needed. For filtering, the DNA of distributed images is extracted, compared against the database, and analyzed for similarity to detect unauthorized content.  
For image DNA techniques to be practical in real-world scenarios, several requirements must be satisfied. First, the overhead for DNA generation and se  
Figure 2: Unlawful image filtering using image DNA.  
acceptable limits. Search overhead is particularly critical as the database grows, potentially impacting real-time upload and download performance. Second, the false positive rate must be kept low to avoid mistakenly restricting legitimate uploads and downloads, which would impose additional manual verification costs on operators. Third, the system must achieve high detection rates for equivalent images to effectively block the spread of unauthorized content.