# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 3 Strategies for Analyzing Image DNA Techniques  
### Performance Metrics for Image DNA  
Evaluating the performance of image DNA techniques requires well-defined metrics from the perspective of the DNA database. These metrics are essential for understanding the strengths and limitations of different techniques and ensuring their practical applicability in real-world scenarios. They can be categorized into two primary aspects: execution time and detection accuracy.  
Execution time metrics are further divided into DNA build time and DNA search time. DNA build time refers to the time required to extract DNA from target images and insert it into the database. DNA search time is the time needed to determine the presence of similar DNA within the database, which includes both DNA extraction and similarity computation. DNA extraction time, common to both the build and search processes, is influenced by the computational complexity of generating image DNA. The size of DNA generated by each technique varies, and larger DNA data typically contain more detailed image features but increase storage costs and negatively impact extraction and search performance.  
Detection accuracy is evaluated through two complementary metrics: the true detection rate (TDR) and the false detection rate (FDR). TDR measures the ability to correctly identify derivative images as equivalent to the original, while FDR quantifies the rate of incorrectly identifying distinct images as equivalent. These rates depend on the similarity threshold used to determine equivalence based on DNA similarity. A tighter threshold leads to a simultaneous decrease in both TDR and FDR.  
Figure 4 illustrates the average detection rates of 17 different image DNA techniques applied to 53 image pairs selected from a Kaggle dataset [27]. In the figure, the blue line represents the \"true detection rate\" (TDR), which indicates the proportion of correctly identified equivalent images as the threshold \(\tau_{1}\) in Definition 2 varies. Conversely, the dashed red line represents the \"false detection rate\" (FDR), which measures the proportion of correctly identified non-equivalent images as the threshold \(\tau_{2}\) varies. Cosine similarity was used as the measure of DNA similarity in this experiment, with the threshold ranging from 0.5 to 1.  
A lower threshold improves TDR, approaching 1, but also increases FDR, highlighting the importance of selecting an appropriate threshold. Even at thresholds close to 1, 9 out of 96 non-equivalent test images were misclassified as equivalent, underscoring the frequent occurrence of false detections. These results indicate the importance of fine-tuning \(\tau_{1}\) and \(\tau_{2}\) to optimize filtering performance. However, optimal thresholds depend heavily on image characteristics, DNA techniques, and distance metrics, requiring extensive environment-specific optimization. As such, fine-tuning falls beyond the scope of this study; we instead conduct a comparative analysis using empirically optimized threshold values to ensure a fair evaluation of each method's effectiveness for the given dataset.