# Evaluating Image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms
## 6 Conclusions  
Filtering systems are essential in large-scale content service platforms to prevent the dissemination of unauthorized media data. However, given the ease with which digital content can be manipulated, such systems are rendered ineffective if they cannot detect derivatives of unauthorized content. This study conducted a comprehensive quantitative performance evaluation of various DNA techniques designed to identify manipulated and derivative image data. To this end, four key performance metrics were defined from the perspective of image equivalence: database build time, database search time, true detection rate (TDR), and false detection rate (FDR). The analysis categorized image DNA techniques into three groups--bitmap-based, image processing-based, and deep learning-based--and evaluated them using extensive experiments.  
The results highlight the distinct advantages and limitations of each approach. Bitmap-based techniques, with minimal computational requirements, demonstrated detection rates suitable for practical deployment, along with the fastest extraction and search speeds. Deep learning-based techniques, such as Autoencoder and VGG, excel in identifying similar images and image classification tasks but underperform in detecting image equivalence, with additional drawbacks in execution speed. For scenarios demanding extremely high detection rates and sufficient computational resources, the image processing-based ORB method emerged as the most effective choice. The findings of this study are expected to offer valuable insights for designing efficient and reliable filtering systems for large-scale content platforms.  
This study proposed three classification methods based on image DNA for detecting image transformations. Future research could explore an additional classification approach using image quality metrics such as SSIM (Structural Similarity Index Measure) and PSNR (Peak Signal-to-Noise Ratio), enabling a comparative analysis of the proposed method and quality-based metrics, particularly in low-computational environments. Furthermore, validating our experimental results using widely accepted benchmarks [32; 33] would enhance the reliability of our findings and provide a standardized comparison with existing approaches in the field. Another direction for future research is to explore the feasibility of integrating fuzzy logic into image DNA-based filtering techniques and to compare its performance with the current threshold-based method.  
[PERSON] implemented the architecture and algorithm and performed the experiments. [PERSON] designed the work and provided expertise. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00247727) and in part by RP-Grant 2023 of Ewha Womans University.  
Not applicable.  
The data presented in this study are openly available in github at [[https://github.com/cezanne/nabla-dna](https://github.com/cezanne/nabla-dna)]([https://github.com/cezanne/nabla-dna](https://github.com/cezanne/nabla-dna)) (accessed on 30 December 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DNA & Deoxyribonucleic Acid \\ DRM & Digital Rights Management \\ SURF & Speeded-Up Robust Features \\ ORB & Oriented FAST and Rotated BRIEF \\ CNN & Convolutional Neural Network \\ TDR & True Detection Rate \\ FDR & False Detection Rate \\ VDB & Vector Database \\ MVR & Matched Vector Ratio \\ \end{tabular}