Design of a Duplicate Image Detection Method Based on Multiple Features

The widespread distribution of illegal pornographic material has become a significant societal concern due to the ease of sharing digital content over the internet. Existing detection methods often rely on manual review or keyword filtering, which are time-consuming and ineffective against evolving content. This paper proposes a novel real-time method for detecting duplicate images. The proposed method combines Convolutional Neural Network (CNN) based feature extraction with image hashing techniques for efficient image comparison. In this paper, we describe the dataset composition and procedures for training, and finally, we perform a performance evaluation.