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Conference Paper Design of a Duplicate Image Detection Method based on Multiple Features
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Authors
Jin-Hyuk Song, Yongseong Cho
Issue Date
2024-10
Citation
International Conference on Information and Communication Technology Convergence (ICTC) 2024, pp.383-385
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/ICTC62082.2024.10827639
Abstract
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.
KSP Keywords
Convolution neural network(CNN), Detection Method, Digital content, Duplicate image detection, Feature extractioN, Image Hashing, Performance evaluation, Real-time method, image comparison, manual review, multiple features