Perceptual Hashing Using Pretrained Vision Transformers

被引:0
|
作者
De Geest, Jelle [1 ]
De Smet, Patrick [2 ]
Bonetto, Lucio [2 ]
Lambert, Peter [1 ]
Van Wallendael, Glenn [1 ]
Mareen, Hannes [1 ]
机构
[1] Univ Ghent, Imec, Dept Elect & Informat Syst, Technol Pk Zwijnaarde 122, B-9052 Ghent, Belgium
[2] Natl Inst Criminalist & Criminol NICC, Vilvoordsesteenweg 100, B-1120 Brussels, Belgium
来源
2024 IEEE GAMING, ENTERTAINMENT, AND MEDIA CONFERENCE, GEM 2024 | 2024年
关键词
Perceptual Hashing; Vision Transformer; Image Forensics;
D O I
10.1109/GEM61861.2024.10585453
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The rapid evolution of digital image circulation has necessitated robust techniques for image identification and comparison, particularly for sensitive applications such as detecting Child Sexual Abuse Material (CSAM) and preventing the spread of harmful content online. Traditional perceptual hashing methods, while useful, fall short when exposed to some common image transformations, or when images are doctored to avoid detection, rendering them ineffective for nuanced comparisons. Addressing this challenge, this paper introduces a novel pretrained vision transformer artificial intelligence (AI) model approach that enhances the robustness and accuracy of perceptual hashing. Leveraging a pretrained Vision Transformer (ViT-L/14), our approach integrates visual and textual data processing to generate feature arrays that represent perceptual image hashes. Through a comprehensive evaluation using a dataset of 50,000 images, we demonstrate that our method offers significant improvements in detecting similarities for certain complex image transformations, aligning more closely with human visual perception than conventional methods. While our method presents certain initial drawbacks such as larger hash sizes and high computational complexity, its ability to better handle perceptual nuances presents a forward step in the realm of image forensics. The potential applications of this research extend to law enforcement, digital media management, and the broader domain of content verification, setting the stage for more secure and efficient digital content analysis.
引用
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页码:19 / 24
页数:6
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