计算机科学
人工智能
数字取证
鉴定(生物学)
催交
领域(数学)
机器学习
对象(语法)
模式识别(心理学)
计算机视觉
工程类
计算机安全
植物
数学
系统工程
纯数学
生物
作者
Serkan Karakuş,Mehmet Kaya,Seda Arslan Tuncer
出处
期刊:Traitement Du Signal
[International Information and Engineering Technology Association]
日期:2023-10-30
卷期号:40 (5): 2029-2039
摘要
Rapid advancements in artificial intelligence, machine learning, deep learning, coupled with easy access to high-capacity processing hardware, expansive organized datasets, and the evolution of artificial intelligence algorithms, have extensively influenced numerous fields. Digital Forensics is one such discipline where the application of artificial intelligence has been significantly amplified in recent years. The analysis of extensive image and video files derived from forensic evidence presents challenges in terms of time efficiency and accuracy. To surmount these challenges, artificial intelligence models can be employed to perform identification and classification processes on these data, thus expediting the resolution of forensic cases with enhanced precision. In the current study, state-of-the-art pre-trained YOLOv8 object recognition models - nano, small, medium, large, and extra-large - were utilized. These models were trained on the Wider-Face dataset with the objective of identifying suspects from images and videos sourced from digital materials in the field of digital forensics. The models achieved mean Average Precision (mAP) values of 97.513%, 98.569%, 98.763%, 98.775%, and 99.032% respectively. The YOLOv8 architecture demonstrated superior performance, outperforming the YOLOv5 architecture by a margin of 7.1% to 8.8%. To aid digital forensic experts in the detection and identification of suspicious individuals, a desktop application capable of real-time image analysis was developed.
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