Intelligent Deep Detection Method for Malicious Tampering of Cancer Imagery

被引:3
作者
Alheeti, Khattab M. Ali [1 ]
Alzahrani, Abdulkareem [2 ]
Khoshnaw, Najmaddin [3 ]
Al-Dosary, Duaa [4 ]
机构
[1] Univ Anbar, Coll Comp & Informat Technol, Comp Networking Syst Dept, Ramadi, Iraq
[2] Al Baha Univ, Fac Comp Sci & Informat Technol, Comp Engn & Sci Dept, Al Baha, Saudi Arabia
[3] Komar Univ Sci & technol, Hiwa Canc Hosp, Dept heamtol hiwa & MLS Komar, Sulaymaniyah, Iraq
[4] Univ Anbar, Coll Comp Sci & Informat Technol, Ramadi, Iraq
来源
2022 7TH INTERNATIONAL CONFERENCE ON DATA SCIENCE AND MACHINE LEARNING APPLICATIONS (CDMA 2022) | 2022年
关键词
deepfake; medical image tampering; machine learning; DNN; detection accuracy; false alarms; FORGERY DETECTION; MEDICAL IMAGES; WATERMARKING;
D O I
10.1109/CDMA54072.2022.00010
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years, deep generative networks have reinforced the need for caution while consuming different formats of digital information. One method of deepfake generation involves the insertion and removal of tumors from medical scans. Significant drains on hospital resources or even loss of life are the consequences of failure to detect medical deepfakes. This research attempts to evaluate machine learning algorithms and pre-trained deep neural networks' (DNN) ability to distinguish tampered data and authentic data. Moreover, this research aims to classify cancer scans based on DNN. The experimental results show that the proposed method based on using DNN can enhance performance detection. Furthermore, the proposed system increased the detection accuracy rate and reduced the number of false alarms.
引用
收藏
页码:25 / 28
页数:4
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