Intelligent Deep Detection Method for Malicious Tampering of Cancer Imagery

Assist Prof Dr. Khattab M. Ali Alheeti   / Article Link

Abstract    In recent years, deep generative networks have reinforced caution requirements while consuming different formats of digital information. One way of deepfake generation is aligned with the insertion and removal of tumors from medical scans. Large setbacks on hospital resources or even loss of life are the consequences of failure to detect medical deepfakes. This paper attempts to evaluate machine learning algorithms and pre-trained deep neural networks' (DNN) ability to distinguish between tampered and authentic data. Moreover, this work aims to classify cancer scans based on DNN. The experimental results show that the proposed method based on using DNN can enhance performance detection. Further, the proposed system increased the detection accuracy rate and reduced the number of false alarms.

Keywords Deepfake, medical image tampering, machine learning, DNN, detection accuracy, false alarms

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