Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. We evaluate different machine learning algorithms and pretrained convolutional neural networks on distinguishing between tampered and untampered data. The findings of this work show near perfect accuracy in detecting instances of tumor injections and removals.

翻译：近年来,深层基因网络在消耗各种数字信息的同时更加需要谨慎。深度假造的一个途径是与注射和从医疗扫描中移除肿瘤相一致的。未能检测出医学深假会导致医院资源严重倒退,甚至生命损失。本文试图通过结构化的案例研究解决此类袭击的检测问题。我们评估了不同的机器学习算法和预先训练的神经网络,以区分被篡改的数据和未经篡改的数据。这项工作的结果显示,在检测肿瘤注射和清除情况时,几乎完全准确。