  Using generative adversarial network (GAN)\cite{RN90} for data enhancement of
medical images is significantly helpful for many computer-aided diagnosis (CAD)
tasks. A new attack called CT-GAN has emerged. It can inject or remove lung
cancer lesions to CT scans. Because the tampering region may even account for
less than 1\% of the original image, even state-of-the-art methods are
challenging to detect the traces of such tampering.
  This paper proposes a cascade framework to detect GAN-based medical image
small region forgery like CT-GAN. In the local detection stage, we train the
detector network with small sub-images so that interference information in
authentic regions will not affect the detector. We use depthwise separable
convolution and residual to prevent the detector from over-fitting and enhance
the ability to find forged regions through the attention mechanism. The
detection results of all sub-images in the same image will be combined into a
heatmap. In the global classification stage, using gray level co-occurrence
matrix (GLCM) can better extract features of the heatmap. Because the shape and
size of the tampered area are uncertain, we train PCA and SVM methods for
classification. Our method can classify whether a CT image has been tampered
and locate the tampered position. Sufficient experiments show that our method
can achieve excellent performance.
