Adaptive content suppression based on shared‐hidden‐layer convolutional autoencoder for blind image forensics

Abstract

SummaryDouble JPEG (DJPEG) compression detection is one of the crucial issues in detecting the presence of image forgery or tampering in blind image forensics. To address this challenge, this article investigates DJPEG compression detection in aligned and nonaligned tampering cases using adaptive image content removal based on a shared‐hidden‐layer convolutional autoencoder for preprocessing. Then the suppressed content images are classified with two convolutional neural networks (CNNs) in a parallel structure. These two networks are responsible for separating the small 64 × 64 single JPEG compression images from the DJPEG ones. The results indicate appropriate performance in the aligned and especially nonaligned cases. The detection possibility in the absence of alignment was investigated and favorable results were achieved. Since it is not possible to determine the exact values of the quality factor (QF) for optimal detection in some cases, we examined the network sensitivity to these QFs. The experimental results were satisfactory, especially in the aligned case. Comparing state‐of‐the‐art methods, we achieved a relative error reduction of about 69% in some cases. In addition, we determined the manipulated locations with high accuracy.