An exhaustive measurement of re-sampling detection in lossy compressed images using deep learning approach

Abstract

The detection of resampling in digital images is critical for image authentication, but performance can be challenging when dealing with lossy compression. This study proposes an efficient feature extraction technique for detecting resampling (i.e., tampering) in post-JPEG compressed images. Our approach combines compression clues with resampling clues and feeds them to various traditional machine learning (ML) algorithms such as logistic regression, K-nearest neighbours (K-NN), support vector machine (SVM), decision tree (DT), and random forest (RF) to detect and classify doctored images in the re-compression scenario. We propose and evaluate feed-forward deep neural networks (DNN) and 1D convolutional neural networks (CNN) based on evaluation parameters such as accuracy, recall, precision, and F1 score, comparing them with the aforementioned traditional ML algorithms. Our results show that the RF and one-dimensional (1D) CNN are the most efficient models for this task. Furthermore, the 1D CNN outperforms the state-of-the-art techniques, particularly in the most challenging case of downscaling in lossy JPEG compressed images. Our proposed method demonstrates promising results for resampling detection in post-JPEG compressed images, which can be helpful in various image authentication applications.