DCT-domain Deep Convolutional Neural Networks for Multiple JPEG Compression Classification

TL;DR

This paper presents a DCT-domain deep CNN approach for accurately classifying images based on the number of JPEG compressions, enhancing detection of multiple re-compression cycles in digital images.

Contribution

It introduces a novel DCT-domain CNN method with a specialized pre-processing step for content-independent JPEG compression classification.

Findings

Outperforms existing methods in multiple JPEG compression detection

Capable of classifying more re-compression cycles

Optimized for accuracy and efficiency

Abstract

With the rapid advancements in digital imaging systems and networking, low-cost hand-held image capture devices equipped with network connectivity are becoming ubiquitous. This ease of digital image capture and sharing is also accompanied by widespread usage of user-friendly image editing software. Thus, we are in an era where digital images can be very easily used for the massive spread of false information and their integrity need to be seriously questioned. Application of multiple lossy compressions on images is an essential part of any image editing pipeline involving lossy compressed images. This paper aims to address the problem of classifying images based on the number of JPEG compressions they have undergone, by utilizing deep convolutional neural networks in DCT domain. The proposed system incorporates a well designed pre-processing step before feeding the image data to CNN to capture essential characteristics of compression artifacts and make the system image content independent. Detailed experiments are performed to optimize different aspects of the system, such as depth of CNN, number of DCT frequencies, and execution time. Results on the standard UCID dataset demonstrate that the proposed system outperforms existing systems for multiple JPEG compression detection and is capable of classifying more number of re-compression cycles then existing systems.